Technical specifications - Core Indicators
PAHO Core Indicators provide the technical specifications for each indicator. These specifications give essential information such as definition, purpose, estimation method, limitations, and data source.
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Adolescent fertility rate (births per 1 000 women aged 15-19 years)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Annual number of births to women aged 15 to 19 per 1 000 women in that age group. Also known as the age-specific fertility rate for women aged 15 to 19.
Measurement Unit
1 000 births per woman
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
Reducing adolescent fertility and addressing the multiple underlying factors are essential to improving adolescent sexual and reproductive health and social and economic wellbeing. In the literature on gender implications, there is substantial agreement that women who become pregnant and give birth very early in their reproductive life are subject to increased risk of complications or even death during pregnancy and childbirth. Their children are also more vulnerable. Therefore, preventing births in adolescent is an important measure to improve maternal health and reduce infant mortality.
In addition, women who have children at an early age experience reduced opportunities for socioeconomic improvement, particularly because young mothers are unlikely to continue studying and, if they need to work, may find it especially difficult to combine family and work responsibilities.
The adolescent fertility rate also provides indirect evidence on access to relevant health services since young people, particularly single adolescent girls, often experience difficulties in accessing sexual and reproductive health services.
In addition, women who have children at an early age experience reduced opportunities for socioeconomic improvement, particularly because young mothers are unlikely to continue studying and, if they need to work, may find it especially difficult to combine family and work responsibilities.
The adolescent fertility rate also provides indirect evidence on access to relevant health services since young people, particularly single adolescent girls, often experience difficulties in accessing sexual and reproductive health services.
Estimation method
The age-specific adolescent fertility rate is calculated as a ratio. The numerator is the number of live births to women 15 to 19 years of age. The denominator is an estimate of exposure to motherhood for women 15 to 19 years of age.
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Estimation method for global and regional aggregates:
World Population Prospects (WPP) age-specific fertility rates are calculated according to population reconstruction at the country level and provide a better estimate based on all available demographic information. WPP potentially considers as many types and sources of empirical estimates as possible (including retrospective birth histories, and direct and indirect fertility estimates). Final estimates are derived to ensure the greatest possible internal consistency with all other demographic components and intercensal cohorts listed in successive censuses.
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Estimation method for global and regional aggregates:
World Population Prospects (WPP) age-specific fertility rates are calculated according to population reconstruction at the country level and provide a better estimate based on all available demographic information. WPP potentially considers as many types and sources of empirical estimates as possible (including retrospective birth histories, and direct and indirect fertility estimates). Final estimates are derived to ensure the greatest possible internal consistency with all other demographic components and intercensal cohorts listed in successive censuses.
Interpretation - example
In 2018, the adolescent fertility rate in country A was 20.7 per 1 000 adolescent women. This means that, during that year, there were an estimated 21 births per 1 000 women 15 to 19 years of age.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths and births in the system, and the integrity of the registry.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG)
Indicator 3.7.2 Adolescent fertility rate (10-14 years; 15 to 19 years) per 1 000 women in that age group.
Available from: https://unstats.un.org/sdgs/metadata/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.b Fertility rate in women 10-19 years of age (disaggregated by 10-14 and 15-19 years) in Latin America and the Caribbean
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Sustainable Development Goals (SDG)
Indicator 3.7.2 Adolescent fertility rate (10-14 years; 15 to 19 years) per 1 000 women in that age group.
Available from: https://unstats.un.org/sdgs/metadata/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.b Fertility rate in women 10-19 years of age (disaggregated by 10-14 and 15-19 years) in Latin America and the Caribbean
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/data/indicators
United Nations, Department of Economic and Social Affairs, Population Division. Handbook on the Collection of Fertility and Mortality Data. For a complete information on the different calculation methods, see United Nations Publication, No. E.03.XVII.11. Available from: https://unstats.un.org/unsd/publication/seriesf/seriesf_92e.pdf
United Nations. Indirect estimation methods are discussed in Manual X: Manual X: Indirect Techniques for Demographic Estimation, United Nations, N°: E.83.XIII.2. Available from:
https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf
https://www.who.int/data/gho/data/indicators
United Nations, Department of Economic and Social Affairs, Population Division. Handbook on the Collection of Fertility and Mortality Data. For a complete information on the different calculation methods, see United Nations Publication, No. E.03.XVII.11. Available from: https://unstats.un.org/unsd/publication/seriesf/seriesf_92e.pdf
United Nations. Indirect estimation methods are discussed in Manual X: Manual X: Indirect Techniques for Demographic Estimation, United Nations, N°: E.83.XIII.2. Available from:
https://www.un.org/en/development/desa/population/publications/pdf/mortality/Manual_X.pdf
Alzheimer disease and other dementias mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from Alzheimer disease, vascular dementia, and unspecified dementia in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The mortality rate from Alzheimer disease and other dementias is applicable to the design, implementation, and evaluation of health policies related to dementia diseases and the distribution of economic, human, and technological resources for this group of diseases, among others. Its applications include to estimate the specialized health personnel required to address these pathologies.
The age-adjusted Alzheimer disease and other dementias mortality rate allows for the comparison of this indicator across populations or in the same population over time.
The age-adjusted Alzheimer disease and other dementias mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from Alzheimer disease, vascular dementia, and unspecified dementia from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as: national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes F01 - F03, G30 - G31 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted Alzheimer disease mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The mortality rate from Alzheimer disease and other dementias is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes F01 - F03, G30 - G31 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted Alzheimer disease mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The mortality rate from Alzheimer disease and other dementias is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted Alzheimer disease and other dementias mortality rate for 2019 was 12 per 100 000 population in country A and 7 per 100 000 population in country B; that is, in 2019 12 people died from Alzheimer disease and other dementias per 100 000 population in country A, compared to country B, where 7 people died from that cause per 100 000 population. This means that, in 2019, the population of country A had a higher risk of dying from Alzheimer disease and other dementias than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted Alzheimer disease and other dementias mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as: the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the Alzheimer disease and other dementias mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted Alzheimer disease and other dementias mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as: the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the Alzheimer disease and other dementias mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from:
https://www.who.int/data/global-health-estimates
https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
- Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
- Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Annual GDP growth (%)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The percentage annual growth rate of gross domestic product (GDP) at market prices based on constant local currency for a given national economy over a specific period, usually one year. It expresses the variation between the GDP values of one period and the next as a proportion of the GDP of the previous period, usually multiplied by 100. Aggregates are expressed in U.S. dollars at constant 2010 prices to facilitate the calculation of countries' growth rates and the aggregation of country data.
GDP at buyer's prices is the sum of the gross value added of all products in the economy, plus any taxes on the products and minus any subsidies not included in the value of the products. GDP is calculated without deductions for depreciation of manufactured assets or for depletion and degradation of natural resources. Value added is the value of producers' gross output minus the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production.
GDP at buyer's prices is the sum of the gross value added of all products in the economy, plus any taxes on the products and minus any subsidies not included in the value of the products. GDP is calculated without deductions for depreciation of manufactured assets or for depletion and degradation of natural resources. Value added is the value of producers' gross output minus the value of intermediate goods and services consumed in production, before accounting for consumption of fixed capital in production.
Measurement Unit
Percentage
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Annual GDP growth is used to assess variations in a country's economic output over a specific period.
It contributes to evaluating changes in a country's productive capacity and eventually, alongside other indicators, its population's standard of living.
It contributes to evaluating changes in a country's productive capacity and eventually, alongside other indicators, its population's standard of living.
Estimation method
The average annual GDP growth rates are those estimated by the World Bank from the corresponding data provided by the United Nations System of National Accounts, expressed in United States dollars at constant 2010 prices.
The United Nations System of National Accounts requires that value added be valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both valuations exclude transport costs that producers invoice separately. Total GDP is measured at buyer's prices. Value added by industry is normally measured at basic production prices. GDP growth rates and their components are calculated using the least squares method and constant price data in the local currency. Constantly priced US dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year.
The growth rate per least squares is estimated by adjusting a linear regression trend line to the annual logarithmic values of the variable in the relevant period. The calculated growth rate is an average rate that represents the observations available throughout the period. The value obtained may differ from the actual growth rate between two periods
The United Nations System of National Accounts requires that value added be valued at basic prices (excluding net taxes on products) or producer prices (including net taxes on products paid by producers, but excluding sales or value-added taxes). Both valuations exclude transport costs that producers invoice separately. Total GDP is measured at buyer's prices. Value added by industry is normally measured at basic production prices. GDP growth rates and their components are calculated using the least squares method and constant price data in the local currency. Constantly priced US dollar series are used to calculate regional and income group growth rates. Local currency series are converted to constant U.S. dollars using an exchange rate in the common reference year.
The growth rate per least squares is estimated by adjusting a linear regression trend line to the annual logarithmic values of the variable in the relevant period. The calculated growth rate is an average rate that represents the observations available throughout the period. The value obtained may differ from the actual growth rate between two periods
Interpretation - example
According to 2019 data, the annual percentage growth of country A's GDP was 2.0%. This means that the economic productivity of this country grew by 2.0% between 2018 and 2019.
Desagregation
No disaggregation
Limitations
Because this indicator is based on the GDP percentage change, it is affected by its limitations, including that its value is insufficient to assess variations in the true standard of living of its inhabitants.
An increase in the percentage annual growth rate of a country's GDP between two periods may be due to a greater concentration of wealth in the higher economic strata. This may mask situations of extreme poverty or deterioration of the wellbeing and social development of its inhabitants.
The frequency with which countries' National Accounts are updated—sometimes a matter of years—is one of the methodological aspects that limit the estimates of this indicator. Another challenge is estimating the added value of the industry, since this requires detailed information on the price structure of inputs and products. This information is not always available.
An increase in the percentage annual growth rate of a country's GDP between two periods may be due to a greater concentration of wealth in the higher economic strata. This may mask situations of extreme poverty or deterioration of the wellbeing and social development of its inhabitants.
The frequency with which countries' National Accounts are updated—sometimes a matter of years—is one of the methodological aspects that limit the estimates of this indicator. Another challenge is estimating the added value of the industry, since this requires detailed information on the price structure of inputs and products. This information is not always available.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from:
https://data.worldbank.org/indicator
https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 8.1.1 Annual growth rate of real GDP per capita.
Available from: https://sdgs.un.org/goals
Indicator 8.1.1 Annual growth rate of real GDP per capita.
Available from: https://sdgs.un.org/goals
References
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Annual parasite incidence (1 000 pop)
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The number of new confirmed cases of malaria recorded in an epidemiological year in a given country, territory, or geographical area. Expressed per 1 000 people under surveillance. The annual parasite index (API) indicates high, moderate, low, and very low-risk transmission environments.
Measurement Unit
1 000 people under surveillance
Type of measurement
Rate
Type of statistics
Crude
Purpose
Malaria is an often-deadly parasitic disease, borne by female Anopheles mosquitoes. Despite the associated risk, especially for children, the disease is preventable and curable. Prevention is based on anti-vector measures, such as insecticide-treated nets and intra-household residual spraying, and prophylactic treatment, only in case of travel to an endemic area, because it can stop the infection in its blood stage, preventing the disease. The second pillar of malaria control is early diagnosis and treatment to reduce incidence, prevent deaths, and contribute to reducing transmission.
API highlights clinical malaria and provides less information about the potential number of asymptomatic infections. It is useful as a surveillance technique to monitor increasing or decreasing trends in malaria burden.
This indicator is used to monitor countries progressing towards targets established in the Global Technical Strategy for Malaria 2016–2030. It also contributes to monitoring national progress on the objectives of the Monitoring Framework for Universal Health in the Americas.
The indicator makes it possible to assess the temporal and geographical trends of malaria and identify at-risk populations in need of strengthened surveillance programs, disease elimination activities, and access to interventions, especially integrated and quality diagnosis and treatment. The value of the indicator is considered when allocating economic, human, and technological resources.
API highlights clinical malaria and provides less information about the potential number of asymptomatic infections. It is useful as a surveillance technique to monitor increasing or decreasing trends in malaria burden.
This indicator is used to monitor countries progressing towards targets established in the Global Technical Strategy for Malaria 2016–2030. It also contributes to monitoring national progress on the objectives of the Monitoring Framework for Universal Health in the Americas.
The indicator makes it possible to assess the temporal and geographical trends of malaria and identify at-risk populations in need of strengthened surveillance programs, disease elimination activities, and access to interventions, especially integrated and quality diagnosis and treatment. The value of the indicator is considered when allocating economic, human, and technological resources.
Estimation method
API is reported by each country's surveillance system. This information is reported annually to the World Health Organization in the World Malaria Report.
Formula:
(A/B) x 1 000
Numerator (A):
Confirmed cases for 1 year in a given country, territory, or geographic area.
Denominator (B):
Population under surveillance in a given country, territory, or geographic area.
Formula:
(A/B) x 1 000
Numerator (A):
Confirmed cases for 1 year in a given country, territory, or geographic area.
Denominator (B):
Population under surveillance in a given country, territory, or geographic area.
Interpretation - example
In 2019, the annual parasite index of malaria in country A was 10.0 per 1,000 people; that is, during that year one new case was confirmed for every 10 people in the population under surveillance.
Desagregation
No disaggregation
Limitations
The value of this indicator is impacted by the effectiveness of surveillance systems, which in turn may be affected by low diagnostic suspicion and underreporting of cases, the availability of laboratory tests for diagnosis, and whether private health centers report identified cases. Another factor influencing its value are areas with poorly defined at-risk populations.
API is mainly calculated from confirmed and reported symptomatic cases of parasitological malaria. As a result, API may underestimate infection incidence in highly endemic areas, and be especially inaccurate in less endemic areas.
API is mainly calculated from confirmed and reported symptomatic cases of parasitological malaria. As a result, API may underestimate infection incidence in highly endemic areas, and be especially inaccurate in less endemic areas.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO). Malaria surveillance, monitoring & evaluation: a reference manual. Washington, D.C., 2018. Available from: https://iris.paho.org/bitstream/handle/10665.2/50648/9789275320563_spa.pdf?ua=1
Pan American Health Organization (PAHO). Regional Program on Malaria. Available from: https://www3.paho.org/hq/index.php?option=com_content&view=article&id=1979:2010-regional-program-on-malaria&Itemid=2153&lang=en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). The Seventy-fourth World Health Assembly. Recommitting to accelerate progress towards malaria elimination. Draft Resolution A74/B/CONF./2. May 2021. Avaialbe from: https://apps.who.int/gb/ebwha/pdf_files/WHA74/A74_BCONF2-en.pdf
World Health Organization (WHO). WHO malaria terminology. Geneva, 2022. Updated in November 2021. Available from: https://www.who.int/publications/i/item/9789240038400
Pan American Health Organization (PAHO). Regional Program on Malaria. Available from: https://www3.paho.org/hq/index.php?option=com_content&view=article&id=1979:2010-regional-program-on-malaria&Itemid=2153&lang=en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). The Seventy-fourth World Health Assembly. Recommitting to accelerate progress towards malaria elimination. Draft Resolution A74/B/CONF./2. May 2021. Avaialbe from: https://apps.who.int/gb/ebwha/pdf_files/WHA74/A74_BCONF2-en.pdf
World Health Organization (WHO). WHO malaria terminology. Geneva, 2022. Updated in November 2021. Available from: https://www.who.int/publications/i/item/9789240038400
Annual population growth rate (%)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Average annual rate of change in the size of the population residing in a given country, territory, or geographical area, during a specific period. Expresses the ratio between the annual increase in population size and the average population for the same period.
The annual increase in population size is defined as the sum of differences: births minus deaths and immigrants minus emigrants, in a given country, territory, or geographical area in a given year.
Average population: assuming the reference period is year z and that births are evenly distributed throughout that period, the average population will be represented by a mid-year estimate, that is, on 1 July.
The annual increase in population size is defined as the sum of differences: births minus deaths and immigrants minus emigrants, in a given country, territory, or geographical area in a given year.
Average population: assuming the reference period is year z and that births are evenly distributed throughout that period, the average population will be represented by a mid-year estimate, that is, on 1 July.
Measurement Unit
Percentage
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
This indicator reflects the growth rate of a country or geographical area and is influenced by variations in birth, mortality, and migration.
It is used to analyze the temporal and geographical variations of a population's growth.
Results can be directly applied to plan and assess health and other specific public policies, such as territorial planning and resource allocation.
It is used to analyze the temporal and geographical variations of a population's growth.
Results can be directly applied to plan and assess health and other specific public policies, such as territorial planning and resource allocation.
Estimation method
The annual population growth rate is generally based either on the growth rate between censuses, calculated from two censuses adjusted for census omission, or on the components of population growth, i.e. births, deaths, immigration and emigration, adjusted for underreporting if necessary, during a specific period.
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
In 2018, the annual population growth rate of country A was 1.2%. This means that, during that year, this country's population increased by 1.2 people per 100 inhabitants.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths and births in the system, and the integrity of the registry.
Since population estimates are used to calculate the annual population growth rate, its outcome depends on the method used to develop population estimates, and projections and may differ from the results obtained at the country level.
The use of the rate in population projections for years significantly separated from the last demographic census means that recent changes in demographic dynamics may not be reflected. This tends to be higher in small populations.
Since population estimates are used to calculate the annual population growth rate, its outcome depends on the method used to develop population estimates, and projections and may differ from the results obtained at the country level.
The use of the rate in population projections for years significantly separated from the last demographic census means that recent changes in demographic dynamics may not be reflected. This tends to be higher in small populations.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Divions. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Divions. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Antenatal care coverage -at least 4 visits (%)
Domain
Health service coverage
Subdomain
Maternal and reproductive health
Definition
The number of women with a live birth that received four or more prenatal health checkups during pregnancy, in relation to the total number of live births, expressed as a percentage, in a given country, territory, or geographical area, in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Prenatal care makes it possible to prevent and identify complications in the mother and child. As such, it is closely related to the reduction of fetal, maternal, and neonatal morbidity and mortality. Prenatal check-ups also increase the chances of access to birth care by trained staff and allow interventions related to health promotion and parenting skills development, along with social and psychological support for the expectant mother.
This indicator makes it possible to identify populations that need increased coverage, availability, and access to maternal and child health services, and raises awareness of inequities in health. It can be applied during planning, management, and evaluation of health policies and maternal and child health services.
This indicator makes it possible to identify populations that need increased coverage, availability, and access to maternal and child health services, and raises awareness of inequities in health. It can be applied during planning, management, and evaluation of health policies and maternal and child health services.
Estimation method
Numerator: Number of women with a live birth who have received four or more prenatal checkups during a specific period. Data includes all sectors, such as private, public and social.
Denominator: Total number women with a live birth during the same period.
The data comes from countries' routine information systems.
Denominator: Total number women with a live birth during the same period.
The data comes from countries' routine information systems.
Interpretation - example
In 2019, prenatal care coverage in country A was 85%. This means that 85 out of every 100 women who had a live birth during that year received four or more prenatal checkups.
Desagregation
No disaggregation
Limitations
This indicator does not allow for evaluation of the quality of the health care received, nor differentiation among professionals providing the care (doctors, nurses, midwives) or the type of care received during prenatal checkups. Availability or accessibility to this type of health service also cannot be evaluated.
The outcome of this indicator is limited by the country's ability to record in a timely manner all prenatal checkups and live births that occurred during a given period. The definition of live birth may differ between countries.
The outcome of this indicator is limited by the country's ability to record in a timely manner all prenatal checkups and live births that occurred during a given period. The definition of live birth may differ between countries.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
WHO. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf?sequence=1&isAllowed=y
World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. Geneva, 2016. Available from: https://www.who.int/publications/i/item/9789241549912
UNICEF. Antenatal care. Available from: https://data.unicef.org/topic/maternal-health/antenatal-care/
https://opendata.paho.org/en/core-indicators
WHO. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf?sequence=1&isAllowed=y
World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. Geneva, 2016. Available from: https://www.who.int/publications/i/item/9789241549912
UNICEF. Antenatal care. Available from: https://data.unicef.org/topic/maternal-health/antenatal-care/
Births (thousands)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Average births in a specific year for a certain country, territory or geographical area, usually estimated at mid-year.
Measurement Unit
Thousands
Type of measurement
Magnitude
Type of statistics
Corrected/Predicted
Purpose
Facilitates quantification of births that occur in a given country or geographic area during a specific period. It is used as a denominator to estimate key demographic indicators, such as birth and fertility rates.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
In country A there were 2 286 300 births in 2018.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on the adequacy of civil registry coverage (greater than 90 percent), timely registration of births in the system, the integrity of the national registry, and how newborns who die during childbirth or within 24 hours of birth are registered.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Other influencing factors are differences in how life events are defined, geographical coverage, and tabulation procedures applied in each country.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Other influencing factors are differences in how life events are defined, geographical coverage, and tabulation procedures applied in each country.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
United States Census Bureau. International Programs, Glossary. Available from:
https://www.census.gov/programs-surveys/international-programs/about/glossary.html
United Nations, Department of Economic and Social Affairs, Statistics Division. Demographic and Social Statistics. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
https://www.census.gov/programs-surveys/international-programs/about/glossary.html
United Nations, Department of Economic and Social Affairs, Statistics Division. Demographic and Social Statistics. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
Births attended at health facilities (%)
Domain
Health service coverage
Subdomain
Maternal and reproductive health
Definition
The number of births attended at health facilities in relation to total births, in a specific country, territory, or geographic area in a given year. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude/Corrected
Purpose
Births attended at health facilities are closely related to care by trained personnel in a health care settings, which contributes to reducing maternal and neonatal morbidity and mortality, since it makes it possible to prevent, identify, and treat complications, such as hemorrhage or sepsis, when they occur.
This indicator makes it possible to identify those populations in which health facility coverage, availability, and accessibility for childbirth care needs to be improved and maternal and child health programs need to be strengthened. It helps to highlight health inequities and to estimate the need for health facilities and their distribution for maternal and childcare.
In countries where the indicator of births attended by trained personnel is not actively reported, the proportion of births attended at health facilities is used as a proxy indicator. This is common when a high proportion of births are attended by health professionals.
This indicator makes it possible to identify those populations in which health facility coverage, availability, and accessibility for childbirth care needs to be improved and maternal and child health programs need to be strengthened. It helps to highlight health inequities and to estimate the need for health facilities and their distribution for maternal and childcare.
In countries where the indicator of births attended by trained personnel is not actively reported, the proportion of births attended at health facilities is used as a proxy indicator. This is common when a high proportion of births are attended by health professionals.
Estimation method
Numerator: Number of births at health facilities in a given year. The data cover care in all sectors, namely the private and public sectors.
Denominator: Total number of births in the country, territory, or geographic area in the same period.
Health facilities are defined as sites that provide health care and have the resources to provide safe maternal care. They include hospitals, clinics, outpatient care centers, and specialized care centers such as birthing centers.
The data are from countries’ routine information systems or surveys.
Denominator: Total number of births in the country, territory, or geographic area in the same period.
Health facilities are defined as sites that provide health care and have the resources to provide safe maternal care. They include hospitals, clinics, outpatient care centers, and specialized care centers such as birthing centers.
The data are from countries’ routine information systems or surveys.
Interpretation - example
In 2020, 93% of births in country A were attended at health facilities; that is, 9 out of 10 births were attended at a health facility.
Desagregation
No disaggregation
Limitations
The percentage of births attended at health facilities does not make it possible to evaluate the availability of this service or access to it, nor does it assess the quality of care received or whether they have the supplies, medical equipment, trained personnel, or adequate management for attending childbirth and its complications.
This indicator may underestimate the percentage of births attended by trained personnel, particularly in the case of home births attended by trained personnel.
This indicator’s result is influenced by the country’s capacity to register in a timely manner all births and live births that occurred in a given period of time.
This indicator may underestimate the percentage of births attended by trained personnel, particularly in the case of home births attended by trained personnel.
This indicator’s result is influenced by the country’s capacity to register in a timely manner all births and live births that occurred in a given period of time.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 3.1.2: Proportion of births attended by skilled health personnel [Proporción de partos atendidos por personal sanitario especializado].
Available from: https://sdgs.un.org/goals/goal3
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.c Proportion of births attended at health facilities
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.1.2: Proportion of births attended by skilled health personnel [Proporción de partos atendidos por personal sanitario especializado].
Available from: https://sdgs.un.org/goals/goal3
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.c Proportion of births attended at health facilities
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
WHO. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf?sequence=1&isAllowed=y
UNICEF. Delivery care. Available from: https://data.unicef.org/topic/maternal-health/delivery-care/
WHO. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf?sequence=1&isAllowed=y
UNICEF. Delivery care. Available from: https://data.unicef.org/topic/maternal-health/delivery-care/
Births attended by skilled health personnel (%)
Domain
Health service coverage
Subdomain
Maternal and reproductive health
Definition
The number of births attended by trained staff in relation to the total number of births, expressed as a percentage, in a given country, territory, or geographical area, in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
The number of births attended by trained staff is closely related to reduced maternal and neonatal morbidity and mortality, as it allows complications such as hemorrhages or sepsis to be identified and prevented, and mothers to be referred to emergency care if necessary.
This indicator makes it possible to identify populations that need improved coverage, availability, and access to skilled childbirth care to strengthen maternal and child health programs. Health inequities are exposed and specialized training needs for health professionals in maternal and child issues can be estimated.
This indicator makes it possible to identify populations that need improved coverage, availability, and access to skilled childbirth care to strengthen maternal and child health programs. Health inequities are exposed and specialized training needs for health professionals in maternal and child issues can be estimated.
Estimation method
Numerator: Number of births attended by trained staff in a given year. Data includes all sectors, such as private, public and social.
Denominator: Total number of births during the same period. Trained staff include obstetricians, doctors and nurses trained in pregnant woman care, university midwives, and midwives who have graduated with diplomas; this does not include trained or untrained traditional birth attendants.
The data comes from countries' routine information systems.
Denominator: Total number of births during the same period. Trained staff include obstetricians, doctors and nurses trained in pregnant woman care, university midwives, and midwives who have graduated with diplomas; this does not include trained or untrained traditional birth attendants.
The data comes from countries' routine information systems.
Interpretation - example
In 2020, 90% of births in country A were attended by trained staff, meaning 9 out of 10 births were attended by a doctor, nurse, or midwife.
Desagregation
No disaggregation
Limitations
The percentage of deliveries attended by trained staff does not allow evaluation of availability or access to this service, nor the quality of care received.
The outcome of this indicator is influenced by the country's ability to record in a timely manner all births and live births that occurred during a given period.
The outcome of this indicator is influenced by the country's ability to record in a timely manner all births and live births that occurred during a given period.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG)
Indicator 3.1.2: Indicator 3.1.2: Proportion of births attended by skilled health personnel.
Available from: https://sdgs.un.org/goals/goal3
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.d Proportion of births attended by skilled health personnel
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Sustainable Development Goals (SDG)
Indicator 3.1.2: Indicator 3.1.2: Proportion of births attended by skilled health personnel.
Available from: https://sdgs.un.org/goals/goal3
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.d Proportion of births attended by skilled health personnel
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
WHO. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf?sequence=1&isAllowed=y
UNICEF. Delivery care. Available from: https://data.unicef.org/topic/maternal-health/delivery-care/
https://opendata.paho.org/en/core-indicators
WHO. 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf?sequence=1&isAllowed=y
UNICEF. Delivery care. Available from: https://data.unicef.org/topic/maternal-health/delivery-care/
Breast cancer incidence rate (age-adjusted per 100 000 pop); female
Domain
Morbidity
Subdomain
Cancer
Definition
The ratio of new cases of breast cancer (ICD-10 code: C50) arising among women of a given country, territory, or geographical area during a specific period (usually one year), to the total number of women in the same population and year. This indicator is age-standardized to eliminate the effect of different age structures.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Predicted
Purpose
Alongside mortality and prevalence estimates, breast cancer incidence rates provide a comprehensive assessment of the impact of the disease in 185 countries or territories, including the Region of the Americas.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of the disease in the population.
The age-adjusted breast cancer incidence rate provides an estimate of the average risk women have of developing this type of cancer in a given country, territory, or geographic area. Its results are used to planning and allocating economic, human, and technological resources to fight breast cancer.
As the breast cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. Inequities in health and at-risk groups can be identified.
This indicator supports public policy-making and is essential for planning and evaluating breast cancer screening, early diagnosis, treatment, and control. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of the disease in the population.
The age-adjusted breast cancer incidence rate provides an estimate of the average risk women have of developing this type of cancer in a given country, territory, or geographic area. Its results are used to planning and allocating economic, human, and technological resources to fight breast cancer.
As the breast cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. Inequities in health and at-risk groups can be identified.
This indicator supports public policy-making and is essential for planning and evaluating breast cancer screening, early diagnosis, treatment, and control. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
Estimation method
Data for this indicator come from national or subnational population cancer registries that are routinely maintained in the Global Cancer Observatory (GCO) 2020: Cancer Incidence in Five Continents (CI5) database.
The methods used to estimate the breast cancer incidence rate in a specific country depend on available data sources. These include projections of observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The breast cancer incidence rate is age-standardized and results are based on rates in populations with a standard age structure.
Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
The methods used to estimate the breast cancer incidence rate in a specific country depend on available data sources. These include projections of observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The breast cancer incidence rate is age-standardized and results are based on rates in populations with a standard age structure.
Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
Interpretation - example
In 2019, the breast cancer incidence rate in country A was 38.2 per 100 000 pop. In country B it was 70.0 per 100 000 pop. In other words, the probability of developing breast cancer in country A in 2019 was 38.2 per 100 000 pop. The risk of developing this type of cancer in country B is twice as high as in country A.
Desagregation
No disaggregation
Limitations
The data presented at the Global Cancer Observatory are the best available for each country. However, the indicator should be interpreted with caution considering current limitations in the quality and coverage of cancer data, particularly in low- and middle-income countries.
In addition, the age-adjusted breast cancer incidence rate is not a real value. Its main purpose is to allow comparisons over time between different populations or within the same population. This indicator reflects the average risk women have of developing breast cancer in a particular country. It should not be interpreted as the individual risk of developing the cancer.
The estimated value of the age-adjusted breast cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
The calculation method is based on new cases of breast cancer in women. It should be noted that men can also acquire a cancer of this type; however, the number of men with breast cancer is minimal compared to women, and there is no effect on the final estimated value of the indicator.
In addition, the age-adjusted breast cancer incidence rate is not a real value. Its main purpose is to allow comparisons over time between different populations or within the same population. This indicator reflects the average risk women have of developing breast cancer in a particular country. It should not be interpreted as the individual risk of developing the cancer.
The estimated value of the age-adjusted breast cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
The calculation method is based on new cases of breast cancer in women. It should be noted that men can also acquire a cancer of this type; however, the number of men with breast cancer is minimal compared to women, and there is no effect on the final estimated value of the indicator.
Data source(s)
Global Cancer Observatory (GCO). Cancer Incidence in Five Continents (CI5). Available from:
https://ci5.iarc.fr/CI5plus/Default.aspx
https://ci5.iarc.fr/CI5plus/Default.aspx
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
International Agency for Research on Cancer (IARC), CANCER TODAY. Available from: https://gco.iarc.fr/today/home
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from: https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/handle/10665/259951
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from: https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/handle/10665/259951
Breast cancer mortality rate (age-adjusted per 100 000 pop); female
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from breast cancer, in the population of women of a certain age group, in a given country, territory or geographic area during a specific year, divided by total women in that population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Removing the effect of a different age structure by using a standard population makes it possible to analyze the breast cancer mortality rate across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of breast cancer and the distribution of economic, human, and technological resources, among others. Its applications include, for example, evaluating over time the effect of screening strategies on reducing mortality from this type of cancer.
It helps to identify groups of women at higher risk of dying from breast cancer and to assess the presence of specific risk factors for certain population groups and the opportunity for access to timely detection, diagnosis and treatment.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of breast cancer and the distribution of economic, human, and technological resources, among others. Its applications include, for example, evaluating over time the effect of screening strategies on reducing mortality from this type of cancer.
It helps to identify groups of women at higher risk of dying from breast cancer and to assess the presence of specific risk factors for certain population groups and the opportunity for access to timely detection, diagnosis and treatment.
Estimation method
The numerator of this indicator uses breast cancer deaths from the World Health Organization (WHO) Global Health Estimates (GHE) and the civil registry and national vital statistics systems of the countries of the Region of the Americas.
The underlying cause of death corresponds to code C50 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the breast cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The breast cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The underlying cause of death corresponds to code C50 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the breast cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The breast cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The age-adjusted breast cancer mortality rate for 2019 is 15.1 per 100 000 population in country A and 10.5 per 100 000 population in country B; that is, in that year this malignant neoplasm was responsible for the death of 15 women per 100 000 in country A, compared to country B, where 11 women per 100 000 died from that cause. This means that, after removing the effect of the differences in the age distribution of the population between the two countries, women in country A have a higher risk of dying from breast cancer than women in country B.
Desagregation
No disaggregation
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
Due to the application of advanced statistical models, estimates may differ from each country’s results. The estimated value of the age-adjusted breast cancer mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences its result is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
The calculation method is based on breast cancer deaths in women.
Estimating the breast cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Due to the application of advanced statistical models, estimates may differ from each country’s results. The estimated value of the age-adjusted breast cancer mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences its result is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
The calculation method is based on breast cancer deaths in women.
Estimating the breast cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Cervical cancer incidence rate (age-adjusted per 100 000 pop); female
Domain
Morbidity
Subdomain
Cancer
Definition
The ratio of new cases of cervical cancer (ICD-10 code: C53) among women of a given country, territory, or geographical area during a specific period (usually one year), to the total number of women in the same population and year. This indicator is age-standardized to control for the effect of different age structures.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Predicted
Purpose
Alongside mortality and prevalence estimates, cervical cancer incidence rates provide a comprehensive assessment of the impact of the disease in 185 countries or territories, including the Region of the Americas.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of the disease in the population.
The age-adjusted cervical cancer incidence rate provides an estimate of the average risk women have of developing this type of cancer in a given country, territory, or geographic area. Results of the indicator are used to planning and allocating economic, human, and technological resources to fight cervical cancer. It can be applied, for example, to evaluate over time of the effect of implementing immunoprevention strategies against this cancer.
As the cervical cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. It helps to identify groups of at-risk women, determine the presence of specific risk factors for particular groups, and assess timely access to diagnosis and treatment.
This indicator supports public policy-making and is essential for planning and evaluating cervical cancer screening, early diagnosis, treatment, and control. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of the disease in the population.
The age-adjusted cervical cancer incidence rate provides an estimate of the average risk women have of developing this type of cancer in a given country, territory, or geographic area. Results of the indicator are used to planning and allocating economic, human, and technological resources to fight cervical cancer. It can be applied, for example, to evaluate over time of the effect of implementing immunoprevention strategies against this cancer.
As the cervical cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. It helps to identify groups of at-risk women, determine the presence of specific risk factors for particular groups, and assess timely access to diagnosis and treatment.
This indicator supports public policy-making and is essential for planning and evaluating cervical cancer screening, early diagnosis, treatment, and control. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
Estimation method
Data for this indicator come from national or subnational population cancer registries that are routinely maintained in the Global Cancer Observatory (GCO) 2020: Cancer Incidence in Five Continents (CI5) database.
The methods used to estimate the cervical cancer incidence rate in a specific country depend on available data sources. These include projections of observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The cervical cancer incidence rate is age-standardized and results are based on rates in populations with a standard age structure.
The age-standardized rate is a summary measure of the rate that would have been observed in a population with a standard age structure. Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
The methods used to estimate the cervical cancer incidence rate in a specific country depend on available data sources. These include projections of observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The cervical cancer incidence rate is age-standardized and results are based on rates in populations with a standard age structure.
The age-standardized rate is a summary measure of the rate that would have been observed in a population with a standard age structure. Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
Interpretation - example
In 2019, the cervical cancer incidence rate in country A was 44.8 per 100 000 pop. In country B, it was 88.9 per 100 000 pop. In other words, the probability of developing cervical cancer in country A in 2019 was 44.8 per 100 000 pop. The risk of developing this type of cancer in country B is twice as high as in country A.
Desagregation
No disaggregation
Limitations
The data presented at the Global Cancer Observatory are the best available for each country. However, the indicator should be interpreted with caution considering current limitations in the quality and coverage of cancer data, particularly in low- and middle-income countries.
In addition, the age-adjusted cervical cancer incidence rate is not a real value. Its main purpose is to allow comparisons between different populations or within the same population over time therefore, its interpretation must be done with caution. This indicator reflects the average risk women have of developing cervical cancer in a particular country. It should not be interpreted as the individual risk of developing the cancer.
The estimated value of the age-adjusted cervical cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
In addition, the age-adjusted cervical cancer incidence rate is not a real value. Its main purpose is to allow comparisons between different populations or within the same population over time therefore, its interpretation must be done with caution. This indicator reflects the average risk women have of developing cervical cancer in a particular country. It should not be interpreted as the individual risk of developing the cancer.
The estimated value of the age-adjusted cervical cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
Data source(s)
Global Cancer Observatory (GCO). Cancer Incidence in Five Continents (CI5). Available from:
https://ci5.iarc.fr/CI5plus/Default.aspx
https://ci5.iarc.fr/CI5plus/Default.aspx
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
International Agency for Research on Cancer (IARC), CANCER TODAY. Available from: https://gco.iarc.fr/today/home
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from: https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators. Available from: https://opendata.paho.org/en/core-indicators/core-indicators-dashboard
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/handle/10665/259951
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from: https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators. Available from: https://opendata.paho.org/en/core-indicators/core-indicators-dashboard
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/handle/10665/259951
Cervical cancer mortality rate (age-adjusted per 100 000 pop); female
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from cervical cancer, in the population of women in a given country or geographic area during a specific year, divided by total women in that population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify groups of women at higher risk of dying from cervical cancer and to assess the presence of specific risk factors for certain population groups and the opportunity for access to timely detection, diagnosis and treatment.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, screening, diagnosis, treatment, and control of cervical cancer and the distribution of economic, human, and technological resources, among others. Its applications include, for example, evaluating over time the effect of implementing strategies for immunoprevention, screening, and early diagnosis of this type of cancer.
Removing the effect of a different age structure by using a standard population makes it possible to analyze the cervical cancer mortality rate across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, screening, diagnosis, treatment, and control of cervical cancer and the distribution of economic, human, and technological resources, among others. Its applications include, for example, evaluating over time the effect of implementing strategies for immunoprevention, screening, and early diagnosis of this type of cancer.
Removing the effect of a different age structure by using a standard population makes it possible to analyze the cervical cancer mortality rate across populations or in the same population over time.
Estimation method
The numerator of this indicator uses cervical cancer deaths from the World Health Organization (WHO) Global Health Estimates (GHE) and the civil registry and national vital statistics systems of the countries of the Region of the Americas.
The underlying cause of death corresponds to code C53 of the International Classification of Diseases, Tenth Revision (ICD-10). Deaths assigned to ICD code C55, cancer of the uterus, part unspecified, and distributed pro-rata to cervix uteri cancer (C53) and corpus uteri cancer (C54).
The populations used in the denominator of the cervical cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The cervical cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The underlying cause of death corresponds to code C53 of the International Classification of Diseases, Tenth Revision (ICD-10). Deaths assigned to ICD code C55, cancer of the uterus, part unspecified, and distributed pro-rata to cervix uteri cancer (C53) and corpus uteri cancer (C54).
The populations used in the denominator of the cervical cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The cervical cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The age-adjusted cervical cancer mortality rate for 2019 is 25 per 100 000 population in country A and 16 per 100 000 population in country B; that is, in that year cervical cancer was responsible for the death of 25 per 100 000 women in country A, compared to country B, where 16 per 100 000 women died from that cause. This means that, after removing the effect of differences in the age distribution of the population in the two countries, women in country A had a higher risk of dying from cervical cancer than women in country B.
Desagregation
No disaggregation
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted cervical cancer mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences its result is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the cervical cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted cervical cancer mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences its result is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the cervical cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Populations for countries:
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from: https://population.un.org/wpp/
Populations for countries:
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from: https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 10. Mortality rate due to cervical cancer
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 10. Mortality rate due to cervical cancer
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
Cholera cases
Domain
Morbidity
Subdomain
Communicable Diseases
Definition
Number of cholera cases in a given country, territory, or geographical area, during a specific period (usually one year). Includes cases confirmed clinically, epidemiologically, or by laboratory.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Cholera is an acute diarrheal infection with high epidemic potential, caused by ingesting food or water contaminated with the Vibrio cholerae bacterium. Cholera transmission is closely linked to inadequate access to clean water and sanitation facilities.
The number of cases quantifies the magnitude of the disease, reflecting a population's health status and level of socioeconomic development. It raises awareness of inequities and factors enabling transmission of Vibrio cholerae and it helps identify populations in need of guaranteed access to timely and quality health care, strengthen educational interventions and hygiene measures, and improve cholera surveillance activities.
This indicator is used to monitor countries' progress with the Ending Cholera Global Roadmap to 2030. It supports multisectoral strategies to address the disease, as well as development and assessment of public policies for universal access to drinking water and sanitation, and allocation of resources to sustain these initiatives over time. It is used to detect cholera outbreaks and analyze temporal and geographical trends in a population.
The number of cases quantifies the magnitude of the disease, reflecting a population's health status and level of socioeconomic development. It raises awareness of inequities and factors enabling transmission of Vibrio cholerae and it helps identify populations in need of guaranteed access to timely and quality health care, strengthen educational interventions and hygiene measures, and improve cholera surveillance activities.
This indicator is used to monitor countries' progress with the Ending Cholera Global Roadmap to 2030. It supports multisectoral strategies to address the disease, as well as development and assessment of public policies for universal access to drinking water and sanitation, and allocation of resources to sustain these initiatives over time. It is used to detect cholera outbreaks and analyze temporal and geographical trends in a population.
Estimation method
The data is primarily collected from national surveillance systems that regularly report to the Pan American Health Organization.
Interpretation - example
In country A there were 12 cases of cholera in 2019.
Desagregation
By sex
Limitations
The value of this indicator depends on the effectiveness of surveillance systems, which in turn may be affected by low diagnostic suspicion and underreporting of cases. Other factors include poor access to laboratories with appropriate diagnostic methods, and availability of data on confirmed cases from private health centers.
In order not to hinder effective control measures, cholera should be confirmed and reported in national surveillance systems separately from other diarrheal diseases.
In order not to hinder effective control measures, cholera should be confirmed and reported in national surveillance systems separately from other diarrheal diseases.
Data source(s)
National cholera surveillance systems
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Cholera prevention and control. World Health Assembly, 71 (WHA71.4, 2018). Available from: https://www.who.int/publications/i/item/10665-279470
Global Task Force on Cholera Control (GTFCC). Ending cholera, a global roadmap to 2030. Available from: https://www.gtfcc.org/wp-content/uploads/2019/10/gtfcc-ending-cholera-a-global-roadmap-to-2030.pdf
World Health Organization (WHO). Cholera prevention and control. World Health Assembly, 71 (WHA71.4, 2018). Available from: https://www.who.int/publications/i/item/10665-279470
Global Task Force on Cholera Control (GTFCC). Ending cholera, a global roadmap to 2030. Available from: https://www.gtfcc.org/wp-content/uploads/2019/10/gtfcc-ending-cholera-a-global-roadmap-to-2030.pdf
Circulatory diseases mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from circulatory diseases in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from circulatory diseases and to assess the presence of risk factors, such as those associated with the environment or lifestyle.
Its result is applicable to the design, implementation, and evaluation of health policies on circulatory diseases and the distribution of economic, human, and technological resources for this group of diseases, among others.
Removing the effect of a different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from circulatory diseases, in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies on circulatory diseases and the distribution of economic, human, and technological resources for this group of diseases, among others.
Removing the effect of a different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from circulatory diseases, in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from circulatory diseases from the World Health Organization (WHO) Global Health Estimates (GHE) and the civil registry and national vital statistics systems of the countries of the Region of the Americas.
The underlying causes of death in this category correspond to codes
I00 – I99 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the circulatory diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The circulatory diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The underlying causes of death in this category correspond to codes
I00 – I99 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the circulatory diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The circulatory diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The age-adjusted circulatory diseases mortality rate for 2019 was 65 per 100 000 population in country A and 51 per 100 000 population in country B; that is, in that year 65 people died from circulatory diseases per 100 000 population of country A, compared to country B, where 51 people died from the same group of causes per 100 000 population. This means that, in 2019, the population of country A had a higher risk of dying from circulatory diseases than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted circulatory diseases mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the circulatory diseases mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted circulatory diseases mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the circulatory diseases mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Cirrhosis mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from cirrhosis and other chronic liver diseases in the population, in a given country or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution of the population. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The cirrhosis mortality rate reflects a population’s health status and socio-economic development, identifies those with greater risk factors for dying from cirrhosis and other chronic liver diseases, and encourages research in this area, for example, per capita alcohol consumption and the cirrhosis mortality rate in a given population.
Its result is applicable to the design, implementation, and evaluation of health policies on cirrhosis and other chronic liver diseases and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, evaluating over time the effectiveness of interventions promoting healthy lifestyles.
The age-adjusted cirrhosis mortality rate allows for the comparison across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies on cirrhosis and other chronic liver diseases and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, evaluating over time the effectiveness of interventions promoting healthy lifestyles.
The age-adjusted cirrhosis mortality rate allows for the comparison across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from cirrhosis and other chronic liver diseases, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes K70, K74 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted cirrhosis mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The cirrhosis mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes K70, K74 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted cirrhosis mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The cirrhosis mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted cirrhosis mortality rate for 2019 was 21 per 100 000 population in country A and 12 per 100 000 population in country B; that is, in 2019 cirrhosis and other chronic liver diseases were responsible for the death of 21 people per 100 000 population in country A, compared to country B, where 12 people died from the same group of causes per 100 000 population. This means that, after removing the effect of differences in the age distribution on the population of both countries, in 2019 the risk of dying from cirrhosis was greater in the population of country A.
Desagregation
By sex
Limitations
The age-adjusted cirrhosis mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted cirrhosis mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different range of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating this indicator requires a civil registry system with good coverage. Deaths from this group of pathologies must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality and should include the etiology of the cirrhosis and other chronic liver diseases; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted cirrhosis mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different range of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating this indicator requires a civil registry system with good coverage. Deaths from this group of pathologies must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality and should include the etiology of the cirrhosis and other chronic liver diseases; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Colorectal cancer mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from colorectal cancer in the population, in a certain country, territory, or geographic area during a specific year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from colorectal cancer and to evaluate the presence of potential risk factors, such as those associated with diet or lifestyle.
It is applicable to the design, implementation, and evaluation of health policies for the prevention and control of colorectal cancer and the distribution of economic, human, and technological resources for this disease, among others. Its applications include evaluating the effect of the implementation of screening strategies, such as the fecal occult blood test, for reducing mortality from this type of cancer.
Removing the effect of a different age structure by using a standard population makes it possible to analyze the colorectal cancer mortality rate across populations or in the same population over time.
It is applicable to the design, implementation, and evaluation of health policies for the prevention and control of colorectal cancer and the distribution of economic, human, and technological resources for this disease, among others. Its applications include evaluating the effect of the implementation of screening strategies, such as the fecal occult blood test, for reducing mortality from this type of cancer.
Removing the effect of a different age structure by using a standard population makes it possible to analyze the colorectal cancer mortality rate across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from colorectal cancer from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes C18 – C21 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the colorectal cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The colorectal cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes C18 – C21 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the colorectal cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The colorectal cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted colorectal cancer mortality rate for 2019 is 12 per 100 000 population in country A and 6 per 100 000 population in country B; that is, in country A 12 people died from colorectal cancer per 100 000 population, compared to country B, where six died per 100 000 population. This means that, after removing the effect of differences in the age structure in the two countries, the risk of dying from colorectal cancer in 2019 is higher in country A.
Desagregation
By sex
Limitations
The age-adjusted colorectal cancer mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted colorectal cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections, the use of a different group of ICD-10 codes, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the colorectal cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in the system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted colorectal cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections, the use of a different group of ICD-10 codes, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the colorectal cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in the system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Communicable diseases mortality rate (100 000 pop)
Domain
Mortality
Subdomain
Communicable diseases
Definition
Ratio between the estimated total number of deaths from communicable diseases and maternal, perinatal and nutritional conditions in the population, in a given country or geographic area, during a specific calendar year, divided by the total population, generally estimated in the middle of the same year (July 1). Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The mortality rate from communicable diseases represents the risk of dying from this group of pathologies in a certain population or geographic area. It reflects the extent of deaths from communicable diseases in the population.
Its result is applicable to the design, implementation and evaluation of health policies for the prevention, diagnosis, and control of communicable diseases and maternal, perinatal and nutritional conditions and the distribution of economic, human and technological resources for this group of pathologies, among others. These applications include planning maternal and child, nutrition and vaccination programs and designing educational interventions and environmental sanitation measures.
It helps identify populations at higher risk of dying from communicable diseases and assess the presence of risk factors, such as deficiencies in basic environmental sanitation or in coverage of immunization programs against immunopreventable diseases.
Its result is applicable to the design, implementation and evaluation of health policies for the prevention, diagnosis, and control of communicable diseases and maternal, perinatal and nutritional conditions and the distribution of economic, human and technological resources for this group of pathologies, among others. These applications include planning maternal and child, nutrition and vaccination programs and designing educational interventions and environmental sanitation measures.
It helps identify populations at higher risk of dying from communicable diseases and assess the presence of risk factors, such as deficiencies in basic environmental sanitation or in coverage of immunization programs against immunopreventable diseases.
Estimation method
For the numerator of this indicator, deaths from communicable diseases obtained from the World Health Organization (WHO) Global Health Estimates (GHE) are used, which were developed based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and interagency groups and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes A00 - B99, D50 - D53, D64.9, E00 - E02, E40 - E46, E50 - E64, G00 - G04, G14, H65 - H66, J00 - J22, N70 - N73, O00 - O99, P00 - P96, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator for the mortality rate from communicable diseases are estimates by the United Nations Population Division.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as unhelpful causes (known as garbage code).
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The underlying causes of death correspond to codes A00 - B99, D50 - D53, D64.9, E00 - E02, E40 - E46, E50 - E64, G00 - G04, G14, H65 - H66, J00 - J22, N70 - N73, O00 - O99, P00 - P96, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator for the mortality rate from communicable diseases are estimates by the United Nations Population Division.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as unhelpful causes (known as garbage code).
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The mortality rate from communicable diseases in country A for 2019 was 20.2 per 100 000 population, that is, during that year 20 people died per 100 000 population of country A due to some communicable disease and maternal, perinatal and nutritional conditions.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time, therefore, it should be interpreted with caution.
The value of the age-adjusted mortality rate from communicable diseases and maternal, perinatal and nutritional conditions will depend on the standard population used for its adjustment.
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population forecasts and estimates, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate requires a civil registry system with good coverage, that deaths be recorded in this system in a timely manner and the certification of cause of death be of good quality, otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted mortality rate from communicable diseases and maternal, perinatal and nutritional conditions will depend on the standard population used for its adjustment.
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population forecasts and estimates, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate requires a civil registry system with good coverage, that deaths be recorded in this system in a timely manner and the certification of cause of death be of good quality, otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Communicable diseases mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from communicable diseases and maternal, perinatal, and nutritional conditions in the population, in a given country, territory or geographic area, during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions helps to identify populations at higher risk of dying from this group of causes and to assess the presence of risk factors, such as socio-economic factors or those associated with sanitary and environmental conditions.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of communicable diseases, and maternal, perinatal, and nutritional conditions, and the distribution of economic, human, and technological resources for these diseases, among others. These applications include the planning and evaluation of maternal and child, vaccination and nutrition programs and the design and implementation of environmental sanitation measures.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of communicable diseases, and maternal, perinatal, and nutritional conditions, and the distribution of economic, human, and technological resources for these diseases, among others. These applications include the planning and evaluation of maternal and child, vaccination and nutrition programs and the design and implementation of environmental sanitation measures.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from communicable diseases and maternal, perinatal, and nutritional conditions, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes A00 - B99, D50 - D53, D64.9, E00 - E02, E40 - E46, E50 - E64, G00 - G04, G14, H65 - H66, J00 - J22, N70 - N73, O00 - O99, P00 - P96, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes A00 - B99, D50 - D53, D64.9, E00 - E02, E40 - E46, E50 - E64, G00 - G04, G14, H65 - H66, J00 - J22, N70 - N73, O00 - O99, P00 - P96, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions for 2019 was 23 per 100 000 population in country A and 12 per 100 000 population in country B; that is, in that year these diseases and conditions caused the death of 23 people per 100 000 population of country A, compared to country B, where 12 people died from the same group of causes per 100 000 population. This means that, in 2019, the population of country A had a higher risk of dying from communicable diseases and maternal, perinatal, and nutritional conditions than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate from communicable diseases and maternal, perinatal, and nutritional conditions requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Concentrations of fine particulate matter (PM 2.5)
Domain
Risk factor
Subdomain
Environment
Definition
The mean annual concentration of fine suspended particles of less than 2.5 microns in diameters is a common measure of air pollution. The mean is a population-weighted average for urban population in a country.
Measurement Unit
µg/m3
Type of measurement
Average
Type of statistics
Corrected
Purpose
Air pollution consists of many pollutants, among other particulate matter. These particles are able to penetrate deeply into the respiratory tract and therefore constitute a risk for health by increasing mortality from respiratory infections and diseases, lung cancer, and selected cardiovascular diseases.
The indicator makes it possible monitor air quality trends and the result of air pollution control measures. Air quality is related to the climate of the planet and ecosystems around the world. Its result is applicable to guide policies to raise awareness about the risk of air pollution, implement measures to reduce exposure to air pollution and reduce the risks of air pollution as beneficial strategies for climate and health, because in addition to reducing the burden of disease, it helps mitigate climate change in the short and long term.
The indicator makes it possible monitor air quality trends and the result of air pollution control measures. Air quality is related to the climate of the planet and ecosystems around the world. Its result is applicable to guide policies to raise awareness about the risk of air pollution, implement measures to reduce exposure to air pollution and reduce the risks of air pollution as beneficial strategies for climate and health, because in addition to reducing the burden of disease, it helps mitigate climate change in the short and long term.
Estimation method
The value of this indicator comes from estimate made by the World Health Organization (WHO), the body responsible for the custody of this and two others indicator related to air pollution and health.
Concentration of PM2.5 are regularly measured from fixed-site, population-oriented monitors located within the metropolitan areas. High-quality measurements of PM concentration from all the monitors in the metropolitan area can be averaged to develop a single estimate.
Although PM2.5 is measured at many thousands of locations throughout the world, the amount of monitors in different geographical areas vary, with some areas having little or no monitoring. In order to produce global estimates at high resolution (0.1◦ grid‐cells), additional data is required. Annual urban mean concentration of PM2.5 is estimated with improved modelling using data integration from satellite remote sensing, population estimates, topography and ground measurements.
Concentration of PM2.5 are regularly measured from fixed-site, population-oriented monitors located within the metropolitan areas. High-quality measurements of PM concentration from all the monitors in the metropolitan area can be averaged to develop a single estimate.
Although PM2.5 is measured at many thousands of locations throughout the world, the amount of monitors in different geographical areas vary, with some areas having little or no monitoring. In order to produce global estimates at high resolution (0.1◦ grid‐cells), additional data is required. Annual urban mean concentration of PM2.5 is estimated with improved modelling using data integration from satellite remote sensing, population estimates, topography and ground measurements.
Interpretation - example
In 2019 in country A the concentration of PM2.5 was 10µg/m3. This means that, for this year, the population weighted average of the exposure of the population in the country was 10µg/m3.
Desagregation
By urban, rural area
Limitations
Ideally, the monitoring data used to calculate the average annual PM2.5 concentrations should be collected throughout the year, for several years, to reduce bias owing to seasonal fluctuations or to a non-representative year.
Care should be taken to ensure that the monitors used are not unduly influenced by a single source of pollution (i.e. a power plant, factory or highway); rather, the monitors should reflect exposures over a wide area.
Although it is likely that particulate matter data will be available only for larger cities, residents of agglomerations of less than 100 000 inhabitants and of rural areas are also exposed to PM from local industrial activity, transportation, biomass fuels, open burning and regional haze.
Data from different countries are of limited comparability because of:
(a) Different location of measurement stations;
(b) Different measurement methods;
(c) Different temporal coverage of certain measurements; if only part of the year was covered, the measurement may significantly deviate from the annual mean due to seasonal variability;
(d) Possible inclusion of data which were not eligible for this database due to insufficient information to ensure compliance;
(e) Heterogeneous quality of measurements;
(f) Omission of data which are known to exist, but which could not yet be accessed due to language issues or limited accessibility.
Care should be taken to ensure that the monitors used are not unduly influenced by a single source of pollution (i.e. a power plant, factory or highway); rather, the monitors should reflect exposures over a wide area.
Although it is likely that particulate matter data will be available only for larger cities, residents of agglomerations of less than 100 000 inhabitants and of rural areas are also exposed to PM from local industrial activity, transportation, biomass fuels, open burning and regional haze.
Data from different countries are of limited comparability because of:
(a) Different location of measurement stations;
(b) Different measurement methods;
(c) Different temporal coverage of certain measurements; if only part of the year was covered, the measurement may significantly deviate from the annual mean due to seasonal variability;
(d) Possible inclusion of data which were not eligible for this database due to insufficient information to ensure compliance;
(e) Heterogeneous quality of measurements;
(f) Omission of data which are known to exist, but which could not yet be accessed due to language issues or limited accessibility.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available at:
https://www.who.int/data/gho/indicator-metadata-registry
https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
2 to 3 years
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)
Available at: https://unstats.un.org/sdgs/metadata/
Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted)
Available at: https://unstats.un.org/sdgs/metadata/
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C. 2021. Available from: https://iris.paho.org/handle/10665.2/53299?locale-attribute=es
World Health Organization (WHO). Air Quality data base. Available at: https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database
Shaddick G et al (2016). Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution. Royal Statistical Society, arXiv:1609.0014. Available at: https://arxiv.org/abs/1609.00141
World Health Organization (WHO). Road map for an enhanced global response to the adverse health effects of air pollution. World Health Assembly, 71. (2018). Available at:
https://apps.who.int/iris/handle/10665/276321
World Health Organization (WHO). Air Quality data base. Available at: https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database
Shaddick G et al (2016). Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution. Royal Statistical Society, arXiv:1609.0014. Available at: https://arxiv.org/abs/1609.00141
World Health Organization (WHO). Road map for an enhanced global response to the adverse health effects of air pollution. World Health Assembly, 71. (2018). Available at:
https://apps.who.int/iris/handle/10665/276321
Contraceptive prevalence use, modern methods (%)
Domain
Health service coverage
Subdomain
Maternal and reproductive health
Definition
Percentage of women of reproductive age (15-49 years), or their partner, currently using a modern contraceptive method among those who do not want to have (additional) children or who want to postpone the next pregnancy, in a given country, territory, or geographical area, in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
This indicator is used to assess overall coverage levels for family planning programs and services, making it possible to monitor countries' progress toward achieving universal access to sexual and reproductive health services. It helps reveal health inequalities and identify populations that require targeted resources to strengthen the area of sexual and reproductive health.
Availability of and access to family planning services with modern methods helps improve maternal and child health by preventing unwanted or poorly spaced pregnancies, which are at greater risk of poor obstetric outcomes.
Availability of and access to family planning services with modern methods helps improve maternal and child health by preventing unwanted or poorly spaced pregnancies, which are at greater risk of poor obstetric outcomes.
Estimation method
The prevalence of modern contraceptive use comes from estimates made by the United Nations Population Division, based on data from internationally coordinated national household surveys, such as the Demographic and Health Surveys (DHS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Gender and Generation Surveys (GGS), and other nationally sponsored surveys.
Modern contraceptive methods include female and male sterilization, intrauterine devices (IUD), implants, injectable methods, oral contraceptive pills, male and female condoms, vaginal barrier methods (including diaphragm, cervical cap, and spermicidal foam, jelly, cream, and sponge), lactation amenorrhea method, emergency contraception, and other modern methods that are not reported separately (e.g., contraceptive patch or vaginal ring).
For more details on methodology, see:
United Nations, Department of Economic and Social Affairs, Population Division (2021). World Contraceptive Use 2021. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
Modern contraceptive methods include female and male sterilization, intrauterine devices (IUD), implants, injectable methods, oral contraceptive pills, male and female condoms, vaginal barrier methods (including diaphragm, cervical cap, and spermicidal foam, jelly, cream, and sponge), lactation amenorrhea method, emergency contraception, and other modern methods that are not reported separately (e.g., contraceptive patch or vaginal ring).
For more details on methodology, see:
United Nations, Department of Economic and Social Affairs, Population Division (2021). World Contraceptive Use 2021. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
Interpretation - example
According to 2019 data, the prevalence of use of modern contraceptive methods in country A is 67%, meaning that, in that country, of every 100 women aged 15 to 49 years who wish to delay or prevent motherhood, 67 currently use a modern contraceptive method.
Desagregation
By marital status: Married/ in union, unmarried
Limitations
International comparability of this indicator is affected by aspects of the design and implementation of the survey from which the data are obtained. Factors to be considered include the range of contraceptives included, and specific characteristics of the base population (age, sex, marital status, etc.). The time frame used to assess contraceptive prevalence can also vary.
Data source(s)
United Nations, Population Division. Available from: https://www.un.org/development/desa/pd/data/sdg-indicator-371-contraceptive-use
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.7.1. Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods.
Available from: https://sdgs.un.org/goals
Sustainable Development Goals (SDG).
Indicator 3.7.1. Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods.
Available from: https://sdgs.un.org/goals
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations. Population Division. World Contraceptive Use. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
United Nations. Sustainable Development Goals (SDG). E-Learning tool for SDG Indicator 3.7.1. Available from:
https://www.un.org/development/desa/pd/file/10712
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations. Population Division. World Contraceptive Use. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
United Nations. Sustainable Development Goals (SDG). E-Learning tool for SDG Indicator 3.7.1. Available from:
https://www.un.org/development/desa/pd/file/10712
Corpus uteri cancer mortality rate (age-adjusted per 100 000 pop); female
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from corpus uteri cancer, in the population of women in a given country, territory or geographic area during a specific calendar year, divided by total women in that population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Removing the effect of a different age structure by using a standard population makes it possible to analyze the corpus uteri cancer mortality rate across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, diagnosis, treatment, and control of corpus uteri cancer and the distribution of economic, human, and technological resources, among others. Its applications include, for example, quantifying specialized health personnel and infrastructure needed to address this type of cancer.
It helps to identify groups of women at higher risk of dying from corpus uteri cancer and to assess the presence of specific risk factors for certain population groups and the opportunity for access to timely diagnosis and treatment.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, diagnosis, treatment, and control of corpus uteri cancer and the distribution of economic, human, and technological resources, among others. Its applications include, for example, quantifying specialized health personnel and infrastructure needed to address this type of cancer.
It helps to identify groups of women at higher risk of dying from corpus uteri cancer and to assess the presence of specific risk factors for certain population groups and the opportunity for access to timely diagnosis and treatment.
Estimation method
The numerator of this indicator uses corpus uteri cancer deaths from the World Health Organization (WHO) Global Health Estimates (GHE) and the civil registry and national vital statistics systems of the countries of the Region of the Americas.
The populations used in the denominator of the corpus uteri cancer mortality rate are from estimates by the United Nations Population Division.
The underlying causes of death correspond to codes C54 of the International Classification of Diseases, Tenth Revision (ICD-10). Deaths assigned to ICD code C55, cancer of the uterus, part unspecified, and distributed pro-rata to cervix uteri cancer (C53) and corpus uteri cancer (C54).
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The corpus uteri cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The populations used in the denominator of the corpus uteri cancer mortality rate are from estimates by the United Nations Population Division.
The underlying causes of death correspond to codes C54 of the International Classification of Diseases, Tenth Revision (ICD-10). Deaths assigned to ICD code C55, cancer of the uterus, part unspecified, and distributed pro-rata to cervix uteri cancer (C53) and corpus uteri cancer (C54).
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The corpus uteri cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The age-adjusted corpus uteri cancer mortality rate for 2019 is 15 per 100 000 population in country A and 12 per 100 000 population in country B; that is, in that year 15 women per 100 000 in country A died from uterine cancer, compared to country B, where 12 women died from that cause per 100 000. This means that, after removing the effect of differences in the age structure in the population of the two countries, in 2019 women in country A had a higher risk of dying from corpus uteri cancer than women in country B.
Desagregation
No disaggregation
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted corpus uteri cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate from corpus uteri cancer requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted corpus uteri cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate from corpus uteri cancer requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from:
https://www.who.int/data/global-health-estimates
Populations for countries:
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from: https://population.un.org/wpp/
https://www.who.int/data/global-health-estimates
Populations for countries:
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from: https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No.31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No.31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
Crude birth rate (1 000 pop)
Domain
Sociodemographic
Subdomain
Demographic
Definition
The ratio between the number of births in a population during a specific year and the total population for the same year.
Measurement Unit
Per 1 000 population
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
This indicator is used for planning, implementation, development, and evaluation of public policies related to maternal and childcare. It allows evaluation of the effect of implementing birth control or birth incentivization strategies. It is also used to distribute human, economic, and technological resources necessary for perinatal health programs.
The crude birth rate is an indicator of population growth. Alongside the crude mortality rate, these indicators evaluate the level of aging in a population and estimate its natural growth.
If based on a standardized reference population, temporal and geographical birth rate trends between different countries or within the same country over time can be evaluated and compared. The indicator can also be applied to make projections on health care.
The crude birth rate is an indicator of population growth. Alongside the crude mortality rate, these indicators evaluate the level of aging in a population and estimate its natural growth.
If based on a standardized reference population, temporal and geographical birth rate trends between different countries or within the same country over time can be evaluated and compared. The indicator can also be applied to make projections on health care.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
It represents how often births occur in a population.
The crude birth rate of country A for the five-year period 2015–2020 was 19.9 per 1 000 inhabitants. In Country B, it was 40.0 per 1 000 inhabitants. This means that, during this period, country A had 19.9 live births per 1 000 inhabitants, or half of the births of neighboring country B.
The crude birth rate of country A for the five-year period 2015–2020 was 19.9 per 1 000 inhabitants. In Country B, it was 40.0 per 1 000 inhabitants. This means that, during this period, country A had 19.9 live births per 1 000 inhabitants, or half of the births of neighboring country B.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be limited by factors such as the quality of population censuses and demographic surveys used to make calculations, including misinterpretation of questions or memory bias. Estimating the birth rate requires a national civil registry with adequate coverage (greater than 90 per cent) and timely birth registration.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Its value can be affected by variations in the sex and age structure of the population being analyzed. Because the confounding effect of age persists in this indicator, its value should not be used to make comparisons between different populations or even within the same population over time.
The crude birth rate includes births, which are only one component of population change. As such, this indicator shouldn't be confused with the growth rate, which considers all components of change (births, deaths, and migratory movements).
It should not be compared with the fertility rate, because the latter is not affected by variations in the age structure of the population and considers women in its denominator.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Its value can be affected by variations in the sex and age structure of the population being analyzed. Because the confounding effect of age persists in this indicator, its value should not be used to make comparisons between different populations or even within the same population over time.
The crude birth rate includes births, which are only one component of population change. As such, this indicator shouldn't be confused with the growth rate, which considers all components of change (births, deaths, and migratory movements).
It should not be compared with the fertility rate, because the latter is not affected by variations in the age structure of the population and considers women in its denominator.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. Available from: https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Avaiable from:
https://www.who.int/data/gho/indicator-metadata-registry
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from:
https://www.paho.org/bra/dmdocuments/indicadores.pdf
https://www.who.int/data/gho/indicator-metadata-registry
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from:
https://www.paho.org/bra/dmdocuments/indicadores.pdf
Crude death rate (1 000 pop)
Domain
Sociodemographic
Subdomain
Demographic
Definition
The ratio between the number of deaths in a population during a specific year and the total population for the same year.
Measurement Unit
Per 1 000 population
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
Reflects the health status and socioeconomic or environmental conditions of a population. Can be applied to design and assess health policies. Its value encourages research on the causes of death and predominant risk factors in order to implement specific preventive and control strategies. It identifies vulnerable populations and focuses interventions.
Based on a standardized reference population, it is possible to analyze temporal and geographical mortality trends and comparisons between different populations and within the same population over time.
It can be used to estimate natural population growth, or the difference between births and deaths, and is calculated by subtracting the crude mortality rate from the crude birth rate.
Based on a standardized reference population, it is possible to analyze temporal and geographical mortality trends and comparisons between different populations and within the same population over time.
It can be used to estimate natural population growth, or the difference between births and deaths, and is calculated by subtracting the crude mortality rate from the crude birth rate.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
The crude mortality rate of country A in 2016 was 5.9 per 1 000 inhabitants. This means that in 2016, six people died per 1 000 inhabitants in this country.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths in the system, and the integrity of the registry.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The crude mortality rate is affected by the age structure of the population. Therefore, to evaluate temporal trends or to compare rates between countries or geographical areas, it must use a standardized reference population.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The crude mortality rate is affected by the age structure of the population. Therefore, to evaluate temporal trends or to compare rates between countries or geographical areas, it must use a standardized reference population.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from:
https://www.prb.org/population-handbook/
https://www.who.int/data/gho/indicator-metadata-registry
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from:
https://www.prb.org/population-handbook/
Deaths (thousands)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Average deaths in a specific year for a given country, territory, or geographical area, usually estimated at mid-year.
Measurement Unit
Thousands
Type of measurement
Magnitude
Type of statistics
Corrected/Predicted
Purpose
The number of deaths shows the magnitude of deaths in a given country, territory, or geographical area. This indicator is used to estimate the crude mortality rate. By disaggregating by age or sex, the mortality rate can be estimated according to age group and sex.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
In country A there were a total of 29 400 deaths in 2018.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths in the system, and the integrity of the registry.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Because the number of deaths in a given population is affected by its age structure, the value of this indicator should not be used to make comparisons between different populations or even within the same population over time.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Because the number of deaths in a given population is affected by its age structure, the value of this indicator should not be used to make comparisons between different populations or even within the same population over time.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
Deaths due to tetanus in children under age 5
Domain
Mortality
Subdomain
Child health
Definition
Total number of tetanus deaths registered in children under 5 in a specific country, territory, or geographic area in a given calendar year.
Measurement Unit
Deaths
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
The number of deaths from tetanus in children under 5 quantifies the impact of a preventable disease on mortality in this age group and highlights this disease as a public health problem.
This indicator supports public policy-making aimed at reducing mortality in children under 5. It makes it possible to identify areas that require the establishment or intensification of a tetanus surveillance system and to strengthen the tetanus vaccination program.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and specific risk factors.
This indicator applies to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of tetanus and to the distribution of economic, human, and technological resources for this disease, among others. Its main applications include promoting vaccination, improving the hygiene of medical and dental procedures, and training health care workers in the prevention and early diagnosis of this disease. It helps strengthen access to quality health care.
This indicator supports public policy-making aimed at reducing mortality in children under 5. It makes it possible to identify areas that require the establishment or intensification of a tetanus surveillance system and to strengthen the tetanus vaccination program.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and specific risk factors.
This indicator applies to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of tetanus and to the distribution of economic, human, and technological resources for this disease, among others. Its main applications include promoting vaccination, improving the hygiene of medical and dental procedures, and training health care workers in the prevention and early diagnosis of this disease. It helps strengthen access to quality health care.
Estimation method
The number of deaths from tetanus (ICD-10 code: A35) is obtained from deaths in children under 5 in a specific country, territory, or geographic area, reported by the countries to PAHO.
Interpretation - example
In 2019, there were 27 deaths from tetanus among children under 5 in country A.
Desagregation
No disaggregation
Limitations
This indicator refers to tetanus deaths in children under 5, which should not be confused with neonatal tetanus. Tetanus surveillance programs should be able to clearly differentiate between the neonatal and non-neonatal types.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths from tetanus in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths from tetanus in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Deaths due to tetanus neonatorum
Domain
Mortality
Subdomain
Child health
Definition
Total number of deaths registered due to tetanus neonatorum between 0 and 28 days of age (neonatal period) among live births in a specific country, territory, or geographic area in a given calendar year.
Tetanus is an immunopreventable disease that is acquired when spores of the bacterium Clostridium Tetani infect a wound or umbilical stump.
Tetanus is an immunopreventable disease that is acquired when spores of the bacterium Clostridium Tetani infect a wound or umbilical stump.
Measurement Unit
Deaths
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
The number of deaths due to tetanus neonatorum quantifies the impact of a preventable disease on mortality in this period and highlights this disease as a public health problem.
This indicator supports public policy-making aimed at reducing neonatal mortality. It makes it possible to identify areas that require the establishment or intensification of a neonatal tetanus surveillance system and to strengthen the tetanus vaccination program.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and specific risk factors.
This indicator applies to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of neonatal tetanus and to the distribution of economic, human, and technological resources for this disease, among others.
Its main applications include promoting vaccination, improving the hygiene of procedures for childbirth and newborns, and training health personnel in the prevention of this disease. It helps strengthen access to quality maternal and child health care.
This indicator supports public policy-making aimed at reducing neonatal mortality. It makes it possible to identify areas that require the establishment or intensification of a neonatal tetanus surveillance system and to strengthen the tetanus vaccination program.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and specific risk factors.
This indicator applies to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of neonatal tetanus and to the distribution of economic, human, and technological resources for this disease, among others.
Its main applications include promoting vaccination, improving the hygiene of procedures for childbirth and newborns, and training health personnel in the prevention of this disease. It helps strengthen access to quality maternal and child health care.
Estimation method
The number of deaths due to tetanus neonatorum (ICD-10 code: A33) between 0 and 28 days of life (neonatal period) of live births is reported annually by the countries to PAHO.
Interpretation - example
In 2019, there were 24 deaths due to tetanus neonatorum among live births aged 0 to 28 days in country A.
Desagregation
No disaggregation
Limitations
This indicator refers to deaths due to tetanus neonatorum, which should not be confused with non-neonatal tetanus. Tetanus surveillance programs should be able to clearly differentiate between the two types.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths due to tetanus neonatorum requires a civil registry system with good coverage. Births and deaths of infant populations must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths due to tetanus neonatorum requires a civil registry system with good coverage. Births and deaths of infant populations must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Deaths from communicable diseases (%)
Domain
Mortality
Subdomain
Cause of death
Definition
Proportion of deaths from communicable diseases and maternal, perinatal, and nutritional conditions in the population, in a given country or geographic area during a specific calendar year, in relation to total estimated deaths for the same place and year. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
The proportion of deaths from communicable diseases and maternal, perinatal, and nutritional conditions helps to quantify the relative importance of this group in relation to total deaths from all causes in a given population or geographic area.
Its result is applicable to the design, implementation and evaluation of health policies for the prevention and control of communicable diseases and maternal, perinatal, and nutritional conditions and the distribution of economic, human, and technological resources for this pathology, among others. Its applications include planning and evaluation of maternal and child nutrition and vaccination programs and the design and implementation of educational interventions and environmental sanitation measures.
Its result is applicable to the design, implementation and evaluation of health policies for the prevention and control of communicable diseases and maternal, perinatal, and nutritional conditions and the distribution of economic, human, and technological resources for this pathology, among others. Its applications include planning and evaluation of maternal and child nutrition and vaccination programs and the design and implementation of educational interventions and environmental sanitation measures.
Estimation method
For the percentage of deaths from communicable diseases and maternal, perinatal, and nutritional conditions, the number of deaths from this group of diseases is used as the numerator and the total number of deaths from all causes as the denominator.
Underlying causes of death correspond to codes A00 - B99, D50 - D53, D64.9, E00 - E02, E40 - E46, E50 - E64, G00 - G04, G14, H65 - H66, J00 - J22, N70 - N73, O00 - O99, P00 - P96, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, cause of death estimates are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
Underlying causes of death correspond to codes A00 - B99, D50 - D53, D64.9, E00 - E02, E40 - E46, E50 - E64, G00 - G04, G14, H65 - H66, J00 - J22, N70 - N73, O00 - O99, P00 - P96, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, cause of death estimates are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
Interpretation - example
According to 2019 data, 5% of deaths in country A were due to communicable diseases and maternal, perinatal, and nutritional conditions; that is, for every 100 deaths from all causes in country A in 2019, five were from a communicable disease or maternal, perinatal or nutritional conditions.
Desagregation
By sex
Limitations
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the proportion of deaths from communicable diseases and maternal, perinatal, and nutritional conditions requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Estimating the proportion of deaths from communicable diseases and maternal, perinatal, and nutritional conditions requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Deaths from measles
Domain
Mortality
Subdomain
Cause of death
Definition
Total number of registered deaths, the selected underlying cause of which was measles, in a specific country, territory, or geographic area in a given year.
Measurement Unit
Deaths
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
The number of deaths from measles helps to quantify the importance of this disease as a public health problem for a given population or geographic area. This indicator supports public policy-making aimed at reducing mortality from this disease, mainly in children under 5 and pregnant women, which are at-risk populations.
The indicator’s result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of measles and the distribution of economic, human, and technological resources for this disease. Its applications include planning and evaluation of measles vaccination programs and prioritization of health care quality.
The indicator’s result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of measles and the distribution of economic, human, and technological resources for this disease. Its applications include planning and evaluation of measles vaccination programs and prioritization of health care quality.
Estimation method
The number of deaths from measles (ICD-10 code: B05) uses deaths in a given country, territory, or geographic area reported by the countries to PAHO.
Interpretation - example
In 2019, there were 5 deaths from measles in the population of country A.
Desagregation
By sex
Limitations
The number of deaths from measles quantifies this disease’s impact on mortality, which should not be interpreted as the risk of dying, which is estimated by the measles mortality rate.
This indicator’s estimated value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths from measles requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
This indicator’s estimated value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths from measles requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Measles; 2019. Available from: https://www.who.int
ews-room/fact-sheets/detail/measles
World Health Organization (WHO). Global measles and rubella strategic plan: 2012–2020. Available from: http://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236_eng.pdf?sequence=1
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Measles; 2019. Available from: https://www.who.int
ews-room/fact-sheets/detail/measles
World Health Organization (WHO). Global measles and rubella strategic plan: 2012–2020. Available from: http://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236_eng.pdf?sequence=1
Deaths from noncommunicable diseases (%)
Domain
Mortality
Subdomain
Noncommunicable diseases
Definition
Proportion of deaths from noncommunicable diseases in the population in a given country or geographic area during a specific calendar year, in relation to total estimated deaths for the same place and year. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
The proportion of deaths from noncommunicable diseases helps to quantify the relative importance of this group of pathologies in the mortality of a given population or geographic area.
This indicator reflects a population’s lifestyles, socioeconomic development, and health status, and is also related to population aging and rapid, poorly planned urbanization. Its analysis makes it possible to identify populations with greater risk factors for dying from noncommunicable diseases and encourage research in this area.
The proportion of deaths from noncommunicable diseases is applicable to the design, implementation, and evaluation of health policies related to this group of diseases and the distribution of economic, human, and technological resources for their prevention, diagnosis, treatment, and control, among others. Its applications include, for example, the identification of populations requiring interventions to promote healthy lifestyles or specific socioeconomic measures to reduce health inequities. It is also used to estimate the infrastructure and number of specialized health personnel required to address this group of pathologies.
This indicator reflects a population’s lifestyles, socioeconomic development, and health status, and is also related to population aging and rapid, poorly planned urbanization. Its analysis makes it possible to identify populations with greater risk factors for dying from noncommunicable diseases and encourage research in this area.
The proportion of deaths from noncommunicable diseases is applicable to the design, implementation, and evaluation of health policies related to this group of diseases and the distribution of economic, human, and technological resources for their prevention, diagnosis, treatment, and control, among others. Its applications include, for example, the identification of populations requiring interventions to promote healthy lifestyles or specific socioeconomic measures to reduce health inequities. It is also used to estimate the infrastructure and number of specialized health personnel required to address this group of pathologies.
Estimation method
Data for the percentage of deaths from noncommunicable diseases are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and interagency groups, the Global Burden of Disease, and other scientific studies. The number of deaths from noncommunicable diseases is used as the numerator.
The underlying causes of death for this indicator correspond to International Classification of Diseases, Tenth Revision (ICD-10) codes C00 - C97, D00 - D48, D55 - D64 (except D64.9), D65 - D89, E03 - E07, E10 - E34, E65 - E88, F01 - F99, G06 - G98 (except G14), H00 - H61, H68 - H93, I00 - I99, J30 - J98, K00 - K92, L00 - L98, M00 - M99, N00 - N64, N75 - N98, Q00 - Q99, X41 - X42, X44 - X45, R95.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, cause of death estimates are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000–2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The underlying causes of death for this indicator correspond to International Classification of Diseases, Tenth Revision (ICD-10) codes C00 - C97, D00 - D48, D55 - D64 (except D64.9), D65 - D89, E03 - E07, E10 - E34, E65 - E88, F01 - F99, G06 - G98 (except G14), H00 - H61, H68 - H93, I00 - I99, J30 - J98, K00 - K92, L00 - L98, M00 - M99, N00 - N64, N75 - N98, Q00 - Q99, X41 - X42, X44 - X45, R95.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, cause of death estimates are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000–2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
Interpretation - example
According to 2019 data, 52.4% of deaths in country A were from noncommunicable diseases; that is, for every 100 people who died in this country in 2019, noncommunicable diseases were responsible for the death of 52 of them.
Desagregation
By sex
Limitations
The estimated value of this indicator may differ from each country’s calculations due to differences such as the method used to prepare population projections and estimates, the use of a different group of ICD-10 codes, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
The calculation of the proportion of deaths from noncommunicable diseases requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in the system in a timely manner and certification of cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The calculation of the proportion of deaths from noncommunicable diseases requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in the system in a timely manner and certification of cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Deaths from pertussis in children under age 5
Domain
Mortality
Subdomain
Child health
Definition
Total number of pertussis deaths registered in children under 5 in a specific country, territory, or geographic area in a given calendar year.
Measurement Unit
Deaths
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
The number of deaths from pertussis in children under 5 quantifies the importance of an immunopreventable disease on child mortality and highlights it as a public health problem for a given population or geographic area. It supports public policy-making aimed at reducing mortality in childhood.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and populations with higher specific risk factors for dying from pertussis. It is directly related to coverage of the pertussis vaccination program.
This indicator is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of pertussis in children and to the distribution of economic, human, and technological resources for this disease, among others. Its main applications include promoting research related to this disease, planning and evaluating pertussis vaccination programs, and prioritizing access to quality health care in children.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and populations with higher specific risk factors for dying from pertussis. It is directly related to coverage of the pertussis vaccination program.
This indicator is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of pertussis in children and to the distribution of economic, human, and technological resources for this disease, among others. Its main applications include promoting research related to this disease, planning and evaluating pertussis vaccination programs, and prioritizing access to quality health care in children.
Estimation method
The number of deaths from pertussis (ICD-10 code: A37) is obtained from deaths of children under 5 in a specific country, territory, or geographic area, reported by the countries to PAHO.
Interpretation - example
In 2019, there were 24 deaths from pertussis among children under 5 in country A.
Desagregation
No disaggregation
Limitations
The number of deaths from pertussis in children under 5 quantifies the impact of this disease on mortality in this age group. This should not be interpreted as the risk of dying, which is estimated through the pertussis mortality rate.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths from pertussis in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths from pertussis in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Deaths in children under age 5 due to diphtheria
Domain
Mortality
Subdomain
Child health
Definition
Total number of deaths registered due to diphtheria in children under 5 in a given country, territory, or geographic area in a specific calendar year.
Measurement Unit
Deaths
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
The number of deaths due to diphtheria in children under 5 quantifies the importance of this disease as a public health problem for a given population or geographic area. This indicator supports public policy-making aimed at reducing mortality in this age group.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and populations with higher risk factors for dying from diphtheria. It is directly related to the coverage of the diphtheria vaccination program.
This indicator is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of diphtheria in children and to the distribution of economic, human, and technological resources for this disease, among other things. Its main applications include planning and evaluation of diphtheria vaccination programs, access to early antimicrobial therapy, and prioritization of the quality of health care in children.
This indicator reflects the health status, living conditions, and health and socio-economic development of a population, specifically children. It helps identify health inequities and populations with higher risk factors for dying from diphtheria. It is directly related to the coverage of the diphtheria vaccination program.
This indicator is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of diphtheria in children and to the distribution of economic, human, and technological resources for this disease, among other things. Its main applications include planning and evaluation of diphtheria vaccination programs, access to early antimicrobial therapy, and prioritization of the quality of health care in children.
Estimation method
The number of deaths due to diphtheria (ICD-10 code: A36) is from deaths of children under 5 in a given country, territory, or geographic area, reported by the countries to PAHO.
Interpretation - example
In 2019, there were 24 deaths due to diphtheria among children under 5 in country A.
Desagregation
No disaggregation
Limitations
The number of deaths due to diphtheria in children under 5 quantifies the impact of this disease on mortality in this age group. This should not be interpreted as the risk of dying, which is estimated through the diphtheria mortality rate.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths due to diphtheria in children under 5 requires a civil registry system with good coverage. Births and deaths of child populations must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
This indicator’s value may differ from each country’s calculations due to methodological considerations, such as the application of algorithms to correct underreporting of deaths and births, and methods for redistributing ill-defined causes, among others.
Estimating the number of deaths due to diphtheria in children under 5 requires a civil registry system with good coverage. Births and deaths of child populations must be recorded in a timely manner in this system, and medical certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Demand for family planning satisfied by any modern method (%)
Domain
Health service coverage
Subdomain
Maternal and reproductive health
Definition
Percentage of sexually active women of reproductive age (15 to 49 years) who want no (more) children or want to postpone the next pregnancy and who are currently using a modern contraceptive method for a given country, territory or geographical area in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
The indicator is useful for assessing overall levels of coverage of family planning programs and services. Access to and use of an effective means of contraception helps enable women and their partners to exercise their rights to decide freely and responsibly the number and spacing of their children and to have the information and means to do so. Meeting demand for family planning with modern methods contributes to maternal and child health by preventing unintended pregnancies and closely spaced pregnancies, which are at higher risk for poor obstetrical outcomes. Satisfying family planning needs is also one of the most cost-effective investments to alleviate poverty and improve health.
Estimation method
Prevalence of modern contraceptive use is estimated by the United Nations Population Division, based on data from internationally coordinated national household surveys, such as the Demographic and Health Surveys (DHS), Reproductive Health Surveys (HRH), Multiple Indicator Cluster Surveys (MICS), Gender and Generation Surveys (GGS), and other nationally sponsored surveys.
The numerator in the standard definition of unmet need for family planning includes women who are fertile and sexually active, and who report that they do not want to have (more) children or who report that they want to delay the birth of their next child for at least two years or more, or who are undecided about the time of the next birth but are using a contraceptive method.
The denominator is the total number of women aged 15 to 49 who express the need for family planning, either because they are using a contraceptive method or because they have an unsatisfied need for family planning.
Modern contraceptive methods include female and male sterilization, intrauterine devices (IUD), implants, injectable methods, oral contraceptive pills, male and female condoms, vaginal barrier methods (including diaphragm, cervical cap, and spermicidal foam, jelly, cream, and sponge), lactation amenorrhea method, emergency contraception, and other modern methods that are not reported separately (e.g., contraceptive patch or vaginal ring).
For more details on methodology, see:
United Nations, Department of Economic and Social Affairs, Population Division (2021). World Contraceptive Use 2021. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
The numerator in the standard definition of unmet need for family planning includes women who are fertile and sexually active, and who report that they do not want to have (more) children or who report that they want to delay the birth of their next child for at least two years or more, or who are undecided about the time of the next birth but are using a contraceptive method.
The denominator is the total number of women aged 15 to 49 who express the need for family planning, either because they are using a contraceptive method or because they have an unsatisfied need for family planning.
Modern contraceptive methods include female and male sterilization, intrauterine devices (IUD), implants, injectable methods, oral contraceptive pills, male and female condoms, vaginal barrier methods (including diaphragm, cervical cap, and spermicidal foam, jelly, cream, and sponge), lactation amenorrhea method, emergency contraception, and other modern methods that are not reported separately (e.g., contraceptive patch or vaginal ring).
For more details on methodology, see:
United Nations, Department of Economic and Social Affairs, Population Division (2021). World Contraceptive Use 2021. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
Interpretation - example
According to 2019 data, met demand for family planning in country A is 52%, meaning that 52 of every 100 fertile and sexually active women who want to prevent or delay motherhood can do so by using any modern contraceptive method.
Desagregation
By marital status: Married/ in union, unmarried
Limitations
International comparability of this indicator is affected by aspects of the design and implementation of the survey from which the data are obtained. Factors to be considered include the individual characteristics of the base population (age, sex, marital status, etc.) and the time frame used to assess met demand for family planning.
Data source(s)
United Nations, Population Division. Available from: https://www.un.org/development/desa/pd/data/sdg-indicator-371-contraceptive-use
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.7.1. Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods. [Proporción de mujeres en edad reproductiva (de 15 a 49 años de edad) cuyas necesidades de planificación familiar se satisfacen con métodos modernos]
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.a Proportion of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Sustainable Development Goals (SDG).
Indicator 3.7.1. Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methods. [Proporción de mujeres en edad reproductiva (de 15 a 49 años de edad) cuyas necesidades de planificación familiar se satisfacen con métodos modernos]
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2.a Proportion of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations. Population Division. World Contraceptive Use. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
United Nations. Sustainable Development Goals (SDG). E-Learning tool for SDG Indicator 3.7.1. Available from:
https://www.un.org/development/desa/pd/file/10712
Pan American Health Organization. PAHO Strategic Plan 2020-2025 “Equity at the Heart of Health” – Compendium of Outcome Indicators. Available from:
https://www.paho.org/en/documents/paho-strategic-plan-2020-2025-equity-heart-health-compendium-outcome-indicators
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations. Population Division. World Contraceptive Use. Available from:
https://www.un.org/development/desa/pd/data/world-contraceptive-use
United Nations. Sustainable Development Goals (SDG). E-Learning tool for SDG Indicator 3.7.1. Available from:
https://www.un.org/development/desa/pd/file/10712
Pan American Health Organization. PAHO Strategic Plan 2020-2025 “Equity at the Heart of Health” – Compendium of Outcome Indicators. Available from:
https://www.paho.org/en/documents/paho-strategic-plan-2020-2025-equity-heart-health-compendium-outcome-indicators
Dengue cases
Domain
Morbidity
Subdomain
Communicable Diseases
Definition
Number of new suspected, probable, and laboratory-confirmed cases of dengue and severe dengue recorded as of 31 December of a specific year in a given country, territory, or geographical area.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Dengue is a viral disease transmitted mainly by the female Aedes aegypti mosquito. This species is also a vector for the chikungunya, yellow fever (urban), and Zika viruses. Aedes albopictus is a secondary vector found in the Region. Dengue has high morbidity, especially in children, and has an enormous social and economic impact. There is currently no specific treatment for this disease, so much depends on the effectiveness of preventive strategies.
This indicator makes it possible to monitor countries that are planning and implementing strategies to reduce and control Aedes aegypti populations and determine the sustainability of such measures. It helps identify populations in need of strengthened dengue surveillance programs, diagnostic capacity, and health care to address the disease.
PAHO's Strategy for Arboviral Disease Prevention and Control monitors this indicator.
It encourages research and development of new resources for dengue prevention and control. It is also used to promote intersectoral collaboration at the national and international levels and allocate human, technological, and economic resources to fight the disease.
This indicator makes it possible to monitor countries that are planning and implementing strategies to reduce and control Aedes aegypti populations and determine the sustainability of such measures. It helps identify populations in need of strengthened dengue surveillance programs, diagnostic capacity, and health care to address the disease.
PAHO's Strategy for Arboviral Disease Prevention and Control monitors this indicator.
It encourages research and development of new resources for dengue prevention and control. It is also used to promote intersectoral collaboration at the national and international levels and allocate human, technological, and economic resources to fight the disease.
Estimation method
Data are primarily collected from national surveillance systems that regularly report to the Pan American Health Organization.
Interpretation - example
In country A, there were 14 cases of dengue in 2019.
Desagregation
By sex
Limitations
This indicator depends on the effectiveness and coverage of dengue surveillance systems, as well as diagnostic suspicion and timely case reporting.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Fifty-fifth World Health Assembly Dengue fever and dengue haemorrhagic fever prevention and control. Resolution WHA55.17. May 2002. Available from: https://apps.who.int/iris/bitstream/handle/10665/78534/ewha5517.pdf
World Health Organization (WHO). Dengue and severe dengue. Available from: https://www.who.int/health-topics/dengue-and-severe-dengue#tab=tab_1
World Health Organization (WHO). Fifty-fifth World Health Assembly Dengue fever and dengue haemorrhagic fever prevention and control. Resolution WHA55.17. May 2002. Available from: https://apps.who.int/iris/bitstream/handle/10665/78534/ewha5517.pdf
World Health Organization (WHO). Dengue and severe dengue. Available from: https://www.who.int/health-topics/dengue-and-severe-dengue#tab=tab_1
Dentists (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Dentist density is the number of practising dentists in health facilities as of 31 December in a specific year per 10 000 population, in a given country, territory or geographic area.
The International Standard Classification of Occupations code for this category is 2261 (2008 revision).
The International Standard Classification of Occupations code for this category is 2261 (2008 revision).
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Availability and access to dentists are key elements in improving the oral health of a population. This indicator makes it possible to identify inequalities in the distribution of health resources and prioritize human and economic resource allocation to specific populations. Its value is used to develop public policies increasing funding for oral health, and to train, hire, and retrain skilled workers in this area.
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of dentists reported by the countries of the Americas to the Pan American Health Organization (PAHO). This data is collected from secondary sources, such as health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Interpretation - example
According to 2019 data, the density of dentists in country A was 1.5 per 10 000 population. This means that, practising dentists in health facilities, this country had 1.5 dentists per 10 000 population in 2019.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data used for calculation. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. This professional typically works in the private sector, however, most of the time the data that is reported to the Pan American Health Organization comes from the public sector. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working dentists or include all registered or licensed to practice professionally, even when their employment status is unknown.
The value of this indicator may differ from figures calculated by each country due to differences such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working dentists or include all registered or licensed to practice professionally, even when their employment status is unknown.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Dependency ratio (100 pop)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Average number of economically dependent people per 100 economically productive people in a given country, territory, or geographical area, at a specific point in time.
Measurement Unit
100 000 population
Type of measurement
Ratio
Type of statistics
Corrected/Predicted
Purpose
This indicator reflects the inactive population segment, which must be sustained by the potentially productive portion. As a result, the indicator is related to other economic indicators and can be directly applied to develop budgetary, employment, and social security policies.
The higher the dependency rate, the greater the burden for the potentially productive population to sustain the potentially inactive population. This means that the working-age population must support a large proportion of dependents.
The indicator can also be applied to monitor the aging process of a country, territory, or geographical area.
The higher the dependency rate, the greater the burden for the potentially productive population to sustain the potentially inactive population. This means that the working-age population must support a large proportion of dependents.
The indicator can also be applied to monitor the aging process of a country, territory, or geographical area.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
An economically "dependent" population is defined as the sum of the population aged 0 to 14 plus the population aged 65 and over in a given country, territory, or geographical area, at a specific point in time, usually at mid-year (July 1). The population of economically "active or productive" age is the population aged 15 to 64 in the same country, territory, or geographical area, at the same specific point in time.
Formula:
(A/B) x 100
Numerator (A):
Population under 15 years and over 65 years in a given country, territory, or geographical area in year z.
Denominator (B):
Population aged 15 to 64 in a given country, territory, or geographical area in year z.
An economically "dependent" population is defined as the sum of the population aged 0 to 14 plus the population aged 65 and over in a given country, territory, or geographical area, at a specific point in time, usually at mid-year (July 1). The population of economically "active or productive" age is the population aged 15 to 64 in the same country, territory, or geographical area, at the same specific point in time.
Formula:
(A/B) x 100
Numerator (A):
Population under 15 years and over 65 years in a given country, territory, or geographical area in year z.
Denominator (B):
Population aged 15 to 64 in a given country, territory, or geographical area in year z.
Interpretation - example
Number of "dependent" people per 100 economically active people within a given population.
In 2015, the dependency ratio of country A was 43.8. This means in 2015, there were 43.8 dependent people for every 100 economically active people.
In 2015, the dependency ratio of country A was 43.8. This means in 2015, there were 43.8 dependent people for every 100 economically active people.
Desagregation
No disaggregation
Limitations
The international quality and comparability of this indicator may be limited by factors such as inaccuracy in age reporting in censuses and demographic surveys or the methodology used to develop population estimates and projections.
In reality, these are potentially inactive and potentially active populations, because not all people aged 0 to 14 or those aged 65 and over are out of the labor market, nor are all those aged 15 to 64 working.
Because the dependency ratio is affected by factors influencing the labor market, such as including young people and older adults or excluding people of working age, this indicator should be analyzed alongside other economic parameters.
In reality, these are potentially inactive and potentially active populations, because not all people aged 0 to 14 or those aged 65 and over are out of the labor market, nor are all those aged 15 to 64 working.
Because the dependency ratio is affected by factors influencing the labor market, such as including young people and older adults or excluding people of working age, this indicator should be analyzed alongside other economic parameters.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from:
https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from: https://www.prb.org/population-handbook/
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from:
https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from: https://www.prb.org/population-handbook/
Diabetes mellitus mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from diabetes mellitus in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator is applicable to the design, implementation, and evaluation of health policies related to diabetes mellitus and the distribution of economic, human, and technological resources for the prevention, diagnosis, and control of this disease, among others. Its applications include analysis of the territorial distribution of mortality from diabetes mellitus and evaluation over time of the effectiveness of interventions designed to prevent the disease.
The age-adjusted diabetes mellitus mortality rate allows for the comparison of this indicator across populations or in the same population over time.
The age-adjusted diabetes mellitus mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses diabetes mellitus deaths from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as: national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes E10 – E14 (except E10.2, E11.2, E12.2, E13.2, E14.2) of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator for the age-adjusted diabetes mellitus mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The diabetes mellitus mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes E10 – E14 (except E10.2, E11.2, E12.2, E13.2, E14.2) of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator for the age-adjusted diabetes mellitus mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The diabetes mellitus mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted diabetes mellitus mortality rate for 2019 was 58 per 100 000 population in country A and 15 per 100 000 population in country B; that is, in 2019 diabetes mellitus was responsible for the death of 58 people per 100 000 population of country A, compared to country B, where 15 people died from that cause per 100 000 population. This means that, in 2019, the risk of dying from diabetes mellitus was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted diabetes mellitus mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted diabetes mellitus mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the diabetes mellitus mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted diabetes mellitus mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the diabetes mellitus mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Diphtheria cases in children under age 5
Domain
Morbidity
Subdomain
Child health
Definition
Number of confirmed cases of diphtheria in children under 5 years of age in a given country, territory, or geographical area, in a specific calendar year. Includes laboratory or clinically confirmed or epidemiologically linked cases.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Diphtheria is a communicable disease with epidemic potential that can lead to death. Vaccination is one of the fundamental pillars of control. The World Health Organization (WHO) has established that all children worldwide should be vaccinated against diphtheria.
The number of confirmed cases of diphtheria in children under 5 years of age reflects the impact this disease has on children, despite being an immunopreventable disease. This indicator makes it possible to identify populations with inadequate immunization program coverage. It is applied when designing, implementing, and assessing health policies to prevent, treat, and control diphtheria in children and distribute economic, human, and technological resources to fight the disease, among other purposes. Among its main applications are planning and assessing vaccination programs against diphtheria, providing access to early antimicrobial therapy, and prioritizing quality health care in children.
It quantifies the impact of diphtheria as a public health problem in a given population or geographical area and supports decision making for public policies aimed at reducing morbidity and mortality in children under 5 years of age. Analysis of the disease's temporal and geographical trends can be performed.
This indicator reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and populations with greater risk factors for transmitting Corynebacterium diphtheriae.
The number of confirmed cases of diphtheria in children under 5 years of age reflects the impact this disease has on children, despite being an immunopreventable disease. This indicator makes it possible to identify populations with inadequate immunization program coverage. It is applied when designing, implementing, and assessing health policies to prevent, treat, and control diphtheria in children and distribute economic, human, and technological resources to fight the disease, among other purposes. Among its main applications are planning and assessing vaccination programs against diphtheria, providing access to early antimicrobial therapy, and prioritizing quality health care in children.
It quantifies the impact of diphtheria as a public health problem in a given population or geographical area and supports decision making for public policies aimed at reducing morbidity and mortality in children under 5 years of age. Analysis of the disease's temporal and geographical trends can be performed.
This indicator reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and populations with greater risk factors for transmitting Corynebacterium diphtheriae.
Estimation method
The number of confirmed cases of diphtheria (ICD-10 code: A36) in children under 5 years of age is obtained from data provided by the countries of the Region of the Americas to the Pan American Health Organization (PAHO), according to information reported by national disease surveillance and control systems.
Interpretation - example
In 2019, there were 64 confirmed cases of diphtheria among children under 5 years of age residing in country A.
Desagregation
No disaggregation
Limitations
Estimates of diphtheria cases in children under 5 years of age are affected by factors such as the effectiveness of national diphtheria surveillance systems, diagnostic suspicion, and underreporting of cases.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.
Available from: https://sdgs.un.org/
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Diphtheria vaccine: WHO position paper. No. 31, August 2017, 92, 417–436. Available from: https://apps.who.int/iris/handle/10665/258683
Indicadores básicos para a saúde no Brasil: conceitos e aplicações, 2ª edição [Core health indicators in Brazil: concepts and applications]. Pan American Health Organization. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Diphtheria vaccine: WHO position paper. No. 31, August 2017, 92, 417–436. Available from: https://apps.who.int/iris/handle/10665/258683
Indicadores básicos para a saúde no Brasil: conceitos e aplicações, 2ª edição [Core health indicators in Brazil: concepts and applications]. Pan American Health Organization. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Estimated infant mortality rate (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
The infant mortality rate is the probability that a child born in a given country, territory, or geographic area during a specific calendar year will die before the age of 1, if subject to the age-specific mortality rates for that period.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Regarding the use of estimated (corrected) data: Since available country data vary in terms of source, definitions, and methods, a methodology was developed to produce country estimates that are representative for each country and time series and are comparable across countries.
This indicator shows the magnitude of infant deaths as a public health problem for a given population or geographic area and measures infant survival.
This value reflects the risk of infants born alive dying during their first year of life. It makes it possible to identify health inequities and populations with specific risk factors.
The infant mortality rate reflects children’s health status and social, economic, and environmental conditions. It is related to maternal and child health care access, quality, and timeliness. Its applications include strengthening professional childbirth care, breastfeeding, and vaccination programs.
This indicator is useful for designing, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health. Its applications include evaluating time and geographic trends in infant mortality.
This indicator shows the magnitude of infant deaths as a public health problem for a given population or geographic area and measures infant survival.
This value reflects the risk of infants born alive dying during their first year of life. It makes it possible to identify health inequities and populations with specific risk factors.
The infant mortality rate reflects children’s health status and social, economic, and environmental conditions. It is related to maternal and child health care access, quality, and timeliness. Its applications include strengthening professional childbirth care, breastfeeding, and vaccination programs.
This indicator is useful for designing, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health. Its applications include evaluating time and geographic trends in infant mortality.
Estimation method
The mortality rate uses deaths of children aged 0 to 1, estimated and corrected by the Inter-agency Group for Child Mortality Estimation (IGME), for a given country or territory during a specific year as the numerator, and the total number of live births for the same population and year as the denominator.
According to the data source, the most commonly used sources for estimating the infant mortality rate are:
Civil registry: The number of deaths at age 0 and the population of that age are used to calculate the mortality rate, which is then converted into the age-specific probability of dying.
Censuses and surveys: Indirect method based on asking each woman of reproductive age how many children she has given birth to and how many are still alive. The Brass method and model life tables are then used to provide an estimate of infant mortality.
Surveys: Direct method based on birth history: a series of detailed questions about each child a woman has given birth to over her lifetime. To reduce sampling errors, estimates are generally presented as period rates, for the five or ten years leading up to the survey.
The IGME produces an infant mortality rate (IMR) trend using a standardized methodology by country groups based on the type and quality of available data. For developed countries where the civil registry has full coverage, the IMR is calculated directly from civil registry data if the data for the year to be estimated are available. For countries with an adequate trend of civil registry data, the age pattern between infant mortality and under-5 mortality from the most recent data is used as a standard for the Modified Logit Life Table developed by WHO, to convert the projected under-5 mortality rate applying a weighted regression to a projected infant mortality rate.
For countries with survey-derived data, since infant mortality rates from survey-derived birth histories are exposed to memory bias, infant mortality is obtained from projected under-5 mortality rates converted to infant mortality rates using the Coale-Demeney model life tables.
The Inter-agency Group for Child Mortality Estimation, which includes representatives from UNICEF, WHO, the World Bank, and the United Nations Population Division, is actively working to produce standardized joint estimates.
According to the data source, the most commonly used sources for estimating the infant mortality rate are:
Civil registry: The number of deaths at age 0 and the population of that age are used to calculate the mortality rate, which is then converted into the age-specific probability of dying.
Censuses and surveys: Indirect method based on asking each woman of reproductive age how many children she has given birth to and how many are still alive. The Brass method and model life tables are then used to provide an estimate of infant mortality.
Surveys: Direct method based on birth history: a series of detailed questions about each child a woman has given birth to over her lifetime. To reduce sampling errors, estimates are generally presented as period rates, for the five or ten years leading up to the survey.
The IGME produces an infant mortality rate (IMR) trend using a standardized methodology by country groups based on the type and quality of available data. For developed countries where the civil registry has full coverage, the IMR is calculated directly from civil registry data if the data for the year to be estimated are available. For countries with an adequate trend of civil registry data, the age pattern between infant mortality and under-5 mortality from the most recent data is used as a standard for the Modified Logit Life Table developed by WHO, to convert the projected under-5 mortality rate applying a weighted regression to a projected infant mortality rate.
For countries with survey-derived data, since infant mortality rates from survey-derived birth histories are exposed to memory bias, infant mortality is obtained from projected under-5 mortality rates converted to infant mortality rates using the Coale-Demeney model life tables.
The Inter-agency Group for Child Mortality Estimation, which includes representatives from UNICEF, WHO, the World Bank, and the United Nations Population Division, is actively working to produce standardized joint estimates.
Interpretation - example
The estimated infant mortality rate of country A for 2019 was 8.3 per 1 000 live births; that is, in that year eight children died before the age of 1, per 1 000 live births in country A.
Desagregation
No disaggregation
Limitations
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths, statistical methods, and assumptions applied.
Data source(s)
United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Available from: https://childmortality.org/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Estimated maternal mortality ratio (100 000 lb)
Domain
Mortality
Subdomain
Maternal and reproductive health
Definition
The ratio between the number of maternal deaths and the number of live births in a given country, territory, or geographical area in a specific calendar year. Expressed per 100 000 live births.
Maternal deaths: The annual number of female deaths from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, expressed per 100,000 live births, for a specified time period.
Maternal deaths: The annual number of female deaths from any cause related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) during pregnancy and childbirth or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, expressed per 100,000 live births, for a specified time period.
Measurement Unit
100 000 live births
Type of measurement
Ratio
Type of statistics
Corrected
Purpose
On the use of estimated (corrected) data: Since the available country data vary in terms of source, definition, and method, a methodology was developed to arrive at country estimates with time series that are country representative and comparable across countries.
The ratio depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth.
In general, the maternal mortality ratio reflects the magnitude of maternal mortality, increases its visibility as a serious public health problem, and contributes to raising societal awareness about the issue. It reflects a population's state of health, as well as its socioeconomic and health development. It allows inequities in health and higher-risk populations to be identified, and highlights the need to focus economic, human, and technological resources to respond appropriately to deficiencies detected.
Its value reflects the risk that a woman has of dying from complications related to pregnancy, childbirth, or the postpartum period, and encourages research in maternal and child issues. Among its applications is the evaluation of geographical and temporal trends in maternal mortality.
This indicator is associated with the quality of women's health care, including family planning, antenatal care, attendance at childbirth, and the postpartum period. It identifies the need for specialized health personnel to prevent and treat complications that occur during pregnancy and childbirth.
The ratio depicts the risk of maternal death relative to the number of live births and essentially captures the risk of death in a single pregnancy or a single live birth.
In general, the maternal mortality ratio reflects the magnitude of maternal mortality, increases its visibility as a serious public health problem, and contributes to raising societal awareness about the issue. It reflects a population's state of health, as well as its socioeconomic and health development. It allows inequities in health and higher-risk populations to be identified, and highlights the need to focus economic, human, and technological resources to respond appropriately to deficiencies detected.
Its value reflects the risk that a woman has of dying from complications related to pregnancy, childbirth, or the postpartum period, and encourages research in maternal and child issues. Among its applications is the evaluation of geographical and temporal trends in maternal mortality.
This indicator is associated with the quality of women's health care, including family planning, antenatal care, attendance at childbirth, and the postpartum period. It identifies the need for specialized health personnel to prevent and treat complications that occur during pregnancy and childbirth.
Estimation method
Data on maternal mortality and other important variables are obtained through databases maintained by the Maternal Mortality Estimation Inter-agency Group (MMEIG), made up of the World Health Organization (WHO), the United Nations Development Programme (UNDP), the United Nations Children's Fund (UNICEF), and the World Bank Group.
MMEIG has developed a method to adjust existing data to consider data quality issues and improve the comparability of different data sources and countries. This method involves assessing underreporting and, where necessary, adjusts for insufficiency and poor classification of deaths while developing estimates through statistical models for countries without reliable data at the national level.
The estimation methods used by MMEIG consider adjustments to correct problems arising from underreporting and misclassification of maternal deaths and to ensure comparability between different data sources and countries.
MMEIG has developed a method to adjust existing data to consider data quality issues and improve the comparability of different data sources and countries. This method involves assessing underreporting and, where necessary, adjusts for insufficiency and poor classification of deaths while developing estimates through statistical models for countries without reliable data at the national level.
The estimation methods used by MMEIG consider adjustments to correct problems arising from underreporting and misclassification of maternal deaths and to ensure comparability between different data sources and countries.
Interpretation - example
The maternal mortality ratio in the Americas is 52 maternal deaths per 100 000 live births. The maternal mortality ratio of country A is 350 per 100 000 live births. This means that in country A the risk of dying due to complications occurring during pregnancy, childbirth, or the postpartum period is almost seven times greater than the regional average in the Americas.
Desagregation
No disaggregation
Limitations
Specifically, regarding point estimates calculated with the MMEIG methodology, data may be affected by the availability of the underlying data from each country, the statistical model (including adjustment for known biases), the number of deaths in women of childbearing age, HIV, and inputs of the model, among other factors. Estimates should be considered alongside the reported margins of uncertainty within which the actual levels are known to exist.
This indicator’s value is affected by the frequency with which country data is updated, as well as transfer to the Maternal Mortality Estimation Inter-agency Group (MMEIG) for calculation.
This indicator’s value is affected by the frequency with which country data is updated, as well as transfer to the Maternal Mortality Estimation Inter-agency Group (MMEIG) for calculation.
Data source(s)
Maternal Mortality Estimation Inter-agency Group. WHO, United Nations Children’s Fund (UNICEF), United Nations Population Fund (UNFPA), World Bank Group, and the Population Division of the Department of Economic and Social Affairs of the United Nations (UNPD).
Available from: https://mmr2020.srhr.org/homepage
Available from: https://mmr2020.srhr.org/homepage
Update periodicity PAHO
Every 3-5 years
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 3.1.1: Maternal mortality ratio
Target 3.1 of the SDG: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 6. Maternal mortality ratio (MMR) (deaths per 100,000 live births)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.1.1: Maternal mortality ratio
Target 3.1 of the SDG: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 6. Maternal mortality ratio (MMR) (deaths per 100,000 live births)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Estimates by WHO, UNICEF, UNFPA, World Bank Group and the UNDESA/ Population Division. Trends in maternal mortality 2000 to 2020. Geneva: World Health Organization; 2023.
Available from: https://www.who.int/publications/i/item/9789240068759
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
Available from: https://www.who.int/publications/i/item/9789240068759
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
Estimated neonatal mortality rate (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
The estimated neonatal mortality rate is the probability that a child born alive will die before the age of 28 days, in a given country, territory, or geographic area during a specific calendar year. Expressed per 1 000 births.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Regarding the use of estimated (corrected) data: Since available country data vary in terms of source, definitions, and methods, a methodology was developed to produce estimates that are representative for each country and time series and are comparable across countries.
This indicator quantifies the scope of neonatal mortality as a public health problem for a given population or geographic area. It measures the risk of children born alive dying before the age of 28 days.
The estimated neonatal mortality rate reflects health status and the social, economic, and environmental conditions in which a population lives, particularly the infant population. It makes it possible to identify health inequities and populations with specific risk factors and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health, particularly prenatal and neonatal care. Its result helps strengthen professional care for childbirth, breastfeeding, and the Expanded Program on Immunization.
It allows for analysis of the geographic and time trend of neonatal mortality in a given population.
This indicator quantifies the scope of neonatal mortality as a public health problem for a given population or geographic area. It measures the risk of children born alive dying before the age of 28 days.
The estimated neonatal mortality rate reflects health status and the social, economic, and environmental conditions in which a population lives, particularly the infant population. It makes it possible to identify health inequities and populations with specific risk factors and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health, particularly prenatal and neonatal care. Its result helps strengthen professional care for childbirth, breastfeeding, and the Expanded Program on Immunization.
It allows for analysis of the geographic and time trend of neonatal mortality in a given population.
Estimation method
The neonatal mortality rate is obtained from the deaths before the age of 28 days of children who were born alive, estimated and corrected by the Inter-agency Group for Child Mortality Estimation.
This indicator is estimated based on data from three sources:
Civil registry: The number of live births and the number of neonatal deaths are used to calculate age-specific rates. This system provides annual data.
Household surveys: Calculations are based on complete birth history, so women are asked the date of birth of each of their children, whether the child is still alive and, if not, the child’s age at the time of death.
To ensure consistency with under-5 mortality rates (U5MR) developed for the United Nations by the Inter-agency Group for Child Mortality Estimation (IGME) and to take into account variation in measurement errors from survey to survey, country data points for U5MR and the neonatal mortality rate (NMR) were rescaled for all years to match the IGME’s latest U5MR time series estimates. This change assumes that the proportional measurement error in NMR and U5MR is equal for each data point. The following multilevel statistical model was then applied to estimate neonatal mortality rates log (NMR / 1000) = α0 + β1 * log (U5MR / 1000) + β2* ([log (U5MR / 1000)]^2) with random effects parameters for both level and trend regression parameters, and random effects parameters influenced by the country itself.
For countries with high-quality civil registry data on neonatal deaths, defined as (i) 100% complete for adults and only civil registry data are used for infant mortality; (ii) population over 800 000; and (iii) with at least three civil registry data points for all subsequent calendar windows 1990–1994, 1995–1999, 2000–2004, 2005 onwards, the same basic equation is used, but with random effects parameters for both level and trend regression parameters and random effects parameters influenced by the country itself.
This indicator is estimated based on data from three sources:
Civil registry: The number of live births and the number of neonatal deaths are used to calculate age-specific rates. This system provides annual data.
Household surveys: Calculations are based on complete birth history, so women are asked the date of birth of each of their children, whether the child is still alive and, if not, the child’s age at the time of death.
To ensure consistency with under-5 mortality rates (U5MR) developed for the United Nations by the Inter-agency Group for Child Mortality Estimation (IGME) and to take into account variation in measurement errors from survey to survey, country data points for U5MR and the neonatal mortality rate (NMR) were rescaled for all years to match the IGME’s latest U5MR time series estimates. This change assumes that the proportional measurement error in NMR and U5MR is equal for each data point. The following multilevel statistical model was then applied to estimate neonatal mortality rates log (NMR / 1000) = α0 + β1 * log (U5MR / 1000) + β2* ([log (U5MR / 1000)]^2) with random effects parameters for both level and trend regression parameters, and random effects parameters influenced by the country itself.
For countries with high-quality civil registry data on neonatal deaths, defined as (i) 100% complete for adults and only civil registry data are used for infant mortality; (ii) population over 800 000; and (iii) with at least three civil registry data points for all subsequent calendar windows 1990–1994, 1995–1999, 2000–2004, 2005 onwards, the same basic equation is used, but with random effects parameters for both level and trend regression parameters and random effects parameters influenced by the country itself.
Interpretation - example
Country A’s neonatal mortality rate for 2019 was 27.3 per 1 000 live births; that is, in that year 27 children born alive died before the age of 28 days per 1 000 live births.
Desagregation
No disaggregation
Limitations
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths, statistical methods, and assumptions applied.
Data source(s)
United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Available from: https://www.unicef.org/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.2.2 Neonatal mortality rate.
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 3. Neonatal mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.2.2 Neonatal mortality rate.
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 3. Neonatal mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Estimated under-five mortality (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
Under-5 mortality is the probability that a child born in a given country, territory, or geographic area, in a specific calendar year, will die before the age of 5, if subject to the age-specific mortality rates for that period.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
Regarding the use of estimated (corrected) data: Since available country data vary in terms of source, definitions, and methods, a methodology was developed to produce estimates that are representative for each country, have a time series, and are comparable across countries.
This indicator highlights under-5 deaths as a public health problem for a given population or geographic area. This value makes it possible to identify health inequities and populations with specific risk factors.
It becomes relevant because most causes of death in this age group are preventable and treatable through simple, affordable interventions such as immunization, adequate nutrition, and access to safe water and basic services.
Under-5 mortality reflects children’s health status and social, economic, and environmental conditions and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health.
This indicator highlights under-5 deaths as a public health problem for a given population or geographic area. This value makes it possible to identify health inequities and populations with specific risk factors.
It becomes relevant because most causes of death in this age group are preventable and treatable through simple, affordable interventions such as immunization, adequate nutrition, and access to safe water and basic services.
Under-5 mortality reflects children’s health status and social, economic, and environmental conditions and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health.
Estimation method
Under-5 mortality is obtained from deaths estimated by the Inter-agency Group for Child Mortality Estimation.
Interpretation - example
According to data for 2019, the under-5 mortality rate in country A was 8.3 per 1 000 live births; that is, in that year eight children died before age 5 per 1 000 live births in that country.
Desagregation
No disaggregation
Limitations
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths, statistical methods, and the assumptions applied.
Data source(s)
United Nations Inter-agency Group for Child Mortality Estimation (UN IGME). Available from: https://www.unicef.org/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.2.1: Under‐5 mortality rate.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 4. Under-5 mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.2.1: Under‐5 mortality rate.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 4. Under-5 mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Exclusive breastfeeding under 6 months (%)
Domain
Risk factor
Subdomain
Child health
Definition
Percentage of infants from 0 to 5 months of age who are exclusively breastfed, for a given year, in a given country, territory, or geographic area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Breast milk contains all the nutrients, antibodies, hormones, and antioxidants necessary for proper development of the infant; exclusive breastfeeding is the single most effective intervention to boost child survival. It protects against gastrointestinal infections, decreasing the risk of death from diarrheal syndromes. Breastfeeding also strengthens the emotional bond with the mother, benefits the psychomotor, emotional, and sensory development of the child, and stimulates the formation of tissues and cell membranes. Among the benefits for the mother are better postpartum recovery, lower fertility during exclusive breastfeeding, and lower probability of breast and ovarian cancer and osteoporosis in the future.
This indicator is part of the global Monitoring Framework for Maternal, Infant and Young Child Nutrition that assesses countries' progress towards global nutrition targets. It also enables follow-up with the Monitoring Framework for Universal Health in the Americas, to measure national progress on implementing policies aimed at strengthening health systems and achieving universal health.
This indicator is applied when developing public policies to encourage exclusive breastfeeding and establish appropriate conditions. It also contributes to research on childhood morbidity and mortality.
This indicator is part of the global Monitoring Framework for Maternal, Infant and Young Child Nutrition that assesses countries' progress towards global nutrition targets. It also enables follow-up with the Monitoring Framework for Universal Health in the Americas, to measure national progress on implementing policies aimed at strengthening health systems and achieving universal health.
This indicator is applied when developing public policies to encourage exclusive breastfeeding and establish appropriate conditions. It also contributes to research on childhood morbidity and mortality.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO). WHO and UNICEF jointly collect data on infant and young child feeding. Among the sources of information consulted are the WHO Global Data Bank on Infant and Young Child Feeding, national health or nutrition surveys, Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS).
Formula:
(A/B) x 100
Numerator (A):
Number of infants 0 to 5 months of age who received only breast milk the previous day, in a given country and year.
Denominator (B):
Total number of infants from 0 to 5 months of age, for the same country and year.
Formula:
(A/B) x 100
Numerator (A):
Number of infants 0 to 5 months of age who received only breast milk the previous day, in a given country and year.
Denominator (B):
Total number of infants from 0 to 5 months of age, for the same country and year.
Interpretation - example
In country A the percentage of infants under 6 months exclusively breastfed in 2019 was 50%, i.e., of 100 infants from 0 to 5 months of age, half were fed exclusively with breastmilk.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data used for calculation. Surveys are the main source of data. As a result, limiting factors for this indicator include survey coverage and completeness, frequency of updates, and level of understanding of the questions. It should also be kept in mind that calculation requires live births to be timely registered in a system with adequate coverage.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/infants-exclusively-breastfed-for-the-first-six-months-of-life-(-)
Update periodicity PAHO
Every 3-5 years
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14.f Percentage of infants under 6 months of age who are exclusively breastfed
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 14.f Percentage of infants under 6 months of age who are exclusively breastfed
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Indicators for the Global Monitoring Framework on Maternal, Infant and Young Child Nutrition, 2014. Available from: https://cdn.who.int/media/docs/default-source
utritionlibrary/global-targets-2025/indicators_monitoringframework_miycn_background.pdf?sfvrsn=b1934036_6
World Health Organization (WHO). Maternal, infant, and young child nutrition, December 2019. Available from: https://apps.who.int/gb/ebwha/pdf_files/EB146/B146_24-en.pdf
UNICEF Data: Monitoring the situation of children and women. Available from: https://data.unicef.org/
World Health Organization and the United Nations Children's Fund (UNICEF) Indicators for assessing infant and young child feeding practices: definitions and measurement methods. Geneva: 2021. Available from: https://www.who.int/publications/i/item/9789240018389
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Indicators for the Global Monitoring Framework on Maternal, Infant and Young Child Nutrition, 2014. Available from: https://cdn.who.int/media/docs/default-source
utritionlibrary/global-targets-2025/indicators_monitoringframework_miycn_background.pdf?sfvrsn=b1934036_6
World Health Organization (WHO). Maternal, infant, and young child nutrition, December 2019. Available from: https://apps.who.int/gb/ebwha/pdf_files/EB146/B146_24-en.pdf
UNICEF Data: Monitoring the situation of children and women. Available from: https://data.unicef.org/
World Health Organization and the United Nations Children's Fund (UNICEF) Indicators for assessing infant and young child feeding practices: definitions and measurement methods. Geneva: 2021. Available from: https://www.who.int/publications/i/item/9789240018389
External causes mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from external causes in the population in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator is applicable to the design, implementation, and evaluation of health, transportation, and public safety policies related to deaths from external causes and the distribution of economic, human, and technological resources for their prevention, among others.
The age-adjusted external causes mortality rate allows for the comparison of this indicator across populations or in the same population over time.
The age-adjusted external causes mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from external causes from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as: national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes V01 - Y89 (except X41-X42, X44 - X45) of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator for the age-adjusted external causes mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The external causes mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes V01 - Y89 (except X41-X42, X44 - X45) of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator for the age-adjusted external causes mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The external causes mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted external causes mortality rate for 2019 was 29 per 100 000 population in country A and 14 per 100 000 population in country B; that is, in 2019, 29 people died from an external cause per 100 000 population of country A, compared to country B, where 14 people died from the same group of causes per 100 000 population. This means that, in 2019, the risk of dying from external causes was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted external causes mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted external causes mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as: the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Calculating the external causes mortality rate requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality, including reporting the intentionality and type of event resulting in death; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted external causes mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as: the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Calculating the external causes mortality rate requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality, including reporting the intentionality and type of event resulting in death; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Falls mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from falls in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator is applicable to the design, implementation, and evaluation of health, education, and public safety policies related to deaths from falls. Its applications include, for example, helping to promote research on falls, designing safer environments, and the impact of those environments on mortality from falls over time.
The age-adjusted falls mortality rate allows for the comparison of this indicator across populations or in the same population over time.
The age-adjusted falls mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator for this indicator uses deaths from falls from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as: national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes W00 – W19 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted falls mortality rate are estimates by the United Nations Population Division.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The falls mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes W00 – W19 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted falls mortality rate are estimates by the United Nations Population Division.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The falls mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted falls mortality rate for 2019 was 30 per 100 000 population in country A and 14 per 100 000 population in country B; that is, in that year 30 people died from falls per 100 000 population of country A, compared to country B where 14 people died from this cause per 100 000 population. This means that, after removing the effect of differences in the age distribution on the population in the two countries, the risk of dying from falls in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted falls mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted falls mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Calculating the falls mortality rate requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality, which includes reporting how the fall leading to death occurred; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted falls mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death, the method for preparing population estimates and projections, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Calculating the falls mortality rate requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality, which includes reporting how the fall leading to death occurred; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
Fetal mortality rate (1 000 births)
Domain
Mortality
Subdomain
Child health
Definition
The quotient between the sum of stillbirths in a specific calendar year and the number of births (live births plus stillbirths) in the same year, for a given country, territory, or geographic area, expressed per 1 000 births.
A stillbirth corresponds to the death of the product of conception, before his expulsion or his removal from his mother's body, weighing at least 1 000 g. When the weight cannot be obtained, a full 28 weeks of gestation is used as a reference. Fetal death does not include termination of pregnancy by induction.
A stillbirth corresponds to the death of the product of conception, before his expulsion or his removal from his mother's body, weighing at least 1 000 g. When the weight cannot be obtained, a full 28 weeks of gestation is used as a reference. Fetal death does not include termination of pregnancy by induction.
Measurement Unit
Per 1 000 births
Type of measurement
Rate
Type of statistics
Crude
Purpose
The fetal mortality rate reflects the risk of a fetus dying as a result of changes during pregnancy or childbirth. It shows the magnitude of stillbirths as a public health problem for a given population or geographic area. It makes it possible to identify health inequities and populations with specific risk factors.
This indicator is related to health care during pregnancy and childbirth and to a population’s health status and socioeconomic development. Its value is used to strengthen professional childbirth care.
The fetal mortality rate is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health.
This indicator is related to health care during pregnancy and childbirth and to a population’s health status and socioeconomic development. Its value is used to strengthen professional childbirth care.
The fetal mortality rate is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health.
Estimation method
The fetal mortality rate uses stillbirths reported by the national health agency as the numerator and the total number of births (live births plus stillbirths) for the same population and year as the denominator. For international comparison of fetal mortality, stillbirths are limited to a weight of at least 1 000 g or 28 weeks gestation. (ICD-10)
Formula:
(A/B) x 1 000
Numerator (A):
Number of stillbirths in year z in a given country, territory or geographic area.
Denominator (B):
Total number of births (stillbirths + live births) in year z in the same country, territory or geographic area.
Formula:
(A/B) x 1 000
Numerator (A):
Number of stillbirths in year z in a given country, territory or geographic area.
Denominator (B):
Total number of births (stillbirths + live births) in year z in the same country, territory or geographic area.
Interpretation - example
According to 2019 data, the fetal mortality rate in country A was 3.2 per 1 000 births; that is, in that year there were three stillbirths per 1000 births in that country.
Desagregation
No disaggregation
Limitations
Estimating the fetal mortality rate requires a civil registry system with good coverage. Stillbirths and live births must be recorded in a timely manner in this system; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the fetal mortality rate may differ from each country’s value, due to methodological differences such as the definition of stillbirth used or the application of methods to correct underreporting of births and deaths.
The estimated value of the fetal mortality rate may differ from each country’s value, due to methodological differences such as the definition of stillbirth used or the application of methods to correct underreporting of births and deaths.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
PAHO/CLAP. Perinatal Information System (SIP). SipPlus. Available from: http://www.sipplus.org/
WHO, 2016. Making every baby count. Available from: https://www.who.int/docs/default-source/mca-documents/maternal-nb/making-every-baby-count.pdf?Status=Master&sfvrsn=6936f980_2
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2016.pdf
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://score.tools.who.int/fileadmin/uploads/score/Documents/Enable_data_use_for_policy_and_action/100_Core_Health_Indicators
WHO, 2016. Making every baby count. Available from: https://www.who.int/docs/default-source/mca-documents/maternal-nb/making-every-baby-count.pdf?Status=Master&sfvrsn=6936f980_2
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2016.pdf
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://score.tools.who.int/fileadmin/uploads/score/Documents/Enable_data_use_for_policy_and_action/100_Core_Health_Indicators
General mortality rate (age-adjusted per 1 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
Total estimated number of deaths from any cause in the population in a given country or geographic area, during a specific calendar year, divided by the total population, generally estimated in the middle of the same year (July 1), after removing the effect of differences in age distribution. Expressed per 1 000 population.
Measurement Unit
Per 1 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The age-adjusted general mortality rate reflects a population’s health status and socioeconomic and environmental conditions. It makes it possible to identify those at higher risk of dying from any cause and to promote research to identify pathologies, lifestyles, external conditions, or risk factors related to this risk.
The age-adjusted general mortality rate is used to assess changes in the time and geographic trend of mortality and to make comparisons across populations.
Its result is applicable to the design, implementation, and evaluation of public policies in various areas. Its applications include the identification of populations that need to be prioritized in allocating economic, human, and technological resources to increase their life expectancy.
The age-adjusted general mortality rate is used to assess changes in the time and geographic trend of mortality and to make comparisons across populations.
Its result is applicable to the design, implementation, and evaluation of public policies in various areas. Its applications include the identification of populations that need to be prioritized in allocating economic, human, and technological resources to increase their life expectancy.
Estimation method
The numerator for this indicator is all-cause deaths from the World Health Organization (WHO) Global Health Estimates (GHE), based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and interagency groups, and the Global Burden of Disease, among others.
The populations used in the denominator of the age-adjusted general mortality rate are from estimates by the United Nations Population Division.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as ‘garbage codes’.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from:
https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The general mortality rate is adjusted for age through direct standardization: estimated age-specific mortality rates, for both sexes or for a given sex, are applied to the World Health Organization (WHO) World Standard Population.
The populations used in the denominator of the age-adjusted general mortality rate are from estimates by the United Nations Population Division.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as ‘garbage codes’.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from:
https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The general mortality rate is adjusted for age through direct standardization: estimated age-specific mortality rates, for both sexes or for a given sex, are applied to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted general mortality rate for 2019 was 5.8 per 1 000 population in country A and 4.2 per 1 000 population in country B; that is, during that year six people died from any cause per 1 000 population of country A, compared to country B, where four per 1 000 population died. This means that, after removing the effect of a different age structure in the populations of the two countries, in 2019 the risk of dying from any cause was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted general mortality rate is a fictitious value. Its main purpose is to allow comparison across populations or in the same population over time. It should be interpreted with caution.
The value of the age-adjusted general mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations, such as the method used to prepare the population estimates and forecasts, or the application of algorithms to correct underreporting, among others.
Calculating the age-adjusted general mortality rate requires a civil registry system with good coverage. Deaths must be recorded in the system in a timely manner; otherwise the estimates will not be sufficiently accurate.
The value of the age-adjusted general mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations, such as the method used to prepare the population estimates and forecasts, or the application of algorithms to correct underreporting, among others.
Calculating the age-adjusted general mortality rate requires a civil registry system with good coverage. Deaths must be recorded in the system in a timely manner; otherwise the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
GINI Index
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The Gini coefficient measures the degree of inequality in income distribution. It measures whether income distribution (or, in some cases, consumer spending) among individuals or households within an economy deviates from a perfectly equal distribution. It is calculated based on the Lorenz curve, a cumulative frequency curve that compares the empirical distribution of a variable with the uniform (equality) distribution. Uniform distribution is represented by a diagonal line. The greater the distance, or more accurately, the area between the Lorenz curve and this diagonal, the greater the inequality. The Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini coefficient measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area below the line. Therefore, a Gini coefficient of 0 represents perfect equality, while an index of 100 implies perfect inequality.
Measurement Unit
Type of measurement
Index
Type of statistics
Corrected
Purpose
The Gini coefficient is one of the most common synthetic indicators to measure inequality. The adjustment method used in its estimation allows income distribution comparisons to be made between different countries or sectors of the same population.
This indicator is used to analyze temporal variations in a country's income distribution. It reflects the behavior of inequality in its population.
This indicator is used to analyze temporal variations in a country's income distribution. It reflects the behavior of inequality in its population.
Estimation method
The estimate of this indicator is based on data on income or consumption distribution from national household surveys obtained from government statistical agencies and World Bank country departments. When original data from household surveys are available, the share of income or consumption per quintile is calculated. Otherwise, the figures are estimated from the best pooled data available. Distribution data are adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustments are made for spatial differences in the cost of living within countries, because the data needed for such calculations are generally not available.
Interpretation - example
Varies between 0 (fully equitable distribution of income) and 100 (maximum inequality in distribution). According to 2019 data, the Gini coefficient of country A was 80.7. This means that country A has a Gini coefficient close to absolute inequality (which would be represented by a hypothetical index value of 100).
Desagregation
No disaggregation
Limitations
One limitation of the Gini coefficient is that it does not differentiate according to how inequality is distributed in the population. Because this indicator measures relative wealth and not absolute wealth, the same Gini coefficient can be obtained with two different Lorenz curves. It is also possible for the number of people in absolute poverty to decrease while the value of the indicator increases due to growing income inequality.
The accuracy of this indicator is affected by the quality of the information used to calculate it. Another aspect to consider is that because household surveys on income or consumption differ in their methodology and in the type of welfare measures collected, the data obtained are not strictly comparable between countries or even within a country in different years. Within these differences, it is the definition of income that varies most frequently in surveys. Variations in household size (number of members), income distribution, consumption needs, and age can also skew comparisons.
The Gini coefficient lacks the property of ‘additive decomposition’, by which a population's income concentration should be equal to the weighted sum of inequality in all its subgroups. Lacking this property, the Gini index is not additive between groups, that is, the total Gini of a society is not equal to the sum of the Gini indices of its subgroups.
The accuracy of this indicator is affected by the quality of the information used to calculate it. Another aspect to consider is that because household surveys on income or consumption differ in their methodology and in the type of welfare measures collected, the data obtained are not strictly comparable between countries or even within a country in different years. Within these differences, it is the definition of income that varies most frequently in surveys. Variations in household size (number of members), income distribution, consumption needs, and age can also skew comparisons.
The Gini coefficient lacks the property of ‘additive decomposition’, by which a population's income concentration should be equal to the weighted sum of inequality in all its subgroups. Lacking this property, the Gini index is not additive between groups, that is, the total Gini of a society is not equal to the sum of the Gini indices of its subgroups.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from: https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
Not applicable
References
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Stastical Databases and Publications. Availaable from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Gross domestic product (US$ per capita), current international (PPP-adjusted)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP)* is the gross domestic product converted into international dollars using purchasing power parity rates. An international dollar has the same purchasing power over Gross National Product as the U.S. dollar in the United States. GDP at market's prices is the sum of the gross value added of all producers residing in the economy, plus any taxes on products and minus any subsidies not included in the value of the products for a given national economy, at a given period, usually one year. Total population is a mid-year figure based on the de facto population, which counts all residents regardless of their legal status or citizenship.
Measurement Unit
PPP-adjusted International dollars
Type of measurement
Ratio
Type of statistics
Corrected
Purpose
The Gross Domestic Product (GDP) measures a country’s total production of goods and services for final use, produced within a national territory. Its value is used to measure the wealth and economic stability of its resident population during a given period, usually one year. International comparisons between countries are possible, as GDP is based on purchasing power parity.
Purchasing Power Parity (PPP) is based on conversion rates between currencies that considers both exchange rate differences and discrepancies in price levels between countries. Therefore, when used to deflate the corresponding aggregate national accounts, they measure the real size of the economies considering the purchasing power of the currency in each country.
This indicator is used to analyze the dynamics of a country’s economy and to evaluate geographical variations in the production and social wellbeing of its inhabitants. It helps to identify gaps in the average national income production and populations with unfavorable living conditions.
Its result is used to generate social and economic policies to respond to identified needs and encourage production in certain areas of the economy.
Purchasing Power Parity (PPP) is based on conversion rates between currencies that considers both exchange rate differences and discrepancies in price levels between countries. Therefore, when used to deflate the corresponding aggregate national accounts, they measure the real size of the economies considering the purchasing power of the currency in each country.
This indicator is used to analyze the dynamics of a country’s economy and to evaluate geographical variations in the production and social wellbeing of its inhabitants. It helps to identify gaps in the average national income production and populations with unfavorable living conditions.
Its result is used to generate social and economic policies to respond to identified needs and encourage production in certain areas of the economy.
Estimation method
GDP figures are estimated by the World Bank, based on figures provided by the United Nations System of National Accounts, expressed in national currency. GDP is calculated without deductions for depreciation of manufactured assets or for depletion and degradation of natural resources. Purchasing power parity conversion factors are estimated by the World Bank based on data collected by the International Comparison Program (ICP), an entity coordinated by United Nations regional economic commissions and other international organizations. Per capita figures are based on International Comparison Program (ICP) estimates.
Interpretation - example
According to 2019 data, the GDP (US$ per capita), PPP, of country A was
US$ 9 240, and of country B US$ 6 500. This means that, in this year, the total economic production of Country A adjusted for purchasing power parity was
US$ 9 240 per year for each of its inhabitants and the population of country A has more purchasing power than population B.
US$ 9 240, and of country B US$ 6 500. This means that, in this year, the total economic production of Country A adjusted for purchasing power parity was
US$ 9 240 per year for each of its inhabitants and the population of country A has more purchasing power than population B.
Desagregation
No disaggregation
Limitations
Because this indicator does not take into account income distribution according to social stratum or geographical area, it provides insufficient information to evaluate the true standard of living of a person residing in a given country.
GDP per capita adjusted for PPP is an average value, which can be affected by wealth concentration in the highest income strata. This may mask situations of extreme poverty, and is insufficient on its own to reflect the wellbeing and social development of a country.
GDP per capita adjusted for PPP is an average value, which can be affected by wealth concentration in the highest income strata. This may mask situations of extreme poverty, and is insufficient on its own to reflect the wellbeing and social development of a country.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from:
https://data.worldbank.org/indicator
https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
Not applicable
References
International Comparison Program, World Bank | World Development Indicators database, World Bank | Eurostat-OECD PPP Programme.
Available from: https://www.worldbank.org/en/programs/icp
World Bank Open Data. Available from: https://data.worldbank.org/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Available from: https://www.worldbank.org/en/programs/icp
World Bank Open Data. Available from: https://data.worldbank.org/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Gross national income (US$ per capita), current international (PPP-adjusted)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
Gross National Income (GNI) per capita based on purchasing power parity (PPP) is GNI converted into international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GNI as the U.S. dollar in the United States.
GNI is the value of the flow of goods and services produced by residents of a country, even if these goods are produced in a foreign country. It includes products made by domestic citizens or companies and excludes foreign products or services made within the country. It is the sum of the value added by all resident producers, plus all taxes on products (minus subsidies) not included in the valuation of the product, plus net inflows of primary income (employee remuneration and property income) from abroad.
Purchasing Power Parity (PPP) is a conversion rate between currencies that considers both exchange rate differences and discrepancies in price levels between countries. Therefore, when used to deflate the corresponding national accounts aggregates, they measure the real size of economies considering the purchasing power of the currency in each country.
GNI is the value of the flow of goods and services produced by residents of a country, even if these goods are produced in a foreign country. It includes products made by domestic citizens or companies and excludes foreign products or services made within the country. It is the sum of the value added by all resident producers, plus all taxes on products (minus subsidies) not included in the valuation of the product, plus net inflows of primary income (employee remuneration and property income) from abroad.
Purchasing Power Parity (PPP) is a conversion rate between currencies that considers both exchange rate differences and discrepancies in price levels between countries. Therefore, when used to deflate the corresponding national accounts aggregates, they measure the real size of economies considering the purchasing power of the currency in each country.
Measurement Unit
US$ per capita, PPP
Type of measurement
Ratio
Type of statistics
Corrected
Purpose
The PPP-adjusted GNI value helps to assess the economic status of a country, specifically its national productive sectors. It reflects the level of national productive competitiveness in manufacturing, investment, or savings.
It helps to assess the economic growth of a country during a specific period (usually one year) and to quantify inflation or poverty, among other purposes.
Despite its limitations, it is often calculated as an indicator reflecting a population's wellbeing.
Its result is used to generate social and economic policies to respond to identified needs and incentivize productive competition in certain areas of a country.
It helps to assess the economic growth of a country during a specific period (usually one year) and to quantify inflation or poverty, among other purposes.
Despite its limitations, it is often calculated as an indicator reflecting a population's wellbeing.
Its result is used to generate social and economic policies to respond to identified needs and incentivize productive competition in certain areas of a country.
Estimation method
GNI figures are estimated by the World Bank from the corresponding figures in the United Nations System of National Accounts, expressed in national currency. Purchasing power parity conversion factors are estimated by the World Bank based on data collected by the International Comparison Program (ICP), an entity coordinated by United Nations regional economic commissions and other international organizations. Per capita figures are based on World Bank population estimates and projections.
Interpretation - example
The PPP-adjusted GNI of country A during 2019 was US$ 17 740 per capita. This means that the total income produced by the country during that year, both inside and outside the national territory, was US$ 17 740 for each inhabitant, and can be compared with the value of another country.
Desagregation
No disaggregation
Limitations
Gross National Income (GNI) should not be confused with Gross Domestic Product (GDP). The difference is that the GNI is based on the concept of nationality, which determines if a certain factor is included in its estimate. GDP is based on the country's territorial limits, calculating all the income generated within those limits, whether domestic or foreign.
This indicator does not take into account the cost of living or subsistence levels. Therefore, it provides insufficient information to assess the true wellbeing of a country and must be interpreted alongside other indicators.
Unpaid or informal work is not included the calculations. As such, it may not reflect a country's true income level.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
This indicator does not take into account the cost of living or subsistence levels. Therefore, it provides insufficient information to assess the true wellbeing of a country and must be interpreted alongside other indicators.
Unpaid or informal work is not included the calculations. As such, it may not reflect a country's true income level.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from:
https://data.worldbank.org/indicator
https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
Not applicable
References
World Bank Open Data. Available from: https://data.worldbank.org/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Gross national income per capita, Atlas method (current US$)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
Current Gross National Income (GNI) per capita is GNI converted to U.S. dollars using the World Bank's Atlas method, divided by population at mid-year. It is the sum of the value added by all resident producers, plus all taxes on products (minus subsidies) not included in the valuation of the products, plus net inflows of primary income (employee remuneration and property income) from abroad. The estimated GNI in domestic currency is usually converted into U.S. dollars using the official exchange rate to compare between economies, although an alternative exchange rate is used when the official exchange rate is estimated to diverge significantly from the exchange rate used in international transactions.
Measurement Unit
US$ per capita
Type of measurement
Ratio
Type of statistics
Corrected
Purpose
The value of current GNI helps to assess the state of a country's economic output, whether in manufacturing, investment, or savings.
It helps to assess the economic growth of a country during a specific period (usually one year) and to quantify inflation or poverty, among other purposes.
Despite its limitations, it is often calculated as an indicator reflecting a population's wellbeing.
Its result is used to generate social and economic policies to respond to identified needs and incentivize productive competition in certain areas of a country.
It helps to assess the economic growth of a country during a specific period (usually one year) and to quantify inflation or poverty, among other purposes.
Despite its limitations, it is often calculated as an indicator reflecting a population's wellbeing.
Its result is used to generate social and economic policies to respond to identified needs and incentivize productive competition in certain areas of a country.
Estimation method
GNI figures are estimated by the World Bank from the corresponding figures in the United Nations System of National Accounts, expressed in national currency. The World Bank's Atlas conversion method is used to smooth out price and exchange rate fluctuations in national income comparisons between countries. The conversion factor averages the exchange rate of a given year and the previous two years, adjusted for the difference between the country's inflation rate and that of Japan, United Kingdom, United States, and the Euro zone.
Interpretation - example
The current GNI of country A during 2019 was US$ 14 940 per capita. This means that the total income produced by the country during that year, both inside and outside the national territory, was US$ 14 940 for each of its inhabitants.
Desagregation
No disaggregation
Limitations
Gross National Income (GNI) should not be confused with Gross Domestic Product (GDP). The difference is that GNI is based on the concept of nationality, which determines if a certain factor is included in its estimate. GDP is based on the country's territorial limits, calculating all the income generated within those limits, whether domestic or foreign.
This indicator does not take into account the cost of living or subsistence levels. Therefore, it provides insufficient information to assess the true wellbeing of a country, and it must be interpreted alongside other indicators. Unpaid or informal work is not included in its calculation. As such, it may not reflect a country's true income level.
In some countries the values of GNI and Gross Domestic Product (GDP) are quite similar, however, in others the GNI may be much higher than GDP, due to the economic aid the country receives from abroad.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
This indicator does not take into account the cost of living or subsistence levels. Therefore, it provides insufficient information to assess the true wellbeing of a country, and it must be interpreted alongside other indicators. Unpaid or informal work is not included in its calculation. As such, it may not reflect a country's true income level.
In some countries the values of GNI and Gross Domestic Product (GDP) are quite similar, however, in others the GNI may be much higher than GDP, due to the economic aid the country receives from abroad.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from:
https://data.worldbank.org/indicator
https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
Not applicable
References
World Bank Open Data. Available from: https://data.worldbank.org/
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization/World Health Organization, Department of Evidence and Intelligence for Action in Health/Health Analysis, Metrics, and Evidence Unit. Database. Health Trends in the Americas: Core Indicators 2019. Washington, D.C., United States of America, 2019. Available from: https://iris.paho.org/handle/10665.2/51542
Pan American Health Organization. Health Information and Analysis Unit (HA). Regional Core Health Data Initiative; Core Indicators Glossary. Washington DC, June 2015. Available from:
https://www.paho.org/hq/dmdocuments/2015/glossary-eng-2014.pdf
Economic Commission for Latin America and the Caribbean - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization/World Health Organization, Department of Evidence and Intelligence for Action in Health/Health Analysis, Metrics, and Evidence Unit. Database. Health Trends in the Americas: Core Indicators 2019. Washington, D.C., United States of America, 2019. Available from: https://iris.paho.org/handle/10665.2/51542
Pan American Health Organization. Health Information and Analysis Unit (HA). Regional Core Health Data Initiative; Core Indicators Glossary. Washington DC, June 2015. Available from:
https://www.paho.org/hq/dmdocuments/2015/glossary-eng-2014.pdf
Economic Commission for Latin America and the Caribbean - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Health technicians (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Health technician density is defined as total number of practising health technicians, with formal education, in health facilities as of December 31 of a specific year, per 10 000 population in a given country, territory, or geographic area.
They carry out technical and practical tasks to support the activities of promotion, prevention, diagnosis, treatment and rehabilitation of diseases, injuries and disabilities. These occupations are classified into the following subgroups: Dental assistants and therapists, Dental prosthetists, Pharmacy assistants, Diagnostic imaging and therapeutic equipment technicians, Clinical laboratory and pathological anatomy technicians, Environmental and occupational health technicians or inspectors, Opticians, Medical Documentalists / Health Statistics Technicians, Medical Prosthetists, Traditional and Complementary Medicine Associates, Community Health Agents or Community Health Workers, Physician Assistants, Ambulance Technicians. Personnel who have been included in the categories corresponding to nursing and midwifery professionals are excluded.
The definitions of the International Standard Classification of Occupations (ISCO-08) are adapted to the context of the Region of the Americas and its countries.
They carry out technical and practical tasks to support the activities of promotion, prevention, diagnosis, treatment and rehabilitation of diseases, injuries and disabilities. These occupations are classified into the following subgroups: Dental assistants and therapists, Dental prosthetists, Pharmacy assistants, Diagnostic imaging and therapeutic equipment technicians, Clinical laboratory and pathological anatomy technicians, Environmental and occupational health technicians or inspectors, Opticians, Medical Documentalists / Health Statistics Technicians, Medical Prosthetists, Traditional and Complementary Medicine Associates, Community Health Agents or Community Health Workers, Physician Assistants, Ambulance Technicians. Personnel who have been included in the categories corresponding to nursing and midwifery professionals are excluded.
The definitions of the International Standard Classification of Occupations (ISCO-08) are adapted to the context of the Region of the Americas and its countries.
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Health technicians play a key role in providing health services. This indicator makes it possible to identify inequalities in the distribution of health resources and prioritize human and economic resource allocation to specific populations. Its value is used to develop public policies to increase funding for health, and to train, hire, and retrain skilled workers in this area.
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of health technicians reported by the countries of the Americas to the Pan American Health Organization (PAHO). This data is collected from secondary sources, such as: health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Interpretation - example
According to 2019 data, the density of health technicians in country A was 10.5 per 10 000 population. This means that, in all health facilities, this country A had 10.5 health technicians per 10 000 population in 2019.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data used for calculation. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working health technicians, or include all registered or licensed, even if their employment status is unknown.
The value of this indicator may differ from figures calculated by each country due to differences such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working health technicians, or include all registered or licensed, even if their employment status is unknown.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
HIV/AIDS mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from HIV/AIDS (human immunodeficiency virus/acquired immunodeficiency syndrome) in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year, after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
This indicator reflects the magnitude of HIV mortality as a public health problem, helps identify populations at higher risk of dying from the disease, and helps design and evaluate specific public health interventions.
Its result is applicable to the design, implementation, and evaluation of public policies related to HIV. Its result contributes, for example, to evaluating access to and timeliness of diagnosis, coverage of antiretroviral therapies and evaluating the effectiveness of health policies aimed at HIV prevention and control.
The age-adjusted HIV mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of public policies related to HIV. Its result contributes, for example, to evaluating access to and timeliness of diagnosis, coverage of antiretroviral therapies and evaluating the effectiveness of health policies aimed at HIV prevention and control.
The age-adjusted HIV mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from the human immunodeficiency virus/ acquired immunodeficiency syndrome (HIV/AIDS), from the World Health Organization (WHO) Global Health Estimates (GHE). Empirical data from different sources on HIV/AIDS surveillance are consolidated to estimate the level and trend of HIV infection and mortality in adults and children. These sources include national civil registry systems, estimates from WHO technical programs, the United Nations, and inter-agency groups.
The underlying causes of death correspond to codes B20 – B24 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted HIV/AIDS mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The HIV/AIDS mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes B20 – B24 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted HIV/AIDS mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The HIV/AIDS mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted HIV/AIDS mortality rate for 2019 was 22 per 100 000 population in country A and 12 per 100 000 population in country B; that is, in that year 22 people died from HIV/AIDS per 100 000 population of country A, compared to country B, where 12 people died from this cause per 100 000 population. This result indicates that, after removing effect of difference in the age distribution in the two countries, in 2019 the risk of dying from HIV/AIDS was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted HIV/AIDS mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted HIV/AIDS mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences the estimate is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating this rate requires a civil registry system with good coverage. HIV/AIDS deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted HIV/AIDS mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences the estimate is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating this rate requires a civil registry system with good coverage. HIV/AIDS deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global HIV Programme. HIV data and statistics. Available from: https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hiv/strategic-information/hiv-data-and-statistics
World Health Organization (WHO). Global progress report on HIV, viral hepatitis, and sexually transmitted infections, 2021. Accountability for the global health sector strategies 2016–2021: actions for impact. Geneva, 2021. Available from: https://www.who.int/publications/i/item/9789240027077
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global HIV Programme. HIV data and statistics. Available from: https://www.who.int/teams/global-hiv-hepatitis-and-stis-programmes/hiv/strategic-information/hiv-data-and-statistics
World Health Organization (WHO). Global progress report on HIV, viral hepatitis, and sexually transmitted infections, 2021. Accountability for the global health sector strategies 2016–2021: actions for impact. Geneva, 2021. Available from: https://www.who.int/publications/i/item/9789240027077
Homicide mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from homicides and intentionally inflicted injuries in the population in a given country, territory or geographic area during a specific calendar year, divided by the total number of its population, generally estimated in the middle of the same year (average population), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Predicted
Purpose
This indicator reflects the magnitude of homicide mortality as a public health problem and helps to identify populations with higher levels of violence and to design and evaluate specific public health interventions.
Its result is applicable to the design, implementation, and evaluation of public safety policies. Its result contributes, for example, to assessing the levels of violence and public safety in a country or geographic area, along with encouraging research on the causes that give rise to violence.
The age-adjusted homicide mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of public safety policies. Its result contributes, for example, to assessing the levels of violence and public safety in a country or geographic area, along with encouraging research on the causes that give rise to violence.
The age-adjusted homicide mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator for this indicator (also called “interpersonal violence”), uses homicides deaths from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on multiple sources, for example, data from police and civil registry sources provided by countries, data from the United Nations Office on Drugs and Crime (UNODC) global studies on homicides, data from the WHO mortality database, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes X85 – Y09, Y87.1 of the International Classification of Diseases, Tenth Revision (ICD-10).
The estimation process uses observed data on homicide rates, along with regression models for countries without sufficient data availability or quality, to calculate comparable estimates of homicide rates and numbers across countries.
The populations used in the denominator of the age-adjusted homicide mortality rate are from estimates by the United Nations Population Division.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The homicide mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes X85 – Y09, Y87.1 of the International Classification of Diseases, Tenth Revision (ICD-10).
The estimation process uses observed data on homicide rates, along with regression models for countries without sufficient data availability or quality, to calculate comparable estimates of homicide rates and numbers across countries.
The populations used in the denominator of the age-adjusted homicide mortality rate are from estimates by the United Nations Population Division.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The homicide mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted homicide mortality rate for 2019 was 5.9 per 100 000 population in country A and 3.2 per 100 000 population in country B; that is, in country A in 2019 there were six homicides deaths per 100 000 population, compared to country B, where three people died from this cause per 100 000 population. This result indicates that, after removing the effect of differences in the age distribution in the two countries, in 2019 the risk of dying by homicide was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate from homicides is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted homicide mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences this result is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the homicide mortality rate requires a civil registry system with good coverage. Homicide deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted homicide mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Another methodological consideration that influences this result is the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the homicide mortality rate requires a civil registry system with good coverage. Homicide deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 16.1.1 Number of victims of intentional homicide per 100,000 population, by sex and age
Available from: https://sdgs.un.org/goals
Indicator 16.1.1 Number of victims of intentional homicide per 100,000 population, by sex and age
Available from: https://sdgs.un.org/goals
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
United Nations Office on Drugs and Crime (UNODC). Available from: https://www.unodc.org/unodc/en/index.html
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
United Nations Office on Drugs and Crime (UNODC). Available from: https://www.unodc.org/unodc/en/index.html
Hospital beds ratio (1 000 pop)
Domain
Health system
Subdomain
Health service
Definition
Number of hospital beds available per thousand population in a given country, territory, or geographical area in a given year.
Number of hospital beds refers to all hospital beds in public, social and private settings, which are maintained and staffed on a regular basis and are immediately available for the care of admitted patients. They are the sum of the following four categories: (i) Acute care beds; (ii) Rehabilitation beds; (iii) Long-term care beds; and (iv) Other hospital beds. Included: Beds in all general hospitals, mental health hospitals, and other specialized hospitals; Occupied and unoccupied beds. Excluded: Operating room tables, recovery cots, emergency stretchers, beds for same-day care, cribs for healthy infants; Beds in rooms closed for any reason; Temporary beds; Beds in residential long-term care facilities.
Number of hospital beds refers to all hospital beds in public, social and private settings, which are maintained and staffed on a regular basis and are immediately available for the care of admitted patients. They are the sum of the following four categories: (i) Acute care beds; (ii) Rehabilitation beds; (iii) Long-term care beds; and (iv) Other hospital beds. Included: Beds in all general hospitals, mental health hospitals, and other specialized hospitals; Occupied and unoccupied beds. Excluded: Operating room tables, recovery cots, emergency stretchers, beds for same-day care, cribs for healthy infants; Beds in rooms closed for any reason; Temporary beds; Beds in residential long-term care facilities.
Measurement Unit
Hospital beds per 1 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
The hospital bed ratio provides a measure of the resources available to provide services to inpatients in hospitals in terms of the number of beds maintained, staffed, and immediately available for use. It helps monitor health systems, identify areas that require greater investment, and prioritize the allocation of economic, human, and technological resources. It reflects both demand- and supply-side factors and provides a rough approximation of the scope of physical, financial, and other barriers to health care in a population.
Estimation method
The numerator for the ratio of hospital beds is obtained from the number of hospital beds reported by the ministries of health to the Pan American Health Organization (PAHO). For the denominator, population statistics from the United Nations Population Division.
Interpretation - example
The ratio of hospital beds in country A in 2019 was 2.0 per 1 000 population. This means that, including the public, social and private health sectors, there were 2.0 hospital beds available for use per thousand population in country A that year.
Desagregation
No disaggregation
Limitations
Although hospital beds are used to indicate the availability of inpatient services, there is no global standard for hospital bed density relative to the total population.
Due to methodological differences such as the population used as a denominator, the value of this indicator may differ from the results obtained by the country. It should also be considered that, depending on the source used and the means of monitoring, the data may not be exactly comparable between countries.
Due to methodological differences such as the population used as a denominator, the value of this indicator may differ from the results obtained by the country. It should also be considered that, depending on the source used and the means of monitoring, the data may not be exactly comparable between countries.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Organization for Economic Co-operation and Development (OECD). OECD Data. Hospital beds (indicator). Available from:
https://data.oecd.org/healtheqt/hospital-beds.htm
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
https://data.oecd.org/healtheqt/hospital-beds.htm
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
Hospital ICU beds ratio (100 000 pop)
Domain
Health service coverage
Subdomain
Health service
Definition
Number of hospital intensive care unit (ICU) beds available per 100 000 population in a specific country, territory, or geographic area in a given year.
The number of ICU beds refers to all hospital beds in the public, and private sectors.
The ICU is an organized system for delivering highly complex care to critically ill patients that provides intensive, specialized medical and nursing care, enhanced monitoring capacity, and multiple advanced life-sustaining treatments during a period of acute organ system failure. It includes pediatric, neonatal, and adult ICU beds.
The number of ICU beds refers to all hospital beds in the public, and private sectors.
The ICU is an organized system for delivering highly complex care to critically ill patients that provides intensive, specialized medical and nursing care, enhanced monitoring capacity, and multiple advanced life-sustaining treatments during a period of acute organ system failure. It includes pediatric, neonatal, and adult ICU beds.
Measurement Unit
Hospital beds per 100 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
The hospital ICU beds ratio provides a measurement of the resources available to provide services to hospitalized patients who require critical care in terms of the number of beds that are maintained, that have specialized health personnel, and that are immediately available for use. This indicator contributes to monitoring health systems, identifying areas that require greater investment, and prioritizing the allocation of economic, human, and technological resources. It reflects both demand-side and supply-side factors and provides a rough approximation of the extent of physical, financial, and other barriers, as well as the health care management capacity in a population.
Estimation method
The hospital ICU beds ratio uses the number of beds reported by Ministries of Health to the Pan American Health Organization (PAHO) as the numerator. Population figures from the UN Population Division are used for the denominator.
Interpretation - example
The hospital ICU beds ratio in country A in 2019 was 6 per 100 000 population; that is, including the public and private health sectors, that year there were 6 hospital beds available for use per 100 000 population of country A.
Desagregation
No disaggregation
Limitations
Although hospital ICU beds are used to indicate the availability of services for hospitalized critical care patients, there is no global standard for bed density relative to the total population.
Due to methodological differences such as the population used in the denominator, this indicator’s value may differ from each country’s results. Moreover, depending on the definition, source and monitoring methods used, the data may not be exactly comparable across countries.
Due to methodological differences such as the population used in the denominator, this indicator’s value may differ from each country’s results. Moreover, depending on the definition, source and monitoring methods used, the data may not be exactly comparable across countries.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Organization for Economic Co-operation and Development (OECD). Health at a Glance 2021: OECD Indicators. Hospital beds and occupancy; 2021. Available from:
https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2021_ae3016b9-en
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
https://www.oecd-ilibrary.org/social-issues-migration-health/health-at-a-glance-2021_ae3016b9-en
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
Human rabies cases
Domain
Morbidity
Subdomain
Communicable diseases
Definition
Number of confirmed cases of human rabies in a particular country, territory, or geographical area during a specific period. This includes cases confirmed clinically, epidemiologically, or by laboratory investigation.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
The goal of monitoring rabies numbers is to eliminate dog-transmitted human rabies on the continent. In addition, the indicator helps monitor rabies transmitted by wild animals and herbivores that are still widespread in the Americas.
Rabies is an acute zoonotic disease caused by a Rhabdoviridae virus, leading to progressive and almost certain fatal inflammation of the brain and spinal cord. It is transmitted through the saliva of infected domestic or wild mammals; the most frequent vector for human rabies is the domestic dog. Despite being lethal once clinical signs appear, rabies is totally preventable. Vaccinating dog is the primary and most cost-effective measure to prevent human rabies. Prophylactic treatment exists for humans who are exposed.
The number of cases of human rabies indicates the magnitude of the disease in the population, reflecting its health and socioeconomic development and the effectiveness of human and animal rabies control strategies. The indicator is used to identify at-risk populations in order to focus preventive strategies, especially dog vaccination and control of other mammal vectors (e.g., bats), in addition to strengthening intersectoral collaboration to fight the disease and improving surveillance systems for human and animal rabies. This indicator is also used when allocating economic, human, and technological resources.
Rabies is an acute zoonotic disease caused by a Rhabdoviridae virus, leading to progressive and almost certain fatal inflammation of the brain and spinal cord. It is transmitted through the saliva of infected domestic or wild mammals; the most frequent vector for human rabies is the domestic dog. Despite being lethal once clinical signs appear, rabies is totally preventable. Vaccinating dog is the primary and most cost-effective measure to prevent human rabies. Prophylactic treatment exists for humans who are exposed.
The number of cases of human rabies indicates the magnitude of the disease in the population, reflecting its health and socioeconomic development and the effectiveness of human and animal rabies control strategies. The indicator is used to identify at-risk populations in order to focus preventive strategies, especially dog vaccination and control of other mammal vectors (e.g., bats), in addition to strengthening intersectoral collaboration to fight the disease and improving surveillance systems for human and animal rabies. This indicator is also used when allocating economic, human, and technological resources.
Estimation method
The number of laboratory-confirmed cases of human rabies comes from data officially reported by the Ministries of Health of affected countries to the PAHO/WHO Pan American Center for Foot-and-Mouth Disease and Veterinary Public Health (PANAFTOSA/VPH). The data are mostly obtained from national surveillance systems and technical programs on human and animal rabies and collected by PANAFTOSA/VPH from official information sources, such as the Regional Information System for The Epidemiological Surveillance of Rabies (SIRVERA). Cases ruled out after laboratory investigation are not included.
Interpretation - example
In 2019, there were three confirmed cases of human rabies in country A.
Desagregation
By sex, transmission by domestic dog, transmission by wild animals
Limitations
The value of this indicator is impacted by the effectiveness and coverage of human rabies surveillance and control systems.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization/World Health Organization (PAHO/WHO). Pan American Center for Foot-and-Mouth Disease and Veterinary Public Health (PANAFTOSA/VPH). Available from: https://www.paho.org/en/panaftosa
Pan American Health Organization (PAHO). Rabies. Available from: https://www.paho.org/en/topics/rabies
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Rabies. Available from: https://www.paho.org/en/topics/rabies
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ill-defined and unknown causes of death (%)
Domain
Health system
Subdomain
Data quality
Definition
Number of deaths whose underlying cause was assigned an ill-defined code, expressed as a percentage of total deaths in a given country, territory, or geographical area, during a specific year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
The percentage of ill-defined causes of death is used to assess the quality of mortality information. Quantifying the value of this indicator is essential to understand the quality and correct interpretation of causes of death and determine their effect on other mortality indicators.
Ill-defined causes of death refer to conditions that should not be considered as underlying cause (specifically signs, symptoms, or laboratory findings) and do not match the definition of underlying cause (an illness or injury that set off the chain of events directly leading to death, or the accident or violent act that produced the fatal injury). From a public health perspective, it is important to know the underlying cause in order to support prevention programs and design and implement effective policies and strategies.
Among the reasons for selecting this cause as an underlying cause on the death certificate are: insufficient case information from the certifying physician; lack of training among personnel on how to correctly certify the death; and certification authorization given to non-medical personnel that often lack training and information on underlying cause certification. Another reason is failure to train and update mortality coders.
Ill-defined causes impede targeted health interventions. For example, hypovolemic shock (an ill-defined cause) may result from gastrointestinal bleeding, a stab wound (intentional or unintentional), or postpartum obstetric bleeding. Preventive actions differ in each of the three causes, which highlights the importance of correctly specifying the underlying cause of death.
Generally speaking, causes of death statistics are essential when allocating resources to health services and planning and evaluating health policies. As a result, having correct and reliable underlying cause of death information is a public health priority.
Ill-defined causes of death refer to conditions that should not be considered as underlying cause (specifically signs, symptoms, or laboratory findings) and do not match the definition of underlying cause (an illness or injury that set off the chain of events directly leading to death, or the accident or violent act that produced the fatal injury). From a public health perspective, it is important to know the underlying cause in order to support prevention programs and design and implement effective policies and strategies.
Among the reasons for selecting this cause as an underlying cause on the death certificate are: insufficient case information from the certifying physician; lack of training among personnel on how to correctly certify the death; and certification authorization given to non-medical personnel that often lack training and information on underlying cause certification. Another reason is failure to train and update mortality coders.
Ill-defined causes impede targeted health interventions. For example, hypovolemic shock (an ill-defined cause) may result from gastrointestinal bleeding, a stab wound (intentional or unintentional), or postpartum obstetric bleeding. Preventive actions differ in each of the three causes, which highlights the importance of correctly specifying the underlying cause of death.
Generally speaking, causes of death statistics are essential when allocating resources to health services and planning and evaluating health policies. As a result, having correct and reliable underlying cause of death information is a public health priority.
Estimation method
Ill-defined underlying causes of death are: R00-R94, R96-R99 of Chapter XVIII of the ICD-10 "Symptoms, signs, and abnormal clinical and laboratory findings, not classified elsewhere".
The percentage of ill-defined causes of death is calculated by the Pan American Health Organization (PAHO) from data reported by the responsible national institution.
Formula:
(A/B) x 100
Numerator (A):
Number of deaths will ill-defined causes in year z in a given country, territory, or geographic area.
Denominator (B):
Deaths from all causes in year z in a given country, territory, or geographic area.
The percentage of ill-defined causes of death is calculated by the Pan American Health Organization (PAHO) from data reported by the responsible national institution.
Formula:
(A/B) x 100
Numerator (A):
Number of deaths will ill-defined causes in year z in a given country, territory, or geographic area.
Denominator (B):
Deaths from all causes in year z in a given country, territory, or geographic area.
Interpretation - example
According to 2019 data, the percentage of ill-defined causes of death in country A was 4%, i.e., 4 out of every 100 deaths in country A that year were coded with ill-defined causes of death, and therefore are not useful in public health analyses because the true underlying cause of death is unknown.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator is impacted by the coverage of civil registration systems, timely registration of deaths, and recorded information on causes of death. For example, when there is incomplete coverage in registration systems, the proportion of deaths assigned to ill-defined causes will generally increase as coverage increases, even with no real drop in the quality of medical certification.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
University of Melbourne. Avoiding ill-defined and unusable underlying causes: The underlying condition responsible for the failure should also be reported. Available from: https://crvsgateway.info/Avoiding-ill-defined-and-unusable-underlying-causes~343
University of Melbourne. Avoiding ill-defined and unusable underlying causes: The underlying condition responsible for the failure should also be reported. Available from: https://crvsgateway.info/Avoiding-ill-defined-and-unusable-underlying-causes~343
Immunization coverage (%), PCV3
Domain
Health system
Subdomain
Child health
Definition
The percentage of children under 2 years of age who have received three doses of the Pneumococcal Conjugate vaccine (PCV3) in a specific year, in a given country, territory, or geographical area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Pneumococcus is one of the main causes of pneumonia and meningitis in children, causing death in a high number of cases. Immunization is one of the most cost-effective public health interventions and is essential to reducing infant mortality. PCV coverage is a measure of the performance of the health system in administering childhood vaccination. Its value identifies gaps in immunization program coverage and promotes the allocation of economic, human, and technological resources to guarantee proper program functioning.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
In some countries, the third dose is applied in children under 1 year. In others, children receive the vaccine at 1 year of age. As a result, the denominator selected is 2-year-old children.
In some countries, the third dose is applied in children under 1 year. In others, children receive the vaccine at 1 year of age. As a result, the denominator selected is 2-year-old children.
Interpretation - example
In 2019, immunization coverage in children under 2 years of age with the third dose of pneumococcal vaccine in country A was 79%, i.e., for every 100 children of that age, 79 received three doses of pneumococcal vaccine in 2019.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data. Ideally, countries would also have a well-functioning national immunization registry. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national program.
Available from: https://sdgs.un.org/
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national program.
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Pneumococcus: Available from: https://www.paho.org/en/topics/pneumococcus
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Pneumococcus: Available from: https://www.paho.org/en/topics/pneumococcus
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Immunization coverage of 1 year old (%), MMR1
Domain
Health system
Subdomain
Child health
Definition
The percentage of 1-year-old children who have received the first dose of measles vaccine (usually in combination with rubella and mumps) in a specific year, in a given country, territory, or geographic area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Immunization is one of the most cost-effective public health interventions and is essential to reducing infant mortality. Measles vaccination coverage measures the performance of the health system in administering childhood vaccination. Its value identifies gaps in immunization program coverage and promotes the allocation of economic, human, and technological resources to guarantee proper program functioning. It helps measles elimination efforts to become more targeted.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
Interpretation - example
In 2019, immunization coverage in 1-year-old children with the first dose of measles vaccine was 91% in country A; i.e., for every 100 children of that age, 91 received at least one dose of the measles vaccine in 2019.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data. Ideally, countries would also have a well-functioning national immunization registry. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Immunization coverage of under-1 year old (%), BCG
Domain
Health system
Subdomain
Child health
Definition
The percentage of children under 1 year of age who have received a dose of the Bacille Calmette-Guérin (BCG) tuberculosis vaccine in a specific year, in a given country, territory, or geographical area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Immunization is essential to reducing under-5 mortality. The Bacillus Calmette-Guérin (BCG) vaccine has been available for 80 years and is one of today's most widely used vaccines. Although it does not prevent primary infection or reactivation of latent lung infection, the BCG vaccine has a documented protective effect against meningitis and disseminated tuberculosis in children.
Estimates are used to monitor immunization service coverage and to guide efforts to eradicate and eliminate diseases, in this case, tuberculosis. This indicator measures the performance of the health system and the population's access to vaccination programs.
Estimates are used to monitor immunization service coverage and to guide efforts to eradicate and eliminate diseases, in this case, tuberculosis. This indicator measures the performance of the health system and the population's access to vaccination programs.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
Interpretation - example
In 2019, BCG immunization coverage in children under 1 year of age in country A was 80%; i.e., 4 out of 5 children received the single dose of BCG vaccine in 2019.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data. Ideally, countries would also have a well-functioning national immunization registry. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Immunization. Available from:
https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Immunization. Available from:
https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Immunization coverage of under-1 year old (%), DTP3-cv
Domain
Health system
Subdomain
Child health
Definition
The percentage of children who, by the end of their first year of life, have received three doses of the diphtheria, tetanus, and pertussis vaccine (DTP) in a given country, territory, or geographic area, in a specific year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Immunization is one of the most cost-effective public health interventions and is essential to reducing infant mortality. This indicator measures the performance of the health system in administering childhood vaccination. Its value identifies gaps in immunization program coverage and promotes the allocation of economic, human, and technological resources to guarantee proper program functioning.
The indicator is part of the Monitoring Framework for Universal Health in the Americas, which monitors country progress in providing access and universal health coverage.
The indicator is part of the Monitoring Framework for Universal Health in the Americas, which monitors country progress in providing access and universal health coverage.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
Interpretation - example
In 2019, immunization coverage in children under 1 year of age with the third dose of DTP in country A was 85%; i.e., for every 100 children of that age, 85 had received all three doses of the DTP vaccine in 2019.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data. Ideally, countries would also have a well-functioning national immunization registry. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national program.
Available from: https://sdgs.un.org/
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national program.
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
Pan American Health Organization (PAHO). Immunization data and statistics. Available from: https://www.paho.org/en/topics/immunization/immunization-data-and-statistics
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
Pan American Health Organization (PAHO). Immunization data and statistics. Available from: https://www.paho.org/en/topics/immunization/immunization-data-and-statistics
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Immunization coverage of under-1 year old (%), Polio 3
Domain
Health system
Subdomain
Child health
Definition
The percentage of children under 1 year of age who have received three doses of the polio vaccine in a specific year, in a given country, territory, or geographical area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Polio is a highly contagious viral disease that causes permanent paralysis, and can lead to death. The child population is the most susceptible. Immunization is the most effective way to prevent this disease and to reduce disability and infant mortality. Polio vaccine coverage is a measure of the performance of the health system in administering childhood vaccination. The indicator's value identifies gaps in immunization program coverage and promotes the allocation of economic, human, and technological resources to guarantee proper program functioning. It enables efforts to monitor the eradication of the disease in the Region of the Americas.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
Interpretation - example
In 2019, immunization coverage in children under 1 year of age with the third dose of polio vaccine was 86% in country A, i.e., for every 100 children of that age, 86 received three doses of polio vaccine in 2019.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data. Ideally, countries would also have a well-functioning national immunization registry. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Immunization coverage of under-1 year old (%), Rotavirus
Domain
Health system
Subdomain
Child health
Definition
The percentage of children under 1 year of age who completed their rotavirus vaccination schedule in a specific year, in a given country, territory, or geographical area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Rotavirus is highly contagious and is the most common cause of severe diarrheal disease in young children worldwide, for whom it may be deadly. Rotavirus vaccine has been shown to be safe and highly effective in preventing serious illness and hospitalization. Rotavirus vaccine coverage is a measure of the performance of the health system in administering childhood vaccination. Its value identifies gaps in immunization program coverage and promotes the allocation of economic, human, and technological resources to guarantee proper program functioning.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
This indicator is the percentage of children under 1 year of age who received the last dose of rotavirus vaccine. This may be a second or third dose depending on the vaccine used.
This indicator is the percentage of children under 1 year of age who received the last dose of rotavirus vaccine. This may be a second or third dose depending on the vaccine used.
Interpretation - example
In 2019, rotavirus immunization coverage in children under 1 year of age was 69% in country A; i.e., for every 100 children of that age, 69 completed their rotavirus vaccination schedule in 2019.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data. Ideally, countries would also have a well-functioning national immunization registry. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Immunization. Available from: https://www.paho.org/en/topics/immunization
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Incidence of congenital syphilis (1 000 live births)
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The number of new cases of congenital syphilis (live births and stillbirths) reported in the last 12 months in a given country, territory, or geographical area, compared to the total number of live births in the same population and year.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Crude
Purpose
Congenital syphilis occurs when a pregnant woman with syphilis transmits the infection to the fetus. Untreated syphilis is a major public health problem. It is the second most frequent cause of infectious fetal death worldwide and is associated with an increased risk of neonatal death, congenital disease (congenital syphilis), premature birth, low birth weight, and HIV transmission and acquisition in mother and baby. Despite the severity, it is preventable by early diagnosis and immediate treatment of the mother during prenatal care.
The incidence of congenital syphilis is considered when evaluating strategies designed and implemented to reduce mother-to-child transmission of syphilis. Its value helps to identify at-risk groups in need of improved prenatal care and surveillance for the disease, analyze temporal and geographical trends, and estimate human, economic, and technological resources to strengthen preventive programs.
This indicator is part of the Monitoring Framework for Universal Health in the Americas, measuring national progress on implementing policies aimed at strengthening health systems and achieving universal health. It is part of efforts to monitor the progress of countries eliminating mother-to-child transmission of syphilis.
As a shared target exists to eliminate mother-to-child transmission of HIV and congenital syphilis, WHO, UNICEF, and UNAIDS provide joint support to assess congenital syphilis incidence and include it in the Progress Report on the Global Response to HIV/AIDS.
The incidence of congenital syphilis is considered when evaluating strategies designed and implemented to reduce mother-to-child transmission of syphilis. Its value helps to identify at-risk groups in need of improved prenatal care and surveillance for the disease, analyze temporal and geographical trends, and estimate human, economic, and technological resources to strengthen preventive programs.
This indicator is part of the Monitoring Framework for Universal Health in the Americas, measuring national progress on implementing policies aimed at strengthening health systems and achieving universal health. It is part of efforts to monitor the progress of countries eliminating mother-to-child transmission of syphilis.
As a shared target exists to eliminate mother-to-child transmission of HIV and congenital syphilis, WHO, UNICEF, and UNAIDS provide joint support to assess congenital syphilis incidence and include it in the Progress Report on the Global Response to HIV/AIDS.
Estimation method
The value of this indicator comes from cases reported by countries to the World Health Organization (WHO), collected in their routine health information systems.
The numerator is the number of reported congenital syphilis cases (live births and stillbirths) in the previous 12 months in a specific country, territory, or geographical area. The populations used in the denominator are from estimates by the United Nations Population Division. The incidence is calculated by PAHO.
Formula:
(A/B) x 1 000
Numerator (A):
Number of reported cases of congenital syphilis in year z in a given country, territory, or geographic area.
Denominator (B):
Number of live births in year z in a country, territory, or geographic area.
The numerator is the number of reported congenital syphilis cases (live births and stillbirths) in the previous 12 months in a specific country, territory, or geographical area. The populations used in the denominator are from estimates by the United Nations Population Division. The incidence is calculated by PAHO.
Formula:
(A/B) x 1 000
Numerator (A):
Number of reported cases of congenital syphilis in year z in a given country, territory, or geographic area.
Denominator (B):
Number of live births in year z in a country, territory, or geographic area.
Interpretation - example
The incidence of congenital syphilis in country A reached 1.5 per 1 000 live births in 2019, i.e., per 1 000 live births, 1.5 new cases of congenital syphilis were reported that year.
Desagregation
No disaggregation
Limitations
There are several limitations on this indicator, one of the most important being low availability of specific diagnostic tests. In many countries, congenital syphilis is diagnosed based on medical history and physical examination. This affects diagnostic reliability and the accuracy of the indicator.
Possible differences between WHO and the countries regarding their operational definitions of congenital syphilis can be a factor. Although WHO has a global definition for surveillance purposes, the actual definition may vary within or between countries.
Possible differences between WHO and the countries regarding their operational definitions of congenital syphilis can be a factor. Although WHO has a global definition for surveillance purposes, the actual definition may vary within or between countries.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator: 18. Incidence rate of congenital syphilis (including stillbirths)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator: 18. Incidence rate of congenital syphilis (including stillbirths)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Methods for surveillance and monitoring of congenital syphilis elimination within existing systems. Geneva, 2011. Available from: https://www.who.int/reproductivehealth/publications/rtis/9789241503020/en/
World Health Organization (WHO). Global guidance on criteria and processes for validation: elimination of mother-to-child transmission of HIV and syphilis. Geneva, 2017. Available from: https://www.paho.org/en
ode/21360
World Health Organization (WHO). Global health sector strategy on Sexually Transmitted Infections, 2016-2021. Towards ending STIs. Geneva, 2016. Available from: https://www.paho.org/en/documents/global-health-sector-strategy-sexually-transmitted-infections-2016-2021-towards-ending#:~:text=The%20present%20global%20health%20sector,goals%2C%20targets%2C%20guiding%20principles%20and
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Methods for surveillance and monitoring of congenital syphilis elimination within existing systems. Geneva, 2011. Available from: https://www.who.int/reproductivehealth/publications/rtis/9789241503020/en/
World Health Organization (WHO). Global guidance on criteria and processes for validation: elimination of mother-to-child transmission of HIV and syphilis. Geneva, 2017. Available from: https://www.paho.org/en
ode/21360
World Health Organization (WHO). Global health sector strategy on Sexually Transmitted Infections, 2016-2021. Towards ending STIs. Geneva, 2016. Available from: https://www.paho.org/en/documents/global-health-sector-strategy-sexually-transmitted-infections-2016-2021-towards-ending#:~:text=The%20present%20global%20health%20sector,goals%2C%20targets%2C%20guiding%20principles%20and
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Infant mortality rate (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
The quotient between the total number of deaths in children under 1 year of age in a given country, territory, or geographic area during a specific calendar year and the number of live births in the same population and year, expressed per 1 000 live births.
Live birth means the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, that, after such expulsion or extraction, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered a live birth. (ICD-10)
Live birth means the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, that, after such expulsion or extraction, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered a live birth. (ICD-10)
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Crude/Corrected
Purpose
This indicator quantifies infant deaths as a public health problem for a given population or geographic area. The value reflects the risk of children born alive dying during their first year of life. It makes it possible to identify health inequities and populations with specific risk factors.
The infant mortality rate reflects children’s health status and social, economic, and environmental conditions. It is related to maternal and child health care access, quality, and timeliness. Its applications include strengthening professional childbirth care, breastfeeding, and vaccination programs.
This indicator is useful for designing, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health. Its applications include evaluating time and geographic trends in infant mortality.
The infant mortality rate reflects children’s health status and social, economic, and environmental conditions. It is related to maternal and child health care access, quality, and timeliness. Its applications include strengthening professional childbirth care, breastfeeding, and vaccination programs.
This indicator is useful for designing, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health. Its applications include evaluating time and geographic trends in infant mortality.
Estimation method
The infant mortality rate uses infant deaths reported by the national health agency as the numerator and the total number of live births for the same population and year as the denominator.
Formula:
(A/B) x 1 000
Numerator (A):
Number of infant deaths in year z in a given country, territory or geographic area
Denominator (B):
Total number of live births in year z in the same country, territory or geographic area
To estimate the indicator by sex, the following formula is applied:
(A/B) x 1 000
Numerator (A):
Number of infant deaths of a given sex in year z in a specific country, territory, or geographic area.
Denominator (B):
Total number of live births of the same sex in year z in the same country, territory, or geographic area.
Formula:
(A/B) x 1 000
Numerator (A):
Number of infant deaths in year z in a given country, territory or geographic area
Denominator (B):
Total number of live births in year z in the same country, territory or geographic area
To estimate the indicator by sex, the following formula is applied:
(A/B) x 1 000
Numerator (A):
Number of infant deaths of a given sex in year z in a specific country, territory, or geographic area.
Denominator (B):
Total number of live births of the same sex in year z in the same country, territory, or geographic area.
Interpretation - example
The infant mortality rate of country A for 2019 was 8.3 per 1 000 live births; that is, in that year eight children died before the age of 1 per 1 000 live births in country A.
Desagregation
By sex
Limitations
The infant mortality rate requires a civil registry system with good coverage, and births and deaths must be recorded in a timely manner in this system; otherwise, this indicator will not be sufficiently accurate.
The value of this indicator may differ from each country’s value due to methodological differences such as the application of algorithms to reassign ill-defined causes of death and methods to correct underreporting of births and deaths.
The value of this indicator may differ from each country’s value due to methodological differences such as the application of algorithms to reassign ill-defined causes of death and methods to correct underreporting of births and deaths.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Inflation (%)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
Inflation, as measured by the consumer price index, reflects the annual percentage change for an average consumer in the cost of purchasing a basket of goods and services that can be fixed or changed at specific intervals (e.g., annually) for a given national economy. To calculate the consumer price index, the Laspeyres formula is usually used.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Because inflation translates the increase in prices of goods and services, it is used to evaluate consumers' purchasing power in a given national economy and how it evolves over time.
It is one of the indicators used by central banks to set monetary policies, such as interest rates. Analysis of its value includes identifying whether the observed change is due to an increase in demand, the cost of raw materials, or the money supply, among other factors, and implementing measures aimed at controlling it.
Inflation is closely related to a country's fiscal policies, deficit, and unemployment level.
It is one of the indicators used by central banks to set monetary policies, such as interest rates. Analysis of its value includes identifying whether the observed change is due to an increase in demand, the cost of raw materials, or the money supply, among other factors, and implementing measures aimed at controlling it.
Inflation is closely related to a country's fiscal policies, deficit, and unemployment level.
Estimation method
The consumer price index is generally explicitly derived as the weighted arithmetic average of the current prices of goods and services in the fixed basket, obtained through recurring price surveys, according to their weights based on fixed values for the base period (Laspeyres formula), which are also obtained from household expenditure surveys. The growth rates of the consumer price index are estimated by the World Bank from the corresponding data provided by the United Nations System of National Accounts and using the least squares method.
Interpretation - example
The cumulative inflation of country A in 2019 was 3.2%. This means the average price of goods and services included in the basic basket of this country rose by 3.2% compared to the previous year.
Desagregation
No disaggregation
Limitations
Inflation is estimated based on the percentage change in the consumer price index, which measures the average cost of goods and services included in the basic household basket. The makeup of the household basket varies between countries and is subject to the income and consumption patterns of its inhabitants. When based on a survey, the accuracy of this indicator depends on the quality of the data obtained from the survey. Products sold in informal trade are not considered when calculating the consumer price index.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from: https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
Not applicable
References
World Bank Open Data. Available from: https://data.worldbank.org/
Injury deaths (%)
Domain
Mortality
Subdomain
Cause of death
Definition
Proportion of injury deaths during a specific calendar year, in the population in a certain country or geographic area, in relation to total estimated deaths for the same place and year. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
The proportion of injury deaths helps to quantify the relative importance of this group of pathologies in the mortality of a given population or geographic area.
The value of this indicator is related to the quality of health care provided for this type of event and the identification of higher-risk populations and geographic areas, suggesting the need for more detailed analysis.
Its result is applicable in the design, implementation, and evaluation of health, transportation, and public safety policies related to injury deaths and in the distribution of economic, human, and technological resources for the prevention of injury deaths, among others.
The value of this indicator is related to the quality of health care provided for this type of event and the identification of higher-risk populations and geographic areas, suggesting the need for more detailed analysis.
Its result is applicable in the design, implementation, and evaluation of health, transportation, and public safety policies related to injury deaths and in the distribution of economic, human, and technological resources for the prevention of injury deaths, among others.
Estimation method
To estimate the percentage of injury deaths, the number of deaths from this group of conditions is used as the numerator and the total number of deaths from all causes as the denominator.
The underlying causes of death correspond to International Classification of Diseases, Tenth Revision (ICD-10) codes V01 - Y89 (except X41 - X42, X44 - X45).
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, cause of death estimates is calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The underlying causes of death correspond to International Classification of Diseases, Tenth Revision (ICD-10) codes V01 - Y89 (except X41 - X42, X44 - X45).
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, cause of death estimates is calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
Interpretation - example
According to 2019 data, 5.4% of the deaths in country A were from injuries; that is, five out of every 100 deaths in that country were a result of some type of injury.
Desagregation
By sex
Limitations
The estimated value of this indicator may differ from each country’s calculations due to differences such as the use of a different group of ICD-10 codes or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Calculating the proportion of injury deaths requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in that system in a timely manner and certification of cause of death must be of good quality. This must include reporting deliberate acts resulting in death; otherwise, the estimates will not be sufficiently accurate.
Calculating the proportion of injury deaths requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in that system in a timely manner and certification of cause of death must be of good quality. This must include reporting deliberate acts resulting in death; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from:
https://www.who.int/data/global-health-estimates
https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global status report on road safety 2018. Geneva, 2018. Available from: https://www.who.int/publications/i/item/9789241565684
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global status report on road safety 2018. Geneva, 2018. Available from: https://www.who.int/publications/i/item/9789241565684
Ischemic heart diseases mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from ischemic heart diseases in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution of the population. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator reflects the health status and socio-economic development of a population and makes it possible to identify populations at greater risk of dying from ischemic heart diseases and to encourage research in this area.
Its result is applicable to the design, implementation, and evaluation of health policies on ischemic heart diseases and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, evaluating over time the effectiveness of interventions to promote healthy lifestyles or myocardial revascularization.
The age-adjusted ischemic heart diseases mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies on ischemic heart diseases and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, evaluating over time the effectiveness of interventions to promote healthy lifestyles or myocardial revascularization.
The age-adjusted ischemic heart diseases mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from ischemic heart diseases, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes I20 – I25 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted ischemic heart diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The ischemic heart diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes I20 – I25 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted ischemic heart diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The ischemic heart diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted ischemic heart diseases mortality rate for 2019 was 83 per 100 000 population in country A and 38 per 100 000 population in country B; that is, in 2019 ischemic heart diseases were responsible for the death of 83 people per 100 000 population in country A, compared to country B, where 38 people died from the same group of causes per 100 000 population. This means that, in 2019, the population of country A had a higher risk of dying from ischemic heart diseases than in country B.
Desagregation
By sex
Limitations
The age-adjusted ischemic heart diseases mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted ischemic heart diseases mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different group of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the ischemic heart diseases mortality rate requires a civil registry system with good coverage. Deaths from ischemic heart diseases must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted ischemic heart diseases mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different group of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the ischemic heart diseases mortality rate requires a civil registry system with good coverage. Deaths from ischemic heart diseases must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Kuznets ratio
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The ratio of the total cumulative income of the richest 20% of households (quintile 5) to the total cumulative income of the poorest 20% (quintile 1), ordered according to the distribution of household per capita income in a given population and over a specific period, usually one year.
Measurement Unit
Type of measurement
Ratio
Type of statistics
Corrected
Purpose
The Kuznets index is used to measure inequality in a country's income distribution; this inequality is reflected in the proportion of income held by a part of the population classified by income level.
Along with other macroeconomic indicators, such as the Gini coefficient, the Kuznets index is used to analyze changes in a country's economic inequality over time. It is used to design socioeconomic policies to address poverty.
Along with other macroeconomic indicators, such as the Gini coefficient, the Kuznets index is used to analyze changes in a country's economic inequality over time. It is used to design socioeconomic policies to address poverty.
Estimation method
The Kuznets ratio is calculated by PAHO based on estimates made by the World Bank according to data on income distribution or consumption from nationally representative household surveys. When original household survey data are available, they are used to directly calculate the share of income or consumption per quintile. If the original data are not available, figures are estimated from the best available pooled data. Distribution data are adjusted for household size, providing a more consistent measure of per capita income or consumption.
Formula:
(A/B) x 100
Numerator (A):
Total income of the richest quintile
Denominator (B):
Total income of the richest quintile
No adjustments are made for spatial differences in the cost of living within countries, because the data needed for such calculations are generally not available.
Formula:
(A/B) x 100
Numerator (A):
Total income of the richest quintile
Denominator (B):
Total income of the richest quintile
No adjustments are made for spatial differences in the cost of living within countries, because the data needed for such calculations are generally not available.
Interpretation - example
According to 2019 data, the Kuznets index of country A was 10.9. This means that the income of the highest income households (quintile 5) is almost 11 times that of the lowest income households (quintile 1).
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the timeliness, frequency, quality, and comparability of household surveys. The availability of data from these surveys affects use of this indicator to monitor poverty.
Low frequency and lack of comparability among surveys due to methodological differences are other factors that hinder monitoring poverty.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Low frequency and lack of comparability among surveys due to methodological differences are other factors that hinder monitoring poverty.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from: https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
Not applicable
References
World Bank Open Data. Available from: https://data.worldbank.org/
Leprosy cases in treatment
Domain
Morbidity
Subdomain
Communicable diseases
Definition
Number of leprosy cases registered for treatment (reported leprosy prevalence) as of 31 December of a given year in a specific country, territory, or geographical area.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Leprosy (Hansen disease) is a chronic infectious disease caused by Mycobacterium leprae. It mainly affects the skin, peripheral nerves, upper respiratory tract mucosa, and eyes. Despite its severity, the disease can be cured through multi-drug treatment. If treated in the early stages, all types of leprosy can be cured, and the associated disability prevented.
This indicator is used to monitor disease elimination. It helps identify vulnerable populations in need of strengthened national surveillance systems, improve detection among those affected, ensure treatment, and intensify leprosy elimination campaigns in line with the Global Leprosy (Hansen disease) Strategy 2021–2030.
Leprosy was eliminated globally as a public health problem (defined as a recorded prevalence of less than 1 case per 10 000 pop) in 2000.
This indicator is used to monitor disease elimination. It helps identify vulnerable populations in need of strengthened national surveillance systems, improve detection among those affected, ensure treatment, and intensify leprosy elimination campaigns in line with the Global Leprosy (Hansen disease) Strategy 2021–2030.
Leprosy was eliminated globally as a public health problem (defined as a recorded prevalence of less than 1 case per 10 000 pop) in 2000.
Estimation method
The data is primarily collected from national surveillance systems that regularly report to the Pan American Health Organization.
Interpretation - example
In country A, there were two cases of leprosy registered for treatment in 2019.
Desagregation
By sex
Limitations
This indicator depends on the effectiveness and coverage of national leprosy surveillance systems, as well as early detection and timely treatment.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Towards zero leprosy. Global leprosy (Hansen's disease) strategy 2021–2030. Available from: https://www.who.int/publications/i/item/9789290228509
WHO. Weekly Epidemiological Record. Global leprosy (Hansen disease) update, 2020: impact of COVID-19 on global leprosy control. Available from: https://www.who.int/publications/i/item/who-wer9636-421-444
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/data/themes/topics/leprosy-hansens-disease
World Health Organization (WHO). Elimination of leprosy as a public health problem. Resolution WHA51.15. May 1998. Available from: https://apps.who.int/iris/handle/10665/79814
WHO. Weekly Epidemiological Record. Global leprosy (Hansen disease) update, 2020: impact of COVID-19 on global leprosy control. Available from: https://www.who.int/publications/i/item/who-wer9636-421-444
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/data/themes/topics/leprosy-hansens-disease
World Health Organization (WHO). Elimination of leprosy as a public health problem. Resolution WHA51.15. May 1998. Available from: https://apps.who.int/iris/handle/10665/79814
Leptospirosis cases
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The number of registered cases of leptospirosis in the population of a given country, territory, or geographic area in one year. It includes cases of leptospirosis with clinical or epidemiological suspicion, as well as laboratory-confirmed cases.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Leptospirosis is a zoonotic disease caused by spirochetes of the genus Leptospira, which can cause clinical pictures with different degrees of organ involvement and even death. It has a worldwide distribution and is endemic mainly in countries with humid tropical or subtropical climates that have epidemic potential. In addition to humans, it affects several species of domestic and wild mammals, which are its reservoir. Occasionally, outbreaks associated with the ingestion of contaminated water or food may occur. Early detection and administration of antibiotics allows the disease to be treated successfully.
The number of cases of leptospirosis makes it possible to quantify the magnitude of this disease in the population, analyze its temporal and geographic trend, and identify areas and groups at greatest risk for transmission. This indicator contributes to the design and evaluation of strategies for the prevention and control of leptospirosis in the community and to the allocation of resources to control it.
The number of cases of leptospirosis makes it possible to quantify the magnitude of this disease in the population, analyze its temporal and geographic trend, and identify areas and groups at greatest risk for transmission. This indicator contributes to the design and evaluation of strategies for the prevention and control of leptospirosis in the community and to the allocation of resources to control it.
Estimation method
The number of cases of leptospirosis is from data officially reported to the Pan American Health Organization (PAHO) by the Ministries of Health of the affected countries. These data are mostly collected through leptospirosis surveillance systems and national technical programs.
Interpretation - example
Country A had 95 registered cases of leptospirosis in 2019.
Desagregation
By sex
Limitations
This indicator’s value depends on the coverage and performance of surveillance systems and the coordination of efforts across the multiple sectors involved in addressing leptospirosis. One of the main factors affecting the accuracy of this indicator is that leptospirosis is not reported in many countries due to the difficulty of clinical diagnosis and the lack of laboratories to diagnose it. Another element to consider is that its multiple clinical manifestations result in a low diagnostic suspicion and, consequently, an undercount of cases.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). PLISA Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Human leptospirosis: guidance for diagnosis, surveillance, and control. Geneva, 2003. Available from:
https://apps.who.int/iris/handle/10665/42667
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Human leptospirosis: guidance for diagnosis, surveillance, and control. Geneva, 2003. Available from:
https://apps.who.int/iris/handle/10665/42667
Life expectancy at birth (years)
Domain
Sociodemographic
Subdomain
Demographic
Definition
The average number of years that a newborn is expected to live, as a member of a hypothetical birth cohort, if exposed to the age- and sex-specific mortality rates prevailing at birth, for a specific year, in a given country, territory, or geographic area.
Measurement Unit
Years
Type of measurement
Index
Type of statistics
Corrected/Predicted
Purpose
Life expectancy at birth reflects the overall mortality level of a population. It reflects the intensity of mortality in different age groups for the same calendar year and geographical location as well as the living and health conditions of a population.
Unlike the crude mortality rate, it is independent of the population's age structure, which allows comparisons to be made between different populations and within the same population over time.
Unlike the crude mortality rate, it is independent of the population's age structure, which allows comparisons to be made between different populations and within the same population over time.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Life tables are used in the multi-step calculation of life expectancy at birth, based on age- and sex- specific mortality rates. Their values represent mid-year estimates.
Life tables are used in the multi-step calculation of life expectancy at birth, based on age- and sex- specific mortality rates. Their values represent mid-year estimates.
Interpretation - example
The life expectancy of country A in 2018 was 75.8 years. This means that, if the age-specific mortality rates observed in 2018 did not change, people born that year in the country would live an average of 75.8 years.
Desagregation
By sex
Limitations
Life expectancy is a hypothetical measure whose calculation is based on national mortality rates. Therefore, it depends on the quality of information sources used to develop life tables as inputs to calculate life expectancy at birth.
Whenever vital records systems with full coverage are not available or the accuracy of the data is inadequate, life expectancy at birth should be estimated using indirect demographic methods, applicable to large geographical areas.
Life expectancy at birth may differ depending on the method used to draw up the life table.
Life expectancy changes as a person ages, changing according to potential shifts in mortality trends and the health conditions of the population in which they live.
Whenever vital records systems with full coverage are not available or the accuracy of the data is inadequate, life expectancy at birth should be estimated using indirect demographic methods, applicable to large geographical areas.
Life expectancy at birth may differ depending on the method used to draw up the life table.
Life expectancy changes as a person ages, changing according to potential shifts in mortality trends and the health conditions of the population in which they live.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 2. Health-adjusted life expectancy (HALE)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 2. Health-adjusted life expectancy (HALE)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from: https://www.prb.org/population-handbook/
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from: https://www.prb.org/population-handbook/
Low birthweight (<2 500 g) (%)
Domain
Risk factor
Subdomain
Child health
Definition
Percentage of live births with a birth weight of less than 2 500 grams (up to and including 2 499 grams), compared to the total number of live births, in the same country, territory, or geographical area and year. Expressed per 100 live births.
The newborn’s weight measured for the first time after birth is known as birth weight. With live births, birth weight should be taken during the first hour of life, before significant postpartum weight loss occurs.
The newborn’s weight measured for the first time after birth is known as birth weight. With live births, birth weight should be taken during the first hour of life, before significant postpartum weight loss occurs.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
The percentage of live births with low birth weight reflects the health status of a population. It is associated with maternal malnutrition and poor medical care during pregnancy, among other factors, and is an important predictor of fetal mortality and newborn health and survival. In later stages, it is associated with impaired cognitive development and noncommunicable diseases, such as diabetes mellitus or cardiovascular diseases.
This indicator is related to the quality of maternal and child health care and social vulnerability. It allows at-risk populations to be identified and focuses strategies to strengthen access to timely and quality health and nutritional interventions. Research is incentivized to identify risk factors for low birth weight and propose appropriate interventions.
The percentage of low-weight newborns is part of the indicators of the Global Nutrition Monitoring Framework on Maternal, Infant, and Young Child Nutrition. These indicators monitor progress made by countries towards global nutrition targets. They also allow monitoring of the Monitoring Framework for Universal Health in the Americas, to measure national progress on implementing policies aimed at strengthening health systems and achieving universal health.
This indicator is related to the quality of maternal and child health care and social vulnerability. It allows at-risk populations to be identified and focuses strategies to strengthen access to timely and quality health and nutritional interventions. Research is incentivized to identify risk factors for low birth weight and propose appropriate interventions.
The percentage of low-weight newborns is part of the indicators of the Global Nutrition Monitoring Framework on Maternal, Infant, and Young Child Nutrition. These indicators monitor progress made by countries towards global nutrition targets. They also allow monitoring of the Monitoring Framework for Universal Health in the Americas, to measure national progress on implementing policies aimed at strengthening health systems and achieving universal health.
Estimation method
The percentage of newborns with low birth weight is provided by national health agencies to the Pan American Health Organization (PAHO), based on information sources that vary depending on the situation of each country, including national health information systems, national vital statistics, demographic and health surveys (DHS), and health services or facilities records, among other sources.
Formula:
(A/B) x 100 live births
Numerator (A):
Number of live newborns with birth weight lower than 2 500 g, in a given country, country, territory, or geographical area and year.
Denominator (B):
Total number of live births in the same country and year.
Formula:
(A/B) x 100 live births
Numerator (A):
Number of live newborns with birth weight lower than 2 500 g, in a given country, country, territory, or geographical area and year.
Denominator (B):
Total number of live births in the same country and year.
Interpretation - example
According to 2019 data, the percentage of low birth weight in country A is 9.8%, which means, of every 100 live births in this country, 10 have a birth weight of less than 2 500 g.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the quality of the data used for calculations and the availability of information sources. The fact that a high percentage of newborns are not weighed at birth is a major limiting factor. It’s also worth noting that calculations require timely recording of live births in a system with adequate coverage.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C. 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Indicators for the Global Monitoring Framework on Maternal, Infant and Young Child Nutrition, 2014. Available from:
https://www.who.int
ews-room/events/detail/2014/11/24/default-calendar/indicators-for-the-global-monitoring-framework-on-maternal-infant-and-young-child-nutrition
World Health Organization (WHO). Maternal, infant and young child nutrition, March 2018. Available from:
https://apps.who.int/gb/ebwha/pdf_files/WHA71/A71_22-en.pdf
UNICEF Data: Monitoring the situation of children and women. Available from: https://data.unicef.org/
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Indicators for the Global Monitoring Framework on Maternal, Infant and Young Child Nutrition, 2014. Available from:
https://www.who.int
ews-room/events/detail/2014/11/24/default-calendar/indicators-for-the-global-monitoring-framework-on-maternal-infant-and-young-child-nutrition
World Health Organization (WHO). Maternal, infant and young child nutrition, March 2018. Available from:
https://apps.who.int/gb/ebwha/pdf_files/WHA71/A71_22-en.pdf
UNICEF Data: Monitoring the situation of children and women. Available from: https://data.unicef.org/
Lower respiratory infection mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from lower respiratory infections in the population in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution of the population. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator reflects a population’s health status and makes it possible to identify those with greater risk factors for dying from lower respiratory infections and to encourage research in this area.
Its result is applicable to the design, implementation, and evaluation of health policies on lower respiratory infections and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, analyzing the effect of immunoprevention interventions and strategies for promoting healthy lifestyles on mortality from lower respiratory infections.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies on lower respiratory infections and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, analyzing the effect of immunoprevention interventions and strategies for promoting healthy lifestyles on mortality from lower respiratory infections.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from lower respiratory infections, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes J09 - J22, P23, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted lower respiratory infection mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The lower respiratory infection mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes J09 - J22, P23, U04 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted lower respiratory infection mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The lower respiratory infection mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted lower respiratory infection mortality rate for 2019 was 25 per 100 000 population in country A and 12 per 100 000 population in country B; that is, in 2019 lower respiratory infections were responsible for the death of 25 people per 100 000 population of country A, compared to country B, where 12 people died from the same group of causes per 100 000 population. This means that, after removing the effect of differences in the age distribution between the two countries, the risk of dying from lower respiratory infections in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted lower respiratory infection mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted lower respiratory infection mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different range of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the lower respiratory infection mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted lower respiratory infection mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different range of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the lower respiratory infection mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Lung cancer incidence rate (age-adjusted per 100 000 pop)
Domain
Morbidity
Subdomain
Cancer
Definition
The ratio of new cases of trachea, bronchial, and lung cancer (ICD-10 codes: C33 - C34) arising in a given country, territory, or geographical area during a specific period (usually one year), to the total population of the same country and year. This indicator is age-standardized to control for the effect of different age structures.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Predicted
Purpose
Alongside mortality and prevalence estimates, lung cancer incidence rates provide a comprehensive assessment of the impact of the disease in 185 countries or territories, including the Region of the Americas.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of these diseases in the population.
The age-adjusted trachea, bronchial, and lung cancer incidence rate provides an estimate of the average risk the population has of developing these types of cancer in a particular country, territory, or geographic area. Its results are used to planning and allocating economic, human, and technological resources to fight lung cancer. It can be applied, for example, to evaluate the effectiveness of smoking restrictions.
As the trachea, bronchial, and lung cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. It helps to identify at-risk groups, determine the presence of specific risk factors for different groups, and assess timely access to diagnosis and treatment.
This indicator supports public policy-making and is essential for planning and evaluating trachea, bronchial, and lung cancer screening, early diagnosis, treatment, and control programs. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of these diseases in the population.
The age-adjusted trachea, bronchial, and lung cancer incidence rate provides an estimate of the average risk the population has of developing these types of cancer in a particular country, territory, or geographic area. Its results are used to planning and allocating economic, human, and technological resources to fight lung cancer. It can be applied, for example, to evaluate the effectiveness of smoking restrictions.
As the trachea, bronchial, and lung cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. It helps to identify at-risk groups, determine the presence of specific risk factors for different groups, and assess timely access to diagnosis and treatment.
This indicator supports public policy-making and is essential for planning and evaluating trachea, bronchial, and lung cancer screening, early diagnosis, treatment, and control programs. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
Estimation method
Data for this indicator come from national or subnational population cancer registries that are routinely maintained in the Global Cancer Observatory (GCO) 2020: Cancer Incidence in Five Continents (CI5) database.
The methods used to estimate the trachea, bronchial, and lung cancer incidence rate in a specific country depend on the available data sources. These include projections of observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The trachea, bronchial, and lung cancer incidence rate is age-standardized and results are based on rates in populations with a standard age structure.
The age-standardized rate is a summary measure of the rate that would have been observed in a population with a standard age structure. Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
The methods used to estimate the trachea, bronchial, and lung cancer incidence rate in a specific country depend on the available data sources. These include projections of observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The trachea, bronchial, and lung cancer incidence rate is age-standardized and results are based on rates in populations with a standard age structure.
The age-standardized rate is a summary measure of the rate that would have been observed in a population with a standard age structure. Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
Interpretation - example
In 2019, the lung cancer incidence rate in country A was 24.8 per 100 000 pop. In country B, it was 48.9 per 100 000. In other words, the probability of developing lung cancer in country A in 2019 was 24.8 per 100 000 pop. The risk of developing these types of cancer in country B is twice as high as in country A.
Desagregation
By sex
Limitations
The data presented at the Global Cancer Observatory are the best available for each country. However, the indicator should be interpreted with caution considering current limitations in the quality and coverage of cancer data, particularly in low- and middle-income countries.
In addition, the age-adjusted trachea, bronchial, and lung cancer incidence rate is not a real value. Its main purpose is to allow comparisons over time between different populations or within the same population. It reflects the average risk of developing these types of cancer in a particular country. This should not be interpreted as the individual risk of developing these cancers.
The estimated value of the age-adjusted trachea, bronchial, and lung cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
In addition, the age-adjusted trachea, bronchial, and lung cancer incidence rate is not a real value. Its main purpose is to allow comparisons over time between different populations or within the same population. It reflects the average risk of developing these types of cancer in a particular country. This should not be interpreted as the individual risk of developing these cancers.
The estimated value of the age-adjusted trachea, bronchial, and lung cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
Data source(s)
Global Cancer Observatory (GCO). Cancer Incidence in Five Continents (CI5). Available from: https://ci5.iarc.fr/CI5plus/Default.aspx
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
International Agency for Research on Cancer (IARC), CANCER TODAY. Available from: https://gco.iarc.fr/today/home
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from:
https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/handle/10665/259951
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from:
https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Available from: https://apps.who.int/iris/handle/10665/259951
Lung cancer mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from cancer of the trachea, bronchus and lung in the population, in a given country, territory, or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from cancer of the trachea, bronchus and lung and to assess the presence of risk factors, such as those associated with environment or lifestyle.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of cancer of the trachea, bronchus and lung and the distribution of economic, human, and technological resources for this disease, among others. Its applications include, for example, evaluating the effectiveness of smoking restriction measures in closed spaces.
Removing the effect of a different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from cancer of the trachea, bronchus and lung, in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of cancer of the trachea, bronchus and lung and the distribution of economic, human, and technological resources for this disease, among others. Its applications include, for example, evaluating the effectiveness of smoking restriction measures in closed spaces.
Removing the effect of a different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from cancer of the trachea, bronchus and lung, in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from cancer of the trachea, bronchus and lung from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes C33 – C34 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the mortality rate from cancer of lung are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The mortality rate from cancer of lung is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes C33 – C34 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the mortality rate from cancer of lung are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The mortality rate from cancer of lung is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted lung cancer mortality rate for 2019 is 18 per 100 000 population in country A and 11 per 100 000 population in country B; that is, in that year 18 people died from cancer of the trachea, bronchus and lung per 100 000 population of country A, compared to country B, where 11 people died from that cause per 100 000 population. This means that, after removing the effect of differences in the age structure in the two countries, the population of country A had a higher risk of dying from lung cancer in 2019.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted mortality rate from cancer of the lung will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections, the use of a different range of ICD-10 codes, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate from cancer of the lung requires a civil registry system with good coverage. Deaths must be recorded in this system in a timely manner, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted mortality rate from cancer of the lung will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections, the use of a different range of ICD-10 codes, or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the mortality rate from cancer of the lung requires a civil registry system with good coverage. Deaths must be recorded in this system in a timely manner, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Malaria cases
Domain
Morbidity
Subdomain
Communicable diseases
Definition
Number of confirmed cases of malaria in a given year in a specific country, territory, or geographical area.
A case of malaria is defined as the presence of malarial parasite infection in a person's blood, confirmed by diagnostic examination.
A case of malaria is defined as the presence of malarial parasite infection in a person's blood, confirmed by diagnostic examination.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Malaria is an often-deadly parasitic disease, borne by female Anopheles mosquitoes. Despite the associated risk, especially for children, the disease is preventable and curable. Prevention is based on anti-vector measures, such as insecticide-treated nets and intra-household residual spraying, and prophylactic treatment, only in case of travel to an endemic area, because it can stop the infection in its blood stage, preventing the disease. The second pillar of malaria control is early diagnosis and treatment to reduce incidence, prevent deaths, and contribute to reducing transmission.
This indicator is used to monitor countries progressing towards targets established in the Global Technical Strategy for Malaria 2016–2030. It also contributes to monitoring national progress on the objectives of the Monitoring Framework for Universal Health in the Americas.
The indicator makes it possible to assess the temporal and geographical trends of malaria and identify at-risk populations in need of strengthened surveillance programs, malaria elimination activities, and access to interventions, especially integrated and quality diagnosis and treatment. The value of the indicator is considered when allocating economic, human, and technological resources.
In the Americas, there are 18 endemic countries and territories (as of 2021):
Belize, Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, French Guiana, Guatemala, Guyana, Haiti, Honduras, Mexico, Nicaragua, Panama, Peru, Suriname, and Venezuela.
This indicator is used to monitor countries progressing towards targets established in the Global Technical Strategy for Malaria 2016–2030. It also contributes to monitoring national progress on the objectives of the Monitoring Framework for Universal Health in the Americas.
The indicator makes it possible to assess the temporal and geographical trends of malaria and identify at-risk populations in need of strengthened surveillance programs, malaria elimination activities, and access to interventions, especially integrated and quality diagnosis and treatment. The value of the indicator is considered when allocating economic, human, and technological resources.
In the Americas, there are 18 endemic countries and territories (as of 2021):
Belize, Bolivia, Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, French Guiana, Guatemala, Guyana, Haiti, Honduras, Mexico, Nicaragua, Panama, Peru, Suriname, and Venezuela.
Estimation method
The number of cases in malaria-endemic countries is collected from each country's surveillance system. This information is reported annually to the World Health Organization in the World Malaria Report.
Information from non-endemic countries is reported directly to PAHO/WHO and classified as 'imported'.
Imported cases are transmitted by mosquitoes and contracted outside the area where diagnosis took place, in an area known to have malaria transmission, or areas visited by the patient outside the elimination zone.
Information from non-endemic countries is reported directly to PAHO/WHO and classified as 'imported'.
Imported cases are transmitted by mosquitoes and contracted outside the area where diagnosis took place, in an area known to have malaria transmission, or areas visited by the patient outside the elimination zone.
Interpretation - example
In country A, there were 15 cases of malaria in 2019.
Desagregation
By sex
Limitations
The value of this indicator is impacted by the effectiveness of surveillance systems, which in turn may be affected by low diagnostic suspicion and underreporting of cases, the availability of laboratory tests for diagnosis, and whether private health centers report identified cases.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG)
Indicator 3.3.3: Malaria incidence per 1 000 population
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 21. Incidence rate of malaria
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Sustainable Development Goals (SDG)
Indicator 3.3.3: Malaria incidence per 1 000 population
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 21. Incidence rate of malaria
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO). Malaria surveillance, monitoring & evaluation: a reference manual. Washington, D.C., 2018. Available from: https://iris.paho.org/bitstream/handle/10665.2/50648/9789275320563_spa.pdf?ua=1
World Health Organization (WHO). The Seventy-fourth World Health Assembly. Recommitting to accelerate progress towards malaria elimination. Draft Resolution A74/B/CONF./2. May 2021. Available from: https://apps.who.int/gb/ebwha/pdf_files/WHA74/A74_BCONF2-en.pdf
World Health Organization (WHO). WHO malaria terminology. Geneva, 2022. Updated in November 2021. Available from: https://www.who.int/publications/i/item/9789240038400
Global Technical Strategy for Malaria 2016–2030. Available from: https://www.paho.org/en/documents/global-technical-strategy-malaria-2016-2030
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C.; 2021. License: CC BY-NC-SA 3.0 IGO. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). The Seventy-fourth World Health Assembly. Recommitting to accelerate progress towards malaria elimination. Draft Resolution A74/B/CONF./2. May 2021. Available from: https://apps.who.int/gb/ebwha/pdf_files/WHA74/A74_BCONF2-en.pdf
World Health Organization (WHO). WHO malaria terminology. Geneva, 2022. Updated in November 2021. Available from: https://www.who.int/publications/i/item/9789240038400
Global Technical Strategy for Malaria 2016–2030. Available from: https://www.paho.org/en/documents/global-technical-strategy-malaria-2016-2030
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C.; 2021. License: CC BY-NC-SA 3.0 IGO. Available from: https://iris.paho.org/handle/10665.2/53918
Malignant neoplasms mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from malignant neoplasms in the population, in a given country, territory, or geographic area during a specific year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from malignant neoplasms and to evaluate the presence of potential risk factors, such as those associated with diet, environment, or lifestyle.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of malignant neoplasms and the distribution of economic, human, and technological resources, among others. Its applications include evaluating screening and early diagnosis strategies over time.
Removing the effect of differences in the age distribution by using a single standard population makes it possible to analyze the time trend and geographic distribution of deaths from malignant neoplasms in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of malignant neoplasms and the distribution of economic, human, and technological resources, among others. Its applications include evaluating screening and early diagnosis strategies over time.
Removing the effect of differences in the age distribution by using a single standard population makes it possible to analyze the time trend and geographic distribution of deaths from malignant neoplasms in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from malignant neoplasms from the World Health Organization (WHO) Global Health Estimates (GHE) and the civil registry and national vital statistics systems of the countries in the Region of the Americas.
The underlying causes of death correspond to codes C00 – C97 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the malignant neoplasms mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The malignant neoplasms mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes C00 – C97 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the malignant neoplasms mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The malignant neoplasms mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted malignant neoplasms mortality rate for 2019 is 12 per 100 000 population in country A and 6 per 100 000 population in country B; that is, in country A 12 people died from a malignant neoplasm per 100 000 population, compared to country B, where six died per 100 000 population. This means that, after removing the effect of differences in the age structure in the two countries, the risk of dying from malignant neoplasms in 2019 was higher in country A.
Desagregation
By sex
Limitations
The age-adjusted malignant neoplasms mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
Due to the application of advanced statistical models, estimates may differ from each country’s results. The estimated value of the age-adjusted malignant neoplasms mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different range of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the malignant neoplasms mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Due to the application of advanced statistical models, estimates may differ from each country’s results. The estimated value of the age-adjusted malignant neoplasms mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different range of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the malignant neoplasms mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Maternal mortality ratio (100 000 lb)
Domain
Mortality
Subdomain
Maternal and reproductive health
Definition
The ratio between the number of maternal deaths and the number of live births in a given country, territory, or geographical area in a specific calendar year. Expressed per 100 000 live births.
Maternal death is defined as the death of a woman while pregnant or within 42 days following termination of pregnancy, regardless of the duration and anatomical site of the pregnancy, related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) (ICD-10).
Live birth refers to the complete expulsion or extraction from its mother of a product of conception, regardless of the duration of the pregnancy, which, after such separation, breathes or shows any other evidence of life, (e.g., beating heart, pulsating umbilical cord, or definite movement of voluntary muscles) whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered as a live birth. (ICD-10)
Maternal death is defined as the death of a woman while pregnant or within 42 days following termination of pregnancy, regardless of the duration and anatomical site of the pregnancy, related to or aggravated by pregnancy or its management (excluding accidental or incidental causes) (ICD-10).
Live birth refers to the complete expulsion or extraction from its mother of a product of conception, regardless of the duration of the pregnancy, which, after such separation, breathes or shows any other evidence of life, (e.g., beating heart, pulsating umbilical cord, or definite movement of voluntary muscles) whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered as a live birth. (ICD-10)
Measurement Unit
100 000 live births
Type of measurement
Ratio
Type of statistics
Crude/Corrected
Purpose
Maternal mortality (SDG 3.1) remains a high priority on the world's health agenda.
The maternal mortality ratio reflects the magnitude of maternal mortality, increases its visibility as a serious public health problem, and contributes to raising societal awareness about the issue. It reflects a population's state of health, as well as its socioeconomic and health development. It allows inequities in health and higher-risk populations to be identified, and highlights the need to focus economic, human, and technological resources to respond appropriately to deficiencies detected.
Its value reflects the risk that a woman has of dying from complications related to pregnancy, childbirth, or the postpartum period, and encourages research in maternal and child issues.
This indicator is associated with access to and quality of women's health care, including family planning, antenatal care, attendance at childbirth, and the postpartum period. It identifies the need for specialized health personnel to prevent and treat complications that occur during pregnancy and childbirth.
The maternal mortality ratio reflects the magnitude of maternal mortality, increases its visibility as a serious public health problem, and contributes to raising societal awareness about the issue. It reflects a population's state of health, as well as its socioeconomic and health development. It allows inequities in health and higher-risk populations to be identified, and highlights the need to focus economic, human, and technological resources to respond appropriately to deficiencies detected.
Its value reflects the risk that a woman has of dying from complications related to pregnancy, childbirth, or the postpartum period, and encourages research in maternal and child issues.
This indicator is associated with access to and quality of women's health care, including family planning, antenatal care, attendance at childbirth, and the postpartum period. It identifies the need for specialized health personnel to prevent and treat complications that occur during pregnancy and childbirth.
Estimation method
Measurement of maternal mortality requires information on pregnancy status, time of death (during pregnancy, childbirth, or within 42 days of termination of pregnancy), and cause of death.
The maternal mortality ratio is calculated using as a numerator the deaths of women occurring during pregnancy, childbirth, or the postpartum period, reported by the national health authority of a given country, territory, or geographical area in a specific calendar year, and as denominator the total number of live births for the same population and year. [ICD-10: A34, O00-O95, O98-O99]
Formula:
(A/B) x 100 000
Numerator (A):
Number of maternal deaths (direct or indirect obstetric causes) in a given country, territory or geographical area in year z
Denominator (B):
Number of maternal deaths (direct or indirect obstetric causes) in a given country, territory or geographical area in year z
For the purpose of international reporting of maternal mortality, only those maternal deaths occurring before the end of the 42-day reference period should be included in the calculation of the various ratios and rates.
It is recommended to monitor late maternal deaths at the national level.
The maternal mortality ratio is calculated using as a numerator the deaths of women occurring during pregnancy, childbirth, or the postpartum period, reported by the national health authority of a given country, territory, or geographical area in a specific calendar year, and as denominator the total number of live births for the same population and year. [ICD-10: A34, O00-O95, O98-O99]
Formula:
(A/B) x 100 000
Numerator (A):
Number of maternal deaths (direct or indirect obstetric causes) in a given country, territory or geographical area in year z
Denominator (B):
Number of maternal deaths (direct or indirect obstetric causes) in a given country, territory or geographical area in year z
For the purpose of international reporting of maternal mortality, only those maternal deaths occurring before the end of the 42-day reference period should be included in the calculation of the various ratios and rates.
It is recommended to monitor late maternal deaths at the national level.
Interpretation - example
The maternal mortality ratio in the Americas is 52 maternal deaths per 100 000 live births. The maternal mortality ratio of country A is 350 per 100 000 live births. This means that in country A the risk of dying due to complications occurring during pregnancy, childbirth, or the postpartum period is almost seven times greater.
Desagregation
No disaggregation
Limitations
Maternal mortality is difficult to measure and can be subject to significant underreporting. Obtaining reliable data is a challenge for many countries. Estimating maternal deaths requires a civil registry system with good coverage that records in- and out-of-hospital maternal deaths in a timely manner, and a good quality medical certificate indicating the cause of death. Otherwise, the accuracy of the estimates will be inadequate.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.1.1: Maternal mortality ratio
SDG Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100 000 live births.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 6. Maternal mortality ratio (MMR) (deaths per 100,000 live births)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.1.1: Maternal mortality ratio
SDG Target 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100 000 live births.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 6. Maternal mortality ratio (MMR) (deaths per 100,000 live births)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from:
https://apps.who.int/iris/handle/10665/42980
https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from:
https://apps.who.int/iris/handle/10665/42980
Mean years of schooling (years)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The average number of years of schooling completed by the adult population age 25 or older in a given country, territory, or geographical area during a specific period, usually one year, excluding years spent repeating individual grades.
Measurement Unit
Years of study
Type of measurement
Magnitude
Type of statistics
Corrected
Purpose
Mean years of schooling allows analysis of the impact of public policies aimed at strengthening education. As this indicator summarizes in a single number the educational level of the general population, it allows comparisons to be made between countries and within a country over time.
It allows evaluation of temporal and geographical trends in schooling between different populations and identification of at-risk groups in order to focus strategies to strengthen education. It is related to a country's educational system coverage and helps characterize its human capital. It is associated with a population's access to employment and income level.
As parents' educational level is directly related to the health status of their children, this is one of the indicators used to analyze child health and to plan strategies and interventions for health promotion, protection, and recovery.
It allows evaluation of temporal and geographical trends in schooling between different populations and identification of at-risk groups in order to focus strategies to strengthen education. It is related to a country's educational system coverage and helps characterize its human capital. It is associated with a population's access to employment and income level.
As parents' educational level is directly related to the health status of their children, this is one of the indicators used to analyze child health and to plan strategies and interventions for health promotion, protection, and recovery.
Estimation method
The average number of years of schooling in the 25-year-old population comes from estimates made by the United Nations Educational, Scientific, and Cultural Organization (UNESCO), mainly using data from national population censuses and household/active population surveys. The definition of this indicator is in line with the revised recommendations on the International Standardization of Educational Statistics, adopted by UNESCO.
Interpretation - example
According to 2019 data, the average number of years of schooling of the 25-and-over population residing in country A is 8.5 years.
Desagregation
By sex
Limitations
The indicator quantifies the population's educational attainment in a single number, which should not be interpreted as the average duration of education.
Because the World Bank obtains income data through household surveys, the accuracy of this indicator is affected by the timeliness, frequency, quality, and comparability of the surveys used.
Because the World Bank obtains income data through household surveys, the accuracy of this indicator is affected by the timeliness, frequency, quality, and comparability of the surveys used.
Data source(s)
United Nations Educational, Scientific, and Cultural Organization (UNESCO). Institute for Statistics. Available from:
http://uis.unesco.org/
http://uis.unesco.org/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
UNESCO Education. Available from: https://www.unesco.org/en/education
Measles cases
Domain
Morbidity
Subdomain
Communicable diseases
Definition
Number of laboratory, clinically, or epidemiologically confirmed measles cases in a specific country, territory, or geographic area in a given year.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Measles is a highly contagious disease that can cause serious illness, lifelong complications, and death. It is caused by a virus in the paramyxovirus family and is usually transmitted through direct contact and by air (coughing and sneezing). The virus in the air or on infected surfaces remains active and contagious for up to two hours. An infected person can transmit the virus from four days before the rash appears to four days after. This virus can produce epidemics, especially in unvaccinated or malnourished children.
The number of measles cases quantifies the magnitude of this disease as a public health problem for a given population or geographic area. This indicator makes it possible to monitor the elimination of the disease, identify vulnerable populations, strengthen national surveillance systems, and enhance the identification of sick people to prevent further transmission through urgent vaccination of the population.
Monitoring this indicator tracks the progress of the Global Measles and Rubella Strategic Plan.
This indicator’s result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of measles and the distribution of economic, human, and technological resources for this disease. Its applications include planning and evaluation of measles vaccination programs and prioritization of health care quality.
The number of measles cases quantifies the magnitude of this disease as a public health problem for a given population or geographic area. This indicator makes it possible to monitor the elimination of the disease, identify vulnerable populations, strengthen national surveillance systems, and enhance the identification of sick people to prevent further transmission through urgent vaccination of the population.
Monitoring this indicator tracks the progress of the Global Measles and Rubella Strategic Plan.
This indicator’s result is applicable to the design, implementation, and evaluation of health policies for the prevention and control of measles and the distribution of economic, human, and technological resources for this disease. Its applications include planning and evaluation of measles vaccination programs and prioritization of health care quality.
Estimation method
The data collected are mostly from national surveillance systems and are regularly reported to the Pan American Health Organization.
Interpretation - example
In country A, there were 11 cases of measles in 2019.
Desagregation
By sex
Limitations
This indicator is affected by the performance and coverage of national measles surveillance systems, which in turn may be hampered by low early detection and underreporting of cases or untimely reporting.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Measles; 2019. Available from: https://www.who.int
ews-room/fact-sheets/detail/measles
World Health Organization (WHO). Global Measles and Rubella Strategic Plan 2012–2020. Available from: http://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236_eng.pdf?sequence=1
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Measles; 2019. Available from: https://www.who.int
ews-room/fact-sheets/detail/measles
World Health Organization (WHO). Global Measles and Rubella Strategic Plan 2012–2020. Available from: http://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236_eng.pdf?sequence=1
Median age (years)
Domain
Sociodemographic
Subdomain
Demographic
Definition
The age dividing the population into two groups of equal size, meaning that there are as many people above the median age as people under the median.
Measurement Unit
Years
Type of measurement
Index
Type of statistics
Corrected/Predicted
Purpose
This indicator highlights a population’s age distribution. It helps to assess demographic trends and changes, such as aging in a country or geographic area. It is used to develop and assess health and social security policies, among other purposes.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
The median age of a country in 2018 was 28.5 years. This means that, for that year, the estimated number of inhabitants under 28.5 years old was the same as those above that age.
In 2018, the median age in country A was 29 years. In country B, it was 35 years. This indicates that the latter country has an older population.
In 2018, the median age in country A was 29 years. In country B, it was 35 years. This indicates that the latter country has an older population.
Desagregation
No disaggregation
Limitations
The international comparability of this indicator may be limited by factors such as the quality of population censuses, demographic surveys, and national civil registration systems used to calculate estimates. One of these factors is the accuracy of age reporting.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
U. S. Census Bureau. International Programs, Glossary. Disponible en:
https://www.census.gov/programs-surveys/international-programs/about/glossary.html
United Nations, Department of Economic and Social Affairs, Statistics Division. Demographic and Social Statistics. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
United Nations, Department of Economic and Social Affairs, Statistics Division. Demographic and Social Statistics. Statistical Products and Databases. Available from:
https://unstats.un.org/unsd/demographic-social/products/
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from:
https://www.prb.org/population-handbook/
https://www.census.gov/programs-surveys/international-programs/about/glossary.html
United Nations, Department of Economic and Social Affairs, Statistics Division. Demographic and Social Statistics. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
United Nations, Department of Economic and Social Affairs, Statistics Division. Demographic and Social Statistics. Statistical Products and Databases. Available from:
https://unstats.un.org/unsd/demographic-social/products/
Haupt, A., Kane, T., Haub C. Population Reference Bureau’s Population Handbook (Sixth Edition) Washington, D.C. 2011. Available from:
https://www.prb.org/population-handbook/
Midwives (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Midwife density is defined as the total number of practising professional midwives, with formal education, in health facilities as of 31 December, per 10 000 population in a given country, territory or geographic area.
Professionals who plan, manage, provide and evaluate midwifery care and services before, during and after pregnancy and childbirth, both in the health services and in the community.
The International Standard Classification of Occupations code for this category is 2222 and 3222 (2008 revision). Physicians and nursing professionals are not included.
Professionals who plan, manage, provide and evaluate midwifery care and services before, during and after pregnancy and childbirth, both in the health services and in the community.
The International Standard Classification of Occupations code for this category is 2222 and 3222 (2008 revision). Physicians and nursing professionals are not included.
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Professional childbirth care, as well as antenatal care, are key elements in reducing maternal and infant morbidity and mortality in a population. This indicator makes it possible to identify inequalities in the distribution of health resources and prioritize of human and economic resource allocation to specific populations. Its value is used to develop public policies to increase funding for maternal and child health, and to train, hire, and retrain skilled workers in this area.
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of midwives reported by the countries of the Americas to the Pan American Health Organization (PAHO). This data is collected from secondary sources, such as health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/)
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/)
Interpretation - example
According to 2019 data, the density of professional midwives in country A was 3.8 per 10 000 population. This means that, this country had 3.8 professional midwives per 10 000 population in 2019.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working professional midwives, or include all registered or licensed, even if their employment status is unknown.
It is important to consider that there are countries in the Region that do not have this professional.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working professional midwives, or include all registered or licensed, even if their employment status is unknown.
It is important to consider that there are countries in the Region that do not have this professional.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
International Confederation of Midwives (ICM). Available from: https://www.internationalmidwives.org/
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
International Confederation of Midwives (ICM). Available from: https://www.internationalmidwives.org/
Mortality attributable to household and ambient air pollution (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Environment
Definition
Deaths attributable to the joint effects of household and ambient air pollution on the population of a given country, territory, or geographic area, in a specific period of time, usually one year, after removing the confounding effect of a different age distribution of the population. Expressed per 100 000 population.
The causes of death considered in this indicator are acute respiratory infections in children under 5, cerebrovascular diseases, ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer in the population aged 25 years or older.
The causes of death considered in this indicator are acute respiratory infections in children under 5, cerebrovascular diseases, ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer in the population aged 25 years or older.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Household and ambient air pollution is one of the main risk factors for illness and death, especially premature death. It is closely related to the epidemic of noncommunicable diseases and is responsible for the consumption of significant economic and human resources for health. This indicator reflects the risk in a certain population of dying due to household or ambient air pollution; therefore, it makes it possible to identify more vulnerable populations, where prevention strategies need to be focused.
This indicator is part of a group of indicators used at the international level that makes it possible to monitor countries’ progress in addressing the health risks of air pollution. Its value is applicable to strengthening the systems, structures, and processes needed to support monitoring and reporting on health trends associated with air pollution and its sources. This indicator is part of the Monitoring Framework for Universal Health in the Americas, to measure national progress in implementing policies aimed at strengthening health systems and achieving universal health.
This indicator’s value is applicable to multidisciplinary research on environmental pollution. It helps evaluate the effectiveness of strategies aimed at increasing equitable access to clean, safe energy sources.
This indicator is part of a group of indicators used at the international level that makes it possible to monitor countries’ progress in addressing the health risks of air pollution. Its value is applicable to strengthening the systems, structures, and processes needed to support monitoring and reporting on health trends associated with air pollution and its sources. This indicator is part of the Monitoring Framework for Universal Health in the Americas, to measure national progress in implementing policies aimed at strengthening health systems and achieving universal health.
This indicator’s value is applicable to multidisciplinary research on environmental pollution. It helps evaluate the effectiveness of strategies aimed at increasing equitable access to clean, safe energy sources.
Estimation method
This indicator’s value is from estimates by the World Health Organization (WHO)—the custodian for this and two other indicators related to air pollution and health. The estimation method involves using different data sources. Population data are from the United Nations Population Division (World Population Prospects). The proportion of households in a country relying primarily on polluting fuels and technologies for cooking is used as an indirect indicator for estimating the population’s exposure to household air pollution; these data are regularly collected in household surveys or population censuses and recorded in the WHO household energy database (https://www.who.int/data/gho/data/themes/air-pollution/who-household-energy-db). Data on exposure to ambient air pollution are from the WHO air quality database (https://www.who.int/data/gho/data/themes/air-pollution/who-air-quality-database). These data are then used as inputs in a statistical model to derive specific estimates for a particular country and year, which are then reported at the national, urban, and rural levels. The total number of deaths, years of life lost (YLL), years lived with disability (YLD) and disability-adjusted life years (DALYs) by country, sex, and age group for acute lower respiratory tract infections, chronic obstructive pulmonary disease, lung cancer, ischemic heart disease, and stroke are provided by WHO.
Estimates of mortality attributable to air pollution combine data on the increased (or relative) risk of a disease resulting from exposure, with information on the duration of the population’s exposure. These data are used to estimate the "population attributable fraction" (PAF), which is the fraction of disease in a given population that can be attributed to the exposure, in this case, both the annual mean concentration of particulate matter and exposure to solid fuels for cooking. Applying this fraction to the total disease burden yields the total number of deaths resulting from exposure to ambient and household air pollution.
Mortality attributed to ambient and household air pollution is estimated based on the calculation of attributable fractions of the combined population assuming independently distributed exposures and independent risks. PAF for ambient air pollution and PAF for home air pollution are evaluated separately.
More details on the methodology in:
World Health Organization (WHO). Burden of disease from household air pollution for 2016. Description of method. May 2018. Available from:
https://cdn.who.int/media/docs/default-source/air-quality-database/aqd-2018/hap_bod_methods_may2018.pdf?sfvrsn=d277d739_3
Ezzati M, Hoorn SV, Rodgers A, et al. Comparative Risk Assessment Collaborating Group. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 2003; 362(9380):271–80. Available from:
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(03)13968-2/fulltext
Estimates of mortality attributable to air pollution combine data on the increased (or relative) risk of a disease resulting from exposure, with information on the duration of the population’s exposure. These data are used to estimate the "population attributable fraction" (PAF), which is the fraction of disease in a given population that can be attributed to the exposure, in this case, both the annual mean concentration of particulate matter and exposure to solid fuels for cooking. Applying this fraction to the total disease burden yields the total number of deaths resulting from exposure to ambient and household air pollution.
Mortality attributed to ambient and household air pollution is estimated based on the calculation of attributable fractions of the combined population assuming independently distributed exposures and independent risks. PAF for ambient air pollution and PAF for home air pollution are evaluated separately.
More details on the methodology in:
World Health Organization (WHO). Burden of disease from household air pollution for 2016. Description of method. May 2018. Available from:
https://cdn.who.int/media/docs/default-source/air-quality-database/aqd-2018/hap_bod_methods_may2018.pdf?sfvrsn=d277d739_3
Ezzati M, Hoorn SV, Rodgers A, et al. Comparative Risk Assessment Collaborating Group. Estimates of global and regional potential health gains from reducing multiple major risk factors. Lancet. 2003; 362(9380):271–80. Available from:
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(03)13968-2/fulltext
Interpretation - example
The mortality rate attributable to air pollution in country A in 2019 was 95.2 per 100 000 population. This means that, in that year, the population of country A had a risk of dying due to the joint effect of household and ambient air pollution of 95.2 per 100 000 population.
Desagregation
By sex
Limitations
One of the limiting factors in estimating this indicator is that the parallel use of different fuels and technologies in households today is not fully captured in country data. This likely leads to an underestimation of exposure to household air pollution and also of its contribution to ambient pollution levels.
Due to the application of advanced statistical methods, this indicator’s estimated value may differ from each country’s calculations. These differences may be due to the use of different exposure data, exposure-risk estimates, and mortality data.
Since one of the necessary inputs for estimating the mortality rate attributable to air pollution is deaths, the accuracy of this indicator requires a civil registry system with complete coverage. Deaths must have medical certification of the cause of death and must be recorded in a timely manner in this system.
Due to the application of advanced statistical methods, this indicator’s estimated value may differ from each country’s calculations. These differences may be due to the use of different exposure data, exposure-risk estimates, and mortality data.
Since one of the necessary inputs for estimating the mortality rate attributable to air pollution is deaths, the accuracy of this indicator requires a civil registry system with complete coverage. Deaths must have medical certification of the cause of death and must be recorded in a timely manner in this system.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 3.9.1 Mortality rate attributed to household and ambient air pollution
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 26. Mortality rate attributed to household and ambient air pollution
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.9.1 Mortality rate attributed to household and ambient air pollution
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 26. Mortality rate attributed to household and ambient air pollution
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). WHO indoor air quality guidelines: household fuel combustion. Geneva, 2021. Available from:
https://www.who.int/publications/i/item/9789241548885
World Health Organization (WHO). Burden of disease from the joint effects of household and ambient air pollution for 2016. Available from:
https://cdn.who.int/media/docs/default-source/air-quality-database/aqd-2018/ap_joint_effect_bod_results_may2018.pdf?sfvrsn=4dc44c26_3
World Health Organization (WHO). Road map for an enhanced global response to the adverse health effects of air pollution. World Health Assembly, 71. (2018). Available from:
https://apps.who.int/iris/handle/10665/276321
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). WHO indoor air quality guidelines: household fuel combustion. Geneva, 2021. Available from:
https://www.who.int/publications/i/item/9789241548885
World Health Organization (WHO). Burden of disease from the joint effects of household and ambient air pollution for 2016. Available from:
https://cdn.who.int/media/docs/default-source/air-quality-database/aqd-2018/ap_joint_effect_bod_results_may2018.pdf?sfvrsn=4dc44c26_3
World Health Organization (WHO). Road map for an enhanced global response to the adverse health effects of air pollution. World Health Assembly, 71. (2018). Available from:
https://apps.who.int/iris/handle/10665/276321
Mortality garbage codes (%)
Domain
Health system
Subdomain
Data quality
Definition
Number of deaths whose underlying causes were assigned garbage codes, expressed as a percentage of total deaths in a given country, territory, or geographical area, during a specific year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
The mortality garbage codes percentage is used to assess the quality of mortality information in detail. Quantifying the value of this indicator is essential to understand the quality and correct interpretation of causes of death and their effect on other mortality indicators.
Garbage codes refers to intermediate causes and terminal or unspecific complications that do not match the definition of underlying cause (an illness or injury that set off the chain of events directly leading to death, or the accident or violent act that produced the fatal injury). From a public health perspective, it is important to know the underlying cause in order to support prevention programs and design and implement effective policies and strategies.
Among the reasons for assigning these codes on the death certificate are: insufficient case information from the certifying physician; lack of training among personnel on how to correctly certify the death; and authorization given to non-medical personnel that often lack training and information on certification of underlying causes. Other reasons include failure to train and update mortality coders. This practice also inflates comorbidity at the time of death, complicating identification of the cause that set off the sequence of events leading to death. Garbage codes hide the true cause of death.
Generally speaking, cause-of-death statistics are essential when allocating resources to health services and planning and evaluating health policies. As a result, having correct and reliable underlying cause of death information is a public health priority.
Garbage codes refers to intermediate causes and terminal or unspecific complications that do not match the definition of underlying cause (an illness or injury that set off the chain of events directly leading to death, or the accident or violent act that produced the fatal injury). From a public health perspective, it is important to know the underlying cause in order to support prevention programs and design and implement effective policies and strategies.
Among the reasons for assigning these codes on the death certificate are: insufficient case information from the certifying physician; lack of training among personnel on how to correctly certify the death; and authorization given to non-medical personnel that often lack training and information on certification of underlying causes. Other reasons include failure to train and update mortality coders. This practice also inflates comorbidity at the time of death, complicating identification of the cause that set off the sequence of events leading to death. Garbage codes hide the true cause of death.
Generally speaking, cause-of-death statistics are essential when allocating resources to health services and planning and evaluating health policies. As a result, having correct and reliable underlying cause of death information is a public health priority.
Estimation method
Deaths with underlying causes appearing on the list of garbage codes are adapted from Naghavi et al. (2010) and the PAHO Regional Advisory Committee on Health Statistics (CRAES), 2012.
The percentage of garbage codes is calculated by the Pan American Health Organization (PAHO) from data reported by responsible national institutions.
Formula:
(A/B) x 100
Numerator (A):
Number of deaths assigned garbage codes in year z in a given country, territory, or geographic area.
Denominator (B):
Deaths from all causes in year z in a given country, territory, or geographic area.
PAHO garbage codes list (ICD-10):
A31.1, A40-A41, A48.0, A48.3, A49.9, A59, A60.0, A63.0, A71-A74, B00.0, B07, B08.1, B08.8, B30, B35-B36, B83.9, B94.8, B94.9, B99, C26, C39, C57.9, C63.9, C76, C80, D00-D13, D16-D18, D20-D24, D28-D48, D65, E85.3-E85.9, E86-E87, E88.9, F32-F33, F40-F42, F45-F48, F51-F53, F60-F98, G43-G45, G47-G52, G54, G56-G58, G80-G83, G91.1, G91.3-G91.8, G92, G93.1-G93.6, H00-H04, H05.2-H69, H71-H80, H83-H93, I10, I15, I26, I27.1, I44-I46, I49-I50, I51, I70, I74, I81, I99, J30, J33, J34.2, J35, J69, J80-J81, J86, J90, J93.8-J93.9, J94, J96, J98.1-J98.3, K00-K11, K14, K65-K66, K71-K72, (except K71.7), K75, K76.0-K76.4, K92.0-K92.2, L04-L08, L20-L25, L28-L87, L90-L92, L94, L98.0-L98.3, L98.5-L98.9, M03, M07, M09-M12, M14-M25, M35.3, M40, M43.6-M43.9, M45, M47-M60, M63-M71, M73-M79, M86, M95-M99, N14, N17-N19, N39.3, N40, N46,N60, N84-N93, N97, Q10-Q18, Q36, Q38.1, Q54, Q65-Q74, Q82-Q84, X59, Y10-Y34, Y86,Y87.2, Y89.
The percentage of garbage codes is calculated by the Pan American Health Organization (PAHO) from data reported by responsible national institutions.
Formula:
(A/B) x 100
Numerator (A):
Number of deaths assigned garbage codes in year z in a given country, territory, or geographic area.
Denominator (B):
Deaths from all causes in year z in a given country, territory, or geographic area.
PAHO garbage codes list (ICD-10):
A31.1, A40-A41, A48.0, A48.3, A49.9, A59, A60.0, A63.0, A71-A74, B00.0, B07, B08.1, B08.8, B30, B35-B36, B83.9, B94.8, B94.9, B99, C26, C39, C57.9, C63.9, C76, C80, D00-D13, D16-D18, D20-D24, D28-D48, D65, E85.3-E85.9, E86-E87, E88.9, F32-F33, F40-F42, F45-F48, F51-F53, F60-F98, G43-G45, G47-G52, G54, G56-G58, G80-G83, G91.1, G91.3-G91.8, G92, G93.1-G93.6, H00-H04, H05.2-H69, H71-H80, H83-H93, I10, I15, I26, I27.1, I44-I46, I49-I50, I51, I70, I74, I81, I99, J30, J33, J34.2, J35, J69, J80-J81, J86, J90, J93.8-J93.9, J94, J96, J98.1-J98.3, K00-K11, K14, K65-K66, K71-K72, (except K71.7), K75, K76.0-K76.4, K92.0-K92.2, L04-L08, L20-L25, L28-L87, L90-L92, L94, L98.0-L98.3, L98.5-L98.9, M03, M07, M09-M12, M14-M25, M35.3, M40, M43.6-M43.9, M45, M47-M60, M63-M71, M73-M79, M86, M95-M99, N14, N17-N19, N39.3, N40, N46,N60, N84-N93, N97, Q10-Q18, Q36, Q38.1, Q54, Q65-Q74, Q82-Q84, X59, Y10-Y34, Y86,Y87.2, Y89.
Interpretation - example
According to 2019 data, the percentage of deaths assigned garbage codes in country A was 11%; i.e., 11 out of every 100 deaths in country A that year were coded with garbage codes and therefore are not useful in public health analyses. The true underlying cause of death is unknown.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator is impacted by the coverage of civil registration systems, timely registration of deaths, and recorded information on causes of death. For example, when there is incomplete coverage in registration systems, the proportion of deaths assigned garbage codes will generally increase as coverage increases, even with no real drop in the quality of medical certification.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Naghavi, M., Makela, S., Foreman, K. et al. Algorithms for enhancing public health utility of national causes-of-death data. Population Health Metrics 8, 9 (2010). Available from: https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-8-9
Naghavi, M., Makela, S., Foreman, K. et al. Algorithms for enhancing public health utility of national causes-of-death data. Population Health Metrics 8, 9 (2010). Available from: https://pophealthmetrics.biomedcentral.com/articles/10.1186/1478-7954-8-9
Mortality under-registration (%)
Domain
Health system
Subdomain
Data quality
Definition
The difference between registered deaths and estimated deaths, expressed as a percentage of total estimated deaths in a given country, territory, or geographical area, in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
This indicator measures the completeness of mortality data and is one of the indicators reflecting the performance of civil registration systems and vital statistics.
Generally speaking, complete, timely, and quality death statistics are essential for designing and evaluating targeted public policies to meet the needs of the population.
The indicator makes inequalities and barriers in access to registration systems visible and facilitates analysis of temporal and geographical trends. It helps to identify areas in need of improved civil registration systems and vital statistics coverage and implement and evaluate specific interventions to enhance deaths registration.
Generally speaking, complete, timely, and quality death statistics are essential for designing and evaluating targeted public policies to meet the needs of the population.
The indicator makes inequalities and barriers in access to registration systems visible and facilitates analysis of temporal and geographical trends. It helps to identify areas in need of improved civil registration systems and vital statistics coverage and implement and evaluate specific interventions to enhance deaths registration.
Estimation method
This indicator is calculated from the number of individual deaths registered in databases maintained by institutions responsible for vital statistics (e.g., the Ministry of Health or the National Institute of Statistics) and reported to the Pan American Health Organization (PAHO). These deaths are the numerator used when calculating the indicator.
For the denominator, population estimates and projections of the United Nations Population Division are used.
Formula:
(A/B) x 100
Numerator (A):
Number of registered deaths in a given country, territory or geographic area in year z.
Denominator (B):
Number of estimated deaths in a given country, territory or geographic area in year z.
For the denominator, population estimates and projections of the United Nations Population Division are used.
Formula:
(A/B) x 100
Numerator (A):
Number of registered deaths in a given country, territory or geographic area in year z.
Denominator (B):
Number of estimated deaths in a given country, territory or geographic area in year z.
Interpretation - example
Mortality under-registration in country A during 2019 was 10%; i.e., in this year, 10 out of every 100 deaths in country A were not registered.
Desagregation
No disaggregation
Limitations
The value of this indicator may differ from that calculated by the country due to methodological differences affecting both the numerator and the denominator. These differences notably include extemporaneous records, that is, including in the numerator deaths that are registered in a specific year, regardless of the year of occurrence, instead of only those that occur in the reported year. Another difference lies in the death estimates used; for example, national versus international estimates.
There are countries in the Region that by law register deaths in the year of occurrence, while other countries register deaths in the year they are reported.
There are countries in the Region that by law register deaths in the year of occurrence, while other countries register deaths in the year they are reported.
Data source(s)
Numerator:
National health authority
Denominator:
Deaths estimated by international agencies
National health authority
Denominator:
Deaths estimated by international agencies
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). Rapid assessment of national civil registration and vital statistics systems, WHO REFERENCE NUMBER: WHO/IER/HSI/STM/2010.1, 2010. Available from: https://www.who.int/publications/i/item/rapid-assessment-of-national-civil-registration-and-vital-statistics-systems
UNSTATS. Handbook on Civil Registration and Vital Statistics Systems: Management, Operation and Maintenance, Revision 1, NY, 2018. Available from: https://unstats.un.org/unsd/demographic-social/Standards-and-Methods/files/Handbooks/crvs/crvs-mgt-E.pdf
World Health Organization (WHO). Rapid assessment of national civil registration and vital statistics systems, WHO REFERENCE NUMBER: WHO/IER/HSI/STM/2010.1, 2010. Available from: https://www.who.int/publications/i/item/rapid-assessment-of-national-civil-registration-and-vital-statistics-systems
UNSTATS. Handbook on Civil Registration and Vital Statistics Systems: Management, Operation and Maintenance, Revision 1, NY, 2018. Available from: https://unstats.un.org/unsd/demographic-social/Standards-and-Methods/files/Handbooks/crvs/crvs-mgt-E.pdf
Municipalities with DTP3 coverage ≥ 95%
Domain
Health system
Subdomain
Health service
Definition
Percentage of municipalities in a given country, territory, or geographic area that in a specific year report coverage, in children under 1 year of age, equal to or greater than 95% for the third dose of diphtheria, tetanus, and pertussis vaccine (DTP3).
Municipalities are defined as the third administrative level of a country, with the country level being the first, unless otherwise indicated.
Municipalities are defined as the third administrative level of a country, with the country level being the first, unless otherwise indicated.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Immunization is one of the most cost-effective public health interventions and is essential to reducing infant mortality. DTP3 vaccine coverage is a measure of the performance of the health system in administering childhood vaccination. This indicator identifies gaps in immunization program coverage at the subnational level that may remain hidden when national analysis is performed. Its value enables more efficient allocation of economic, human, and technological resources.
Spatial analysis of immunization program coverage is a critical tool for obtaining new information and making informed decisions. Spatial analysis is used when obtaining estimates, detecting patterns and variations in time, and interpreting observed changes.
Spatial analysis of immunization program coverage is a critical tool for obtaining new information and making informed decisions. Spatial analysis is used when obtaining estimates, detecting patterns and variations in time, and interpreting observed changes.
Estimation method
Annually, countries officially report coverage figures through the PAHO/WHO/UNICEF Joint Notification Form. The data comes from their administrative information systems. Using these figures, PAHO calculates regional coverage according to the weighted average of the United Nations population.
Interpretation - example
According to 2019 data, 80% of the municipalities in country A reported ≥ 95% DTP3 coverage.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the performance of technical programs and their ability to capture data at the subnational level. Ideally, countries would also have a well-functioning national immunization registry that allows information to be disaggregated at the municipal level. The methodology used to determine immunization coverage may differ from country to country.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
Pan American Health Organization (PAHO). Immunization data and statistics. Available from: https://www.paho.org/en/topics/immunization/immunization-data-and-statistics
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
Pan American Health Organization (PAHO). Immunization data and statistics. Available from: https://www.paho.org/en/topics/immunization/immunization-data-and-statistics
Neonatal mortality rate (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
The quotient between the number of children born alive in a given country, territory, or geographic area that died before the age of 28 days in a specific calendar year and the total number of live births for the same population and year, as reported by the corresponding health authority. Expressed per 1 000 live births.
Live birth means the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, that, after such expulsion or extraction, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered a live birth.
Live birth means the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, that, after such expulsion or extraction, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered a live birth.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Crude/Corrected
Purpose
This indicator quantifies the scope of neonatal mortality and highlights it as a public health problem for a given population or geographic area.
The estimated neonatal mortality rate reflects a population’s health status and social, economic, and environmental conditions. It makes it possible to identify health inequities and populations with specific risk factors and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health, particularly prenatal and neonatal care. Its result helps strengthen professional care for childbirth, breastfeeding, and the Expanded Program on Immunization.
It allows for analysis of the geographic and time trend of neonatal mortality in a given population.
The estimated neonatal mortality rate reflects a population’s health status and social, economic, and environmental conditions. It makes it possible to identify health inequities and populations with specific risk factors and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health, particularly prenatal and neonatal care. Its result helps strengthen professional care for childbirth, breastfeeding, and the Expanded Program on Immunization.
It allows for analysis of the geographic and time trend of neonatal mortality in a given population.
Estimation method
The neonatal mortality rate uses the number of children born alive who died before the age of 28 days as the numerator and the total number of live births as the denominator.
Formula:
(A/B) x 1 000 live births
Numerator (A):
Number of children born alive who died before the age of 28 days in a given country, territory and geographic area during year z
Denominator (B):
Total number of live births in the same population during year z
To estimate the indicator by sex, the following formula is applied:
(A/B) x 1 000 live births
Numerator (A):
Number of children born alive of a given sex, who died before the age of 28 days, in a specific country, territory, or geographic area during year z
Denominator (B):
Total number of live births in the same population during year z
Formula:
(A/B) x 1 000 live births
Numerator (A):
Number of children born alive who died before the age of 28 days in a given country, territory and geographic area during year z
Denominator (B):
Total number of live births in the same population during year z
To estimate the indicator by sex, the following formula is applied:
(A/B) x 1 000 live births
Numerator (A):
Number of children born alive of a given sex, who died before the age of 28 days, in a specific country, territory, or geographic area during year z
Denominator (B):
Total number of live births in the same population during year z
Interpretation - example
Country A’s neonatal mortality rate for 2019 was 26.3 per 1 000 live births; that is, in that year 26 children born alive died before the age of 28 days per 1 000 live births.
Desagregation
By sex
Limitations
The neonatal mortality rate requires a civil registry system with good coverage, and births and deaths must be recorded in a timely manner in this system; otherwise, this indicator will not be sufficiently accurate.
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths.
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.2.2 Neonatal mortality rate.
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 3. Neonatal mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.2.2 Neonatal mortality rate.
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 3. Neonatal mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
New HIV diagnoses rate (100 000 pop)
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The number new cases of people diagnosed with human immunodeficiency virus (HIV), in a given country, territory, or geographical area, during a specific period.
Measurement Unit
100 000 population
Type of measurement
Rate
Type of statistics
Crude
Purpose
Human immunodeficiency virus (HIV) is an infection that attacks the body's immune system. Those affected have a high susceptibility to other diseases, such as tuberculosis and some types of cancer. HIV is a preventable disease, and despite having no cure, antiretroviral therapy (ART) reduces viral replication, shrinking the load to an undetectable level. ART prevents mother-to-child transmission of HIV during pregnancy, childbirth, and lactation. Those receiving ART and who have suppressed the virus will not transmit it to their sexual partners.
The indicator is used to monitor the epidemic’s trends and dynamics within a country or territory and assess access to diagnosis.
This indicator is one of the inputs applied when estimating the HIV burden. It contributes to public health policy-making, planning and allocation of economic, human, and technological resources to respond to the disease. This metric, disaggregated by sex and age, identifies populations in need of direct preventive and surveillance strategies, communication campaigns, and access to diagnosis and early antiretroviral treatment to reduce impact on the immune system and transmission. It is also considered when implementing counseling and accompaniment programs for patients with HIV.
The indicator is used to monitor the epidemic’s trends and dynamics within a country or territory and assess access to diagnosis.
This indicator is one of the inputs applied when estimating the HIV burden. It contributes to public health policy-making, planning and allocation of economic, human, and technological resources to respond to the disease. This metric, disaggregated by sex and age, identifies populations in need of direct preventive and surveillance strategies, communication campaigns, and access to diagnosis and early antiretroviral treatment to reduce impact on the immune system and transmission. It is also considered when implementing counseling and accompaniment programs for patients with HIV.
Estimation method
The number of HIV cases is obtained from each country's HIV surveillance and control system. This information is reported annually to the Pan American Health Organization, where the rate is calculated based on data from the total population indicator.
Interpretation - example
In 2019, the rate of new HIV diagnoses in country A was 25.2 per 100 000 people, i.e., there were 25.2 reported new cases of people diagnosed with HIV per 100 000 people in country A.
Desagregation
By sex
Limitations
The value of the new HIV diagnoses rate is impacted by the coverage, effectiveness, and quality of surveillance and control systems. One factor to consider when interpreting this indicator is that the diagnosis year may differ from the year the person acquired HIV. Another issue affecting the indicator's accuracy is timely access to laboratory diagnostic tests.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator: 16. Incidence rate of HIV infections
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator: 16. Incidence rate of HIV infections
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Joint United Nations Programme on HIV/AIDS (UNAIDS). UNAIDS Data 2017. Available from: https://www.unaids.org/en
World Health Organization (WHO). Consolidated strategic information guidelines for HIV in the health sector. Geneva, 2015. Available from: http://apps.who.int/iris/bitstream/handle/10665/164716/9789241508759_eng.pdf
World Health Organization (WHO). HIV/AIDS. Available from: https://www.who.int/health-topics/hiv-aids#tab=tab_1
Joint United Nations Programme on HIV/AIDS (UNAIDS). UNAIDS Data 2017. Available from: https://www.unaids.org/en
World Health Organization (WHO). Consolidated strategic information guidelines for HIV in the health sector. Geneva, 2015. Available from: http://apps.who.int/iris/bitstream/handle/10665/164716/9789241508759_eng.pdf
World Health Organization (WHO). HIV/AIDS. Available from: https://www.who.int/health-topics/hiv-aids#tab=tab_1
New HIV diagnoses; sex ratio (male:female)
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The woman-to-man ratio of new diagnoses of human immunodeficiency virus (HIV) in a given country, territory, or geographical area, during a specific period.
Measurement Unit
Male:female
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Human immunodeficiency virus (HIV) is an infection that attacks the body's immune system. Those affected have a high susceptibility to other diseases, such as tuberculosis and some types of cancer. HIV is a preventable disease, and despite having no cure, antiretroviral therapy (ART) reduces viral replication, shrinking the load to an undetectable level. ART prevents mother-to-child transmission of HIV during pregnancy, childbirth, and lactation. Those receiving ART and who have suppressed the virus will not transmit HIV to their sexual partners.
The indicator is applied to monitor the differences between men and women in the trends and dynamics of the epidemic within a country or territory. Its value reflects the differentiated results of infection prevention and control strategies by sex.
This indicator is one of the inputs to estimate the HIV burden. It contributes to public health policy-making with a gender approach and for planning and allocation of economic, human, and technological resources to respond to the disease. This metric, disaggregated by sex and age, identifies populations in need of direct preventive and surveillance strategies, communication campaigns, and access to diagnosis and early antiretroviral treatment to reduce impact on the immune system and transmission. The indicator contributes to implementing counseling and accompaniment programs for patients with HIV.
The indicator is applied to monitor the differences between men and women in the trends and dynamics of the epidemic within a country or territory. Its value reflects the differentiated results of infection prevention and control strategies by sex.
This indicator is one of the inputs to estimate the HIV burden. It contributes to public health policy-making with a gender approach and for planning and allocation of economic, human, and technological resources to respond to the disease. This metric, disaggregated by sex and age, identifies populations in need of direct preventive and surveillance strategies, communication campaigns, and access to diagnosis and early antiretroviral treatment to reduce impact on the immune system and transmission. The indicator contributes to implementing counseling and accompaniment programs for patients with HIV.
Estimation method
The number of new HIV diagnoses is obtained from data reported to the Pan American Health Organization (PAHO) by national authorities, compiled mostly from national HIV surveillance and control systems.
The sex ratio is calculated by PAHO.
The sex ratio is calculated by PAHO.
Interpretation - example
In 2019, the sex ratio of new HIV diagnoses in country A was 2.0. This means for every new HIV diagnosis in a woman, there were two in men. In other words, twice as many new HIV diagnoses were reported in men than in women.
Desagregation
No disaggregation
Limitations
The value of the new HIV diagnoses rate is impacted by the coverage, effectiveness, and quality of surveillance and control systems. One factor to consider when interpreting this indicator is that the diagnosis year may differ from the year the person acquired HIV. Another issue affecting the indicator's accuracy is timely access to laboratory diagnostic tests.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Consolidated strategic information guidelines for HIV in the health sector. Geneva, 2015. Available from: http://apps.who.int/iris/bitstream/handle/10665/164716/9789241508759_eng.pdf
Joint United Nations Programme on HIV/AIDS (UNAIDS). UNAIDS Data 2017. Available from: https://www.unaids.org/en
World Health Organization (WHO). HIV/AIDS. Available from: https://www.who.int/health-topics/hiv-aids#tab=tab_1
https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Consolidated strategic information guidelines for HIV in the health sector. Geneva, 2015. Available from: http://apps.who.int/iris/bitstream/handle/10665/164716/9789241508759_eng.pdf
Joint United Nations Programme on HIV/AIDS (UNAIDS). UNAIDS Data 2017. Available from: https://www.unaids.org/en
World Health Organization (WHO). HIV/AIDS. Available from: https://www.who.int/health-topics/hiv-aids#tab=tab_1
Noncommunicable diseases mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from noncommunicable diseases in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution of the population. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator reflects a population’s lifestyles, socio-economic development, and health status and is also related to population aging and rapid, poorly planned urbanization. Its analysis makes it possible to identify populations with greater risk factors for dying from noncommunicable diseases and to encourage research in this area.
Its result is applicable to the design, implementation, and evaluation of health policies on noncommunicable diseases and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, evaluating over time the effectiveness of interventions promoting healthy lifestyles and socio-economic measures to reduce health inequities and increase timely access to care.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from noncommunicable diseases, in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies on noncommunicable diseases and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and control of this group of pathologies, among others. Its applications include, for example, evaluating over time the effectiveness of interventions promoting healthy lifestyles and socio-economic measures to reduce health inequities and increase timely access to care.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from noncommunicable diseases, in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from noncommunicable diseases, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes C00 - C97, D00 - D48, D55 - D64 (except D64.9), D65 - D89, E03 - E07, E10 - E34, E65 - E88, F01 - F99, G06 - G98 (except G14), H00 - H61, H68 - H93, I00 - I99, J30 - J98, K00 - K92, L00 - L98, M00 - M99, N00 - N64, N75 - N98, Q00 - Q99, X41 - X42, X44 - X45, R95 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the noncommunicable diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The noncommunicable diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes C00 - C97, D00 - D48, D55 - D64 (except D64.9), D65 - D89, E03 - E07, E10 - E34, E65 - E88, F01 - F99, G06 - G98 (except G14), H00 - H61, H68 - H93, I00 - I99, J30 - J98, K00 - K92, L00 - L98, M00 - M99, N00 - N64, N75 - N98, Q00 - Q99, X41 - X42, X44 - X45, R95 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the noncommunicable diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The noncommunicable diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted noncommunicable diseases mortality rate for 2019 was 93 per 100 000 population in country A and 60 per 100 000 population in country B; that is, in 2019 noncommunicable diseases were responsible for the death of 93 people per 100 000 population of country A, compared to country B, where 60 people died from the same group of causes per 100 000 population. This means that, after removing the effect of differences in the age distributions in the two countries, the risk of dying from noncommunicable diseases in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted noncommunicable diseases mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted noncommunicable diseases mortality rate will depend on the standard population used for its adjustment; therefore it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different group of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the noncommunicable diseases mortality rate requires a civil registry system with good coverage. Death from this group of pathologies must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted noncommunicable diseases mortality rate will depend on the standard population used for its adjustment; therefore it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different group of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the noncommunicable diseases mortality rate requires a civil registry system with good coverage. Death from this group of pathologies must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Nursing associates professionals (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Nursing associates professionals’ density is defined as the number of practising nursing professionals, with formal education, in health facilities as of 31 December in a specific year, per 10 000 population in a given country, territory or geographic area.
Nursing associates professionals provide basic nursing care and health advice, monitor health status of users, and carry out pre-established care and treatment plans under supervision.
The definitions of the International Standard Classification of Occupations (ISCO-08) are adapted to the context of the Region of the Americas and its countries.
Nursing associates professionals provide basic nursing care and health advice, monitor health status of users, and carry out pre-established care and treatment plans under supervision.
The definitions of the International Standard Classification of Occupations (ISCO-08) are adapted to the context of the Region of the Americas and its countries.
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Nursing associates professionals are an integral part of the health care system. This indicator makes it possible to identify inequalities in the distribution of health resources and prioritize human and economic resource allocation to specific populations. Its value is used for the formulation of public policies that allow promoting health financing, as well as the professionalization of this group of workers, recruitment and retention.
This indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
This indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of nursing associates professionals reported by the countries of the Americas to the Pan American Health Organization (PAHO).
This data is collected from secondary sources, such as health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
This data is collected from secondary sources, such as health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Interpretation - example
According to 2019 data, the density of nursing associates professionals in country A was 19.3 per 10 000 population. This means that, in all health facilities, in 2019 this country had 19.3 nursing associates professionals per 10 000 population.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working nursing associate professionals, or include all registered or licensed, even if their employment status is unknown.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working nursing associate professionals, or include all registered or licensed, even if their employment status is unknown.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Nursing professionals (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Nursing professionals’ density is defined as the number of practising nursing professionals, with high formal education, in health facilities as of 31 December in a specific year, per 10 000 population in a given country, territory or geographic area.
Professionals who provide prevention, promotion, treatment and rehabilitation services to people who need health care. They are responsible for planning and managing the care of users, including supervision of other health workers. They work autonomously or in a team, both in the field of health services and in the community. Includes: Professional Nurse, Registered Nurse, Specialist Nurse, Advanced Practice Nurse, Clinical Nurse, Public Health Nurse, Nursing Technician, Nursing Technologist.
The definitions of the International Standard Classification of Occupations (ISCO-08) are adapted to the context of the Region of the Americas and its countries.
Professionals who provide prevention, promotion, treatment and rehabilitation services to people who need health care. They are responsible for planning and managing the care of users, including supervision of other health workers. They work autonomously or in a team, both in the field of health services and in the community. Includes: Professional Nurse, Registered Nurse, Specialist Nurse, Advanced Practice Nurse, Clinical Nurse, Public Health Nurse, Nursing Technician, Nursing Technologist.
The definitions of the International Standard Classification of Occupations (ISCO-08) are adapted to the context of the Region of the Americas and its countries.
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Nursing professionals are an integral part of the health care system. This indicator makes it possible to identify inequalities in the distribution of health resources and prioritize human and economic resource allocation to specific populations. Its value is used to develop public policies to increase funding for health, and to train, hire, and retrain skilled workers in this area.
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of nursing professionals reported by the countries of the Americas to the Pan American Health Organization (PAHO). This data is collected from secondary sources, such as: health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Interpretation - example
According to 2019 data, the density of nursing professionals in country A was 19.3 per 10 000 population. This means that, in all health facilities, this country had 19.3 nurses per 10 000 population in 2019.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working nursing professionals, or include all registered or licensed, even if their employment status is unknown.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working nursing professionals, or include all registered or licensed, even if their employment status is unknown.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
International Council of Nurses (ICN). Available from: https://www.icn.ch/
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
International Council of Nurses (ICN). Available from: https://www.icn.ch/
Out-of-pocket expenditure as % of current health expenditure
Domain
Health system
Subdomain
Health system
Definition
Direct payment for health goods and services made at the time the individual or household receives care and at the point of access, for a given year, in a given country, territory, or geographic area. This includes formal payments (deductibles, coinsurance, and/or co-payments in fees for consultations and/or hospitalizations, purchase of medicines in pharmacies, and other services, such as laboratories) and informal payments, always deducting any subsequent reimbursement. Expressed as a proportion of total current health expenditure.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Out-of-pocket health expenditure is a basic indicator of health financing systems. It reflects the economic burden that households face to pay for health. Because out-of-pocket expenditure is strongly associated with catastrophic expenditure, it is possible to identify health inequalities. The indicator is applied when developing and assessing public policies that support universal access to health.
The indicator is also used to evaluate changes in out-of-pocket expenditure over time, its relationship to total household expenditure, and the factors that determine out-of-pocket expenditure in a household.
The indicator is also used to evaluate changes in out-of-pocket expenditure over time, its relationship to total household expenditure, and the factors that determine out-of-pocket expenditure in a household.
Estimation method
The value of this indicator comes from data reported by countries to the World Health Organization (WHO), collected from health accounts (HA). Because not all countries have or update their HA, WHO may eventually obtain the data through technical contacts in countries or through publicly available documents and reports synced with the HA framework.
Missing values are estimated using various accounting techniques based on the data available for each country. The main international references come from financial statistics from the International Monetary Fund (IMF), health data from the Organisation for Economic Cooperation and Development (OECD), and national accounts statistics from the United Nations. National sources include NHA reports, national accounts reports, comprehensive financing studies, Classification of Individual Consumption According to Purpose (COICOP) reports, and institutional reports from private entities involved in providing or financing health care, in actuarial and financial reports from private companies and health insurance agencies. Additional sources include household and business surveys, and economic censuses. Ad hoc surveys are another potential source of data.
Data collected by WHO feed into its Global Health Expenditure Database (GHED), which serves as a global benchmark for information on health expenditures in WHO Member States.
For more details on methodology, see:
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
World Health Organization (WHO) Methodology for the update of the Global Health Expenditure Database, 2000–2018. Technical note. Version December 2020. Available from: https://apps.who.int
ha/database/DocumentationCentre/Index/en
Missing values are estimated using various accounting techniques based on the data available for each country. The main international references come from financial statistics from the International Monetary Fund (IMF), health data from the Organisation for Economic Cooperation and Development (OECD), and national accounts statistics from the United Nations. National sources include NHA reports, national accounts reports, comprehensive financing studies, Classification of Individual Consumption According to Purpose (COICOP) reports, and institutional reports from private entities involved in providing or financing health care, in actuarial and financial reports from private companies and health insurance agencies. Additional sources include household and business surveys, and economic censuses. Ad hoc surveys are another potential source of data.
Data collected by WHO feed into its Global Health Expenditure Database (GHED), which serves as a global benchmark for information on health expenditures in WHO Member States.
For more details on methodology, see:
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
World Health Organization (WHO) Methodology for the update of the Global Health Expenditure Database, 2000–2018. Technical note. Version December 2020. Available from: https://apps.who.int
ha/database/DocumentationCentre/Index/en
Interpretation - example
The out-of-pocket health expenditure of country A during 2019 was 45.5% of the total current health expenditure of this country.
Desagregation
No disaggregation
Limitations
Most health accounts cause measurement problems when estimating this indicator, mainly due to the measurement methods used by countries. This limits the comparability of results at the international level. The challenges in calculating out-of-pocket health expenditure have to do with the data, including frequency of updates, coverage, and level of detail.
Due to the methodology used, the value of the indicator estimated by WHO may differ from the results obtained by each country.
Due to the methodology used, the value of the indicator estimated by WHO may differ from the results obtained by each country.
Data source(s)
World Health Organization. Global Health Expenditure database. Available from: https://apps.who.int
ha/database/Home/Index/en
ha/database/Home/Index/en
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or income
Available from: https://unstats.un.org/sdgs/metadata/?Text=&Goal=3&Target=3.8
Sustainable Development Goals (SDG).
Indicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or income
Available from: https://unstats.un.org/sdgs/metadata/?Text=&Goal=3&Target=3.8
References
World Health Organization (WHO). Global Health Expenditure Database. Available from: https://apps.who.int
ha/database/Home/Index/en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Organisation for Economic Cooperation and Development (OECD).
Health Expenditure. Available from: https://www.oecd.org/els/health-systems/health-expenditure.htm#:~:text=Latest%20OECD%20estimates%20point%20to,previous%20years%20at%20around%208.8%25.
ha/database/Home/Index/en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Organisation for Economic Cooperation and Development (OECD).
Health Expenditure. Available from: https://www.oecd.org/els/health-systems/health-expenditure.htm#:~:text=Latest%20OECD%20estimates%20point%20to,previous%20years%20at%20around%208.8%25.
Pancreas cancer mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from pancreas cancer in the population, in a given country, territory, or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July) after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from pancreas cancer and to evaluate the presence of potential risk factors, such as those associated with environment or lifestyle.
The age-adjusted mortality rate from pancreas cancer is applicable to the design, implementation, and evaluation of health policies for the prevention and control of pancreas cancer and the distribution of economic, human, and technological resources for this disease, among others. Its applications include, for example, evaluating the impact of strategies to prevent harmful alcohol consumption on mortality from pancreatic cancer.
Removing the effect of the different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from pancreas cancer, in the same population or across populations.
The age-adjusted mortality rate from pancreas cancer is applicable to the design, implementation, and evaluation of health policies for the prevention and control of pancreas cancer and the distribution of economic, human, and technological resources for this disease, among others. Its applications include, for example, evaluating the impact of strategies to prevent harmful alcohol consumption on mortality from pancreatic cancer.
Removing the effect of the different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from pancreas cancer, in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from pancreas cancer from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying cause of death corresponds to code C25 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the pancreas cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The pancreas cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying cause of death corresponds to code C25 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the pancreas cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The pancreas cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted pancreas cancer mortality rate for 2019 is 10 per 100 000 population in country A and 20 per 100 000 population in country B; that is, pancreas cancer was responsible for the death of 10 people per 100 000 population of country A in 2019, compared to country B, where 20 people died from that cause per 100 000 population, so there is twice the risk of dying from pancreas cancer in country B. This means that, after removing the effect of differences in the age structure in the two countries, the population in country A had a higher risk of dying from this cancer in 2019 than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted pancreas cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the pancreas cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in this system in a timely manner, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted pancreas cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the pancreas cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in this system in a timely manner, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pertussis cases in children under age 5
Domain
Morbidity
Subdomain
Child health
Definition
Number of confirmed cases of pertussis in children under 5 years of age reported during a specific year, in a given country, territory, or geographical area. Includes laboratory or clinically confirmed or epidemiologically linked cases.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Pertussis is a highly contagious disease and a major cause of death in children, especially infants. The fundamental pillar of pertussis prevention is vaccination. This indicator makes it possible to identify populations with inadequate immunization program coverage, as well as those in need of strengthened surveillance programs and diagnostic capacity.
The number of confirmed cases of pertussis in children under 5 years of age reflects the impact of this disease on the child population and allows the temporal and geographical trends of this disease to be analyzed. It quantifies the impact of pertussis as a public health problem in a given population or geographical area and supports decision making for public policies aimed at reducing morbidity and mortality in children under 5 years of age.
This indicator reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and populations with greater risk factors for transmitting Bordetella pertussis.
It is applied for designing, implementing, and assessing health policies to prevent, treat, and control pertussis in children and distribute economic, human, and technological resources to fight the disease, among other purposes. Among its main applications are planning and assessing vaccination programs against pertussis and prioritizing quality health care in children.
The number of confirmed cases of pertussis in children under 5 years of age reflects the impact of this disease on the child population and allows the temporal and geographical trends of this disease to be analyzed. It quantifies the impact of pertussis as a public health problem in a given population or geographical area and supports decision making for public policies aimed at reducing morbidity and mortality in children under 5 years of age.
This indicator reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and populations with greater risk factors for transmitting Bordetella pertussis.
It is applied for designing, implementing, and assessing health policies to prevent, treat, and control pertussis in children and distribute economic, human, and technological resources to fight the disease, among other purposes. Among its main applications are planning and assessing vaccination programs against pertussis and prioritizing quality health care in children.
Estimation method
The number of confirmed cases of pertussis (ICD-10 code: A37) in resident children under 5 years of age, is obtained from data collected by national disease surveillance and control systems and reported by the countries of the Region of the Americas.
Interpretation - example
In 2019, there were 44 confirmed cases of pertussis among children under 5 years of age residing in country A.
Desagregation
No disaggregation
Limitations
Estimates of the number of cases of pertussis in children under 5 years of age are affected by factors, such as: the effectiveness of national pertussis surveillance and control systems, diagnostic suspicion, and overreporting of cases due to clinical similarities with whooping cough syndromes caused by other agents, for example B. parapertussis, Mycoplasma pneumoniae, Chlamydia trachomatis, Chlamydia pneumoniae, and Adenovirus 1, 2, 3, and 5. Other factors to consider are country-specific variations in case definitions and available diagnostic tests.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme. Available from: https://sdgs.un.org/
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme. Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pinell-McNamara V., Acosta A., Pedreira M et al. Expanding Pertussis Epidemiology in 6 Latin America Countries through the Latin American Pertussis Project. Emerg Infect Dis. 2017 Dec; 23 (Suppl 1): S94–S100. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711316/
Indicadores básicos para a saúde no Brasil: conceitos e aplicações, 2ª edição [Core health indicators in Brazil: concepts and applications]. Pan American Health Organization. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Pinell-McNamara V., Acosta A., Pedreira M et al. Expanding Pertussis Epidemiology in 6 Latin America Countries through the Latin American Pertussis Project. Emerg Infect Dis. 2017 Dec; 23 (Suppl 1): S94–S100. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711316/
Indicadores básicos para a saúde no Brasil: conceitos e aplicações, 2ª edição [Core health indicators in Brazil: concepts and applications]. Pan American Health Organization. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Pharmacists (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Pharmacist density is defined as the total number of practising professionals in health facilities as of 31 December in a given year, per 10 000 population in a specific country, territory or geographic area.
The International Standard Classification of Occupations code for this category is 2262 (2008 revision).
The International Standard Classification of Occupations code for this category is 2262 (2008 revision).
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
Pharmacists play a fundamental role in the health team. Without access to of quality medicines and responsible use of them, health systems lose their ability to meet health care needs. This indicator makes it possible to identify inequalities in the distribution and prioritize human and economic resource allocation to specific populations. Its value is used to develop public policies to increase funding for health, and to train, hire, and retrain skilled workers in this area.
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of pharmacists reported by the countries of the Americas to the Pan American Health Organization (PAHO). This data is collected from secondary sources, such as: health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator. (https://population.un.org/wpp/).
Population figures from the United Nations Population Division are used for the denominator. (https://population.un.org/wpp/).
Interpretation - example
According to 2019 data, the density of pharmacists in country A was 2.3 per 10 000 population. This means that, in all health facilities, country A had 2.3 pharmacists per 10 000 population in 2019.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working pharmacists or include all registered or licensed to practice professionally, even when their employment status is unknown.
The value of this indicator may differ from figures calculated by each country due to differences such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working pharmacists or include all registered or licensed to practice professionally, even when their employment status is unknown.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Physicians (10 000 pop)
Domain
Health service coverage
Subdomain
Human resources
Definition
Physician density is defined as the number of practising physicians (including generalist practitioners and specialists) in health facilities of 31 December of a specific year, per 10 000 population, in a given country, territory or geographic area.
The International Standard Classification of Occupations codes (2008 revision) included in this category are: 221 (2211, and 2212).
The International Standard Classification of Occupations codes (2008 revision) included in this category are: 221 (2211, and 2212).
Measurement Unit
Per 10 000 population
Type of measurement
Ratio
Type of statistics
Crude
Purpose
The availability of and access to properly trained physicians is key to promoting, maintaining, and recovering the health of a population. This indicator makes it possible to identify inequalities in the distribution of health resources and prioritize human and economic resource allocation to specific populations. Its value is used to develop public policies to increase funding for health, and to train, hire, and retrain skilled workers in this area.
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
The indicator also contributes to monitoring country progress in implementing the Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023 (https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt) and the Global Strategy on Human Resources for Health 2030 (https://www.who.int/publications/i/item/9789241511131).
Estimation method
This indicator is calculated using as numerator the number of physicians reported by the countries of the Americas to the Pan American Health Organization (PAHO). This data is collected from secondary sources, such as: health workers records or databases, aggregated data from health facilities (routine administrative records, health management information systems, censuses, and surveys from the district health information system), records of councils/chambers/professional associations, active population surveys, workforce surveys, national censuses, among other verifiable sources (official scientific articles, unpublished publications, central bank accounts).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Population figures from the United Nations Population Division are used for the denominator (https://population.un.org/wpp/).
Interpretation - example
According to 2019 data, the density of physicians in country A was 20.3 per 10 000 population. This means that, in health facilities, country A had 20.3 physicians per 10 000 population in 2019.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data. In general, the public health sector tends to maintain more complete data on its workers. This may lead to underestimation of the active workforce in the private health, military, non-governmental organization, and religious sectors. Due to differences in data sources, there is considerable variability between countries in the coverage, regularity, quality, and integrity of the original data.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working physicians or include all registered or licensed to practice professionally, even when their employment status is unknown.
The value of this indicator may differ from figures calculated by each country due to differences, such as the populations used as denominator. Another factor to consider, depending on the nature of the original data source, is that the numerator could be limited to actively working physicians or include all registered or licensed to practice professionally, even when their employment status is unknown.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
Indicator 3.c.1 Health worker density and distribution
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Pan American Health Organization (PAHO). Strategy on Human Resources for Universal Access to Health and Universal Health Coverage. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/34198
Pan American Health Organization (PAHO). Plan of Action on Human Resources for Universal Access to Health and Universal Health Coverage 2018-2023. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/49611?locale-attribute=pt
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). 2018 Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Geneva, 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/259951/WHO-HIS-IER-GPM-2018.1-eng.pdf
World Health Organization (WHO). National health workforce accounts: a handbook. Geneva, 2017. Available from: https://apps.who.int/iris/bitstream/handle/10665/259360/9789241513111-eng.pdf
International Labour Organization (ILO). International Standard Classification of Occupations 08 (ISCO-08). Available from: https://unstats.un.org/unsd/classifications/Family/Detail/1067
World Health Organization (WHO). Global Health Workforce statistics database. Available from: https://www.who.int/data/gho/data/themes/topics/health-workforce
Population aged < 15 years (%)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Proportion of the population that is under the age of 15 compared to the total population, residing in a country, territory, or geographical area, at a specific point in time, usually at mid-year (July 1).
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/Predicted
Purpose
This indicator is related to the age structure of the population and the share of potentially inactive members that is used to estimate the dependency ratio.
It is also used to analyze the geographical distribution and temporal trends of this population segment, which is useful information for planning and evaluating health and educational policies, among others.
It is also used to analyze the geographical distribution and temporal trends of this population segment, which is useful information for planning and evaluating health and educational policies, among others.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
The share of the population <15 (%) by sex is calculated based on the total population of the respective sex. The proportion was calculated by PAHO
The share of the population <15 (%) by sex is calculated based on the total population of the respective sex. The proportion was calculated by PAHO
Interpretation - example
In 2018, 28.6% of the population of country A was under the age of 15.
Desagregation
By sex
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths and births in the system, and the integrity of the registry.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from: https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from: https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Population aged 65 and over (%)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Proportion of population aged 65 and over, compared to the total population, residing within a given country, territory, or geographical area, estimated at a specific point in time, usually at mid-year (1 July).
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/Predicted
Purpose
This indicator reflects the relative weight of the population aged 65 and over in the general population of a country or geographical area and is required to estimate numerous demographic indicators, including the dependency ratio.
It reflects aging in a given country or geographical area.
The indicator is used for planning, management, and evaluation of public policies related to health and social security, specifically for older adults.
It reflects aging in a given country or geographical area.
The indicator is used for planning, management, and evaluation of public policies related to health and social security, specifically for older adults.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
The share of the population 65 years and over by sex is calculated based on the total population of the respective sex. The proportion was calculated by PAHO.
The share of the population 65 years and over by sex is calculated based on the total population of the respective sex. The proportion was calculated by PAHO.
Interpretation - example
According to 2018 data, 5.4% of the inhabitants of country A were 65 or older. This means that, for every 100 people residing in this country during 2018, five were 65 or older.
Desagregation
By sex
Limitations
The international comparability of this indicator may be limited by factors such as the quality of population censuses, demographic surveys, and national civil registration systems used to calculate estimates. The value of the indicator also depends on civil registry coverage and whether births and deaths are recorded in a timely manner in this system.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Avaiable from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Avaiable from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Population at risk of malaria (%)
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The percentage of the total population of a country, territory, or geographical area living in areas considered at risk of malaria, where locally acquired cases of malaria have appeared. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Malaria is an often-deadly parasitic disease, borne by female Anopheles mosquitoes. Despite the associated risk, especially for children, the disease is preventable and curable. Prevention is based on anti-vector measures, such as insecticide-treated nets and intra-household residual spraying, and prophylactic treatment, only in case of travel to an endemic area, because it can stop the infection in its blood stage, preventing the disease. The second pillar of malaria control is early diagnosis and treatment to reduce incidence, prevent deaths, and contribute to reducing transmission.
This indicator provides an estimate of exposure to malaria risk in the population. For a vector-borne disease such as malaria, the at-risk population is determined by the human population exposed to the risk of malaria infection.
This indicator is used to monitor countries progressing towards targets established in the Global Technical Strategy for Malaria 2016–2030 and the objectives of the Monitoring Framework for Universal Health in the Americas.
The indicator is used to make maps of endemic areas and assess the temporal and geographical trends of the disease, identifying at-risk populations in need of strengthened surveillance programs, disease elimination activities, and access to interventions, especially integrated and quality diagnosis and treatment.
This indicator provides an estimate of exposure to malaria risk in the population. For a vector-borne disease such as malaria, the at-risk population is determined by the human population exposed to the risk of malaria infection.
This indicator is used to monitor countries progressing towards targets established in the Global Technical Strategy for Malaria 2016–2030 and the objectives of the Monitoring Framework for Universal Health in the Americas.
The indicator is used to make maps of endemic areas and assess the temporal and geographical trends of the disease, identifying at-risk populations in need of strengthened surveillance programs, disease elimination activities, and access to interventions, especially integrated and quality diagnosis and treatment.
Estimation method
The surveillance system in each country provides the at-risk population numbers. This information is reported annually to the World Health Organization in the World Malaria Report. The percentage is calculated by PAHO according to the total population indicator.
Interpretation - example
In 2019, 25.3% of the population of country A was at risk for malaria. This means that 1 of every 4 inhabitants of country A was exposed to malaria risk.
Desagregation
No disaggregation
Limitations
The value of this indicator depends on the effectiveness of surveillance systems, which in turn may be affected by low diagnostic suspicion and underreporting of cases, the availability of laboratory tests for diagnosis, and whether private health centers report identified cases. Another factor impacting the value of the indicator is inadequate demarcation of the areas considered at risk for malaria.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO). Malaria surveillance, monitoring & evaluation: a reference manual. Washington, D.C., 2018. Available from: https://iris.paho.org/bitstream/handle/10665.2/50648/9789275320563_spa.pdf?ua=1
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). The Seventy-fourth World Health Assembly. Recommitting to accelerate progress towards malaria elimination. Draft Resolution A74/B/CONF./2. May 2021. Available from: https://apps.who.int/gb/ebwha/pdf_files/WHA74/A74_BCONF2-en.pdf
World Health Organization (WHO). WHO malaria terminology. Geneva, 2022. Updated in November 2021. Available from: https://www.who.int/publications/i/item/9789240038400
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). The Seventy-fourth World Health Assembly. Recommitting to accelerate progress towards malaria elimination. Draft Resolution A74/B/CONF./2. May 2021. Available from: https://apps.who.int/gb/ebwha/pdf_files/WHA74/A74_BCONF2-en.pdf
World Health Organization (WHO). WHO malaria terminology. Geneva, 2022. Updated in November 2021. Available from: https://www.who.int/publications/i/item/9789240038400
Population using clean fuels and technology (%)
Domain
Risk factor
Subdomain
Environment
Definition
The proportion of the population of a given country, territory, or geographic area that depends on technology and clean fuels as the main source of household energy for cooking, heating, and lighting. Expressed as a percentage.
Clean fuels and technologies are defined according to the World Health Organization (WHO) indoor air quality guidelines: combustion of household fuel. This includes households that rely primarily on electricity, biogas, natural gas, liquefied petroleum gas (LPG), solar fuels, or alcohol for cooking.
Clean fuels and technologies are defined according to the World Health Organization (WHO) indoor air quality guidelines: combustion of household fuel. This includes households that rely primarily on electricity, biogas, natural gas, liquefied petroleum gas (LPG), solar fuels, or alcohol for cooking.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/predicted
Purpose
Clean air in and around the home is essential for healthy living. The use of solid fuels, such as coal, wood, or animal dung and kerosene for cooking or heating, increases the risk of death from acute lower respiratory diseases, chronic obstructive pulmonary disease, stroke, ischemic heart disease, and lung cancer. This is coupled with deaths and serious injuries from scalds, burns, and poisoning. Currently, household air pollution caused by incomplete combustion of fuels in low-efficiency stoves and lamps is the most significant direct health risk globally. Domestic use of clean technologies and fuels can avoid these risks.
The percentage of the population that uses clean fuels and technologies in the home is an indicator of a population’s health status, level of human development, and degree of well-being. Monitoring this indicator makes it possible to track countries’ progress towards universal access to energy, i.e., electricity and clean fuels and technologies for cooking, heating, and lighting.
This indicator’s value is applicable to the design and evaluation of public policies that promote and facilitate access to clean energy sources and strategies aimed at reducing pollution inside and outside the home.
The percentage of the population that uses clean fuels and technologies in the home is an indicator of a population’s health status, level of human development, and degree of well-being. Monitoring this indicator makes it possible to track countries’ progress towards universal access to energy, i.e., electricity and clean fuels and technologies for cooking, heating, and lighting.
This indicator’s value is applicable to the design and evaluation of public policies that promote and facilitate access to clean energy sources and strategies aimed at reducing pollution inside and outside the home.
Estimation method
This indicator’s value is from World Health Organization (WHO) estimates, based on data on technologies and primary fuels for cooking, collected through nationally representative population censuses and household surveys (Demographic and Health Surveys, Multiple Indicator Cluster Survey, Living Standard Measurement Survey, among others). These data are collected in the WHO Household Energy Database (https://www.who.int/data/gho/data/themes/air-pollution/who-household-energy-db). The method for estimating this indicator for the total population, urban and rural, in a given year is obtained separately, using a multilevel model. The model only accounts for regions, countries, and time as a spline function, and estimates are restricted to values ranging from zero to one.
More background on the methodology in:
Stoner O, Shaddick G, Economou T, et al. Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. Journal of the Royal Statistical Society Series C: Applied Statistics, 2020. Vol 69: 815–839. Available from:
https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12428
More background on the methodology in:
Stoner O, Shaddick G, Economou T, et al. Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. Journal of the Royal Statistical Society Series C: Applied Statistics, 2020. Vol 69: 815–839. Available from:
https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12428
Interpretation - example
In 2019, the proportion of the population of country A using clean fuels and technology was 64.3%; that is, in that country, 64 out of every 100 people used fuels such as electricity, biogas, natural gas, liquefied petroleum gas (LPG), solar fuels, or alcohol as their main source of energy for cooking, heating, or lighting.
Desagregation
By urban, rural area
Limitations
This indicator is calculated based on the main type of fuels and technology used for cooking, heating, and lighting, as a proxy for calculating household air pollution exposure and the related disease burden. However, many households use more than one type of fuel, depending on weather and geographic conditions. Another limiting factor for estimating this indicator is that there is currently a limited amount of available data that capture the type of fuel and devices used in the home for heating and lighting.
Applying advanced statistical methods to estimate this indicator may determine that its value differs from each country’s calculation. Other influencing factors are the quality and periodicity of the surveys and population censuses used as a data source.
Applying advanced statistical methods to estimate this indicator may determine that its value differs from each country’s calculation. Other influencing factors are the quality and periodicity of the surveys and population censuses used as a data source.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-phe-primary-reliance-on-clean-fuels-and-technologies-proportion
https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-phe-primary-reliance-on-clean-fuels-and-technologies-proportion
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 7.1.2 Proportion of population with primary reliance on clean fuels and technology.
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 18.e Proportion of population with primary reliance on clean fuels and technology
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 7.1.2 Proportion of population with primary reliance on clean fuels and technology.
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 18.e Proportion of population with primary reliance on clean fuels and technology
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C, 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). WHO indoor air quality guidelines: household fuel combustion. Geneva, 2014. Available from:
https://www.who.int/health-topics/air-pollution#tab=tab_1
World Health Organization (WHO). Road map for an enhanced global response to the adverse health effects of air pollution. World Health Assembly, 71. (2018). Available from: https://apps.who.int/iris/handle/10665/276321
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). WHO indoor air quality guidelines: household fuel combustion. Geneva, 2014. Available from:
https://www.who.int/health-topics/air-pollution#tab=tab_1
World Health Organization (WHO). Road map for an enhanced global response to the adverse health effects of air pollution. World Health Assembly, 71. (2018). Available from: https://apps.who.int/iris/handle/10665/276321
Population using improved sanitation facilities; safely managed (%)
Domain
Risk factor
Subdomain
Environment
Definition
The proportion of the population of a specific country, territory, or geographic area using an improved sanitation facility that is not shared with other households and where excreta is treated and disposed of on-site, temporarily stored and then emptied and transported for off-site treatment, or transported through a sewer with wastewater and then treated off-site. Expressed as a percentage.
Improved sanitation facilities are those designed to hygienically separate excreta from human contact. They include discharge or dumping into a sewer system, septic tanks or pit latrines, pit latrines with slabs (including ventilated pit latrines), and composting toilets.
Improved sanitation facilities are those designed to hygienically separate excreta from human contact. They include discharge or dumping into a sewer system, septic tanks or pit latrines, pit latrines with slabs (including ventilated pit latrines), and composting toilets.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/predicted
Purpose
Access to adequate, equitable sanitation services is considered a human right. A population with poor sanitation has a higher risk of getting sick and dying from diseases such as cholera, dysentery, hepatitis A, typhoid fever, and poliomyelitis, among others. This is particularly true for children, among whom it also causes malnutrition, stunting, and increased susceptibility to opportunistic infections such as pneumonia, measles, and malaria. It also increases the risk of roundworms, schistosomiasis, and trachoma.
This indicator reflects a population’s living conditions and level of well-being and social and economic development. A lack of adequate sanitation services is a marker of poverty; it has a variety of consequences, such as anxiety, risk of sexual assault, and loss of educational opportunities.
The percentage of the population that uses an improved sanitation facility makes it possible to monitor the effectiveness of public policies aimed at reducing poverty and morbidity and mortality in the population and provides evidence for more efficiently distributing economic, human, and technological resources.
This indicator reflects a population’s living conditions and level of well-being and social and economic development. A lack of adequate sanitation services is a marker of poverty; it has a variety of consequences, such as anxiety, risk of sexual assault, and loss of educational opportunities.
The percentage of the population that uses an improved sanitation facility makes it possible to monitor the effectiveness of public policies aimed at reducing poverty and morbidity and mortality in the population and provides evidence for more efficiently distributing economic, human, and technological resources.
Estimation method
This indicator’s value is from estimates by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP). This program is the the custodian of global data on water, sanitation, and hygiene (WASH). For this, it collects standardized data from nationally representative population censuses and household surveys, as well as administrative data from sectoral authorities.
The proportion of the population using improved sanitation facilities, as well as the proportion who defecate in the open, are estimated through linear regression modeling. Regressions are also created to estimate the population using improved sanitation facilities connected to sewers and septic tanks. The regressions are extrapolated for two years beyond the last available data point, after which coverage remains constant for four years. Estimates for urban and rural areas are done independently, and national estimates are generated as weighted averages of the two.
After data are collected and prior to publication, the JMP formally sends preliminary estimates to the countries for consultation and review.
More background on the methodology in:
The WHO/UNICEF Joint Monitoring Programme (JMP). Methods.
Available from: https://washdata.org/monitoring/methods
The proportion of the population using improved sanitation facilities, as well as the proportion who defecate in the open, are estimated through linear regression modeling. Regressions are also created to estimate the population using improved sanitation facilities connected to sewers and septic tanks. The regressions are extrapolated for two years beyond the last available data point, after which coverage remains constant for four years. Estimates for urban and rural areas are done independently, and national estimates are generated as weighted averages of the two.
After data are collected and prior to publication, the JMP formally sends preliminary estimates to the countries for consultation and review.
More background on the methodology in:
The WHO/UNICEF Joint Monitoring Programme (JMP). Methods.
Available from: https://washdata.org/monitoring/methods
Interpretation - example
In 2019, 60% of the population of country A used improved sanitation facilities. This means that 60 out of 100 people were using an improved sanitation facility that is not shared with other households and where excreta is safely disposed of on-site or treated off-site.
Desagregation
By urban, rural area
Limitations
The JMP’s estimate for this indicator is based on national data sources approved as official statistics. There may be discrepancies with the value calculated by countries due to differences in the indicators’ definitions and the methods used to calculate national coverage. In some cases, national estimates are based on the most recent data point and not on the regression of all data points, which is the JMP’s method. This indicator’s value is also affected by the fact that national calculations could be based on administrative sector data, rather than nationally representative surveys and censuses, which are the priority sources consulted by the JMP.
Another aspect influencing the estimates is that, in some countries, sewerage connection and septic tank estimates are only available at the national level, while improved sanitation can be calculated in rural and urban areas. This involves using a weighted average of the national estimate of improved sanitation (not shared), and it is divided into sewer system, septic tanks and improved latrines, and others.
Another aspect influencing the estimates is that, in some countries, sewerage connection and septic tank estimates are only available at the national level, while improved sanitation can be calculated in rural and urban areas. This involves using a weighted average of the national estimate of improved sanitation (not shared), and it is divided into sewer system, septic tanks and improved latrines, and others.
Data source(s)
The WHO/UNICEF Joint Monitoring Programme (JMP). Available from:
https://washdata.org/
https://washdata.org/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 6.2.1 Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water [Proporción de la población que utiliza: a) servicios de saneamiento gestionados sin riesgos y b) instalaciones para el lavado de manos con agua y jabón].
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 18.d Proportion of population using safely managed sanitation services, including a hand-washing facility with soap and water
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 6.2.1 Proportion of population using (a) safely managed sanitation services and (b) a hand-washing facility with soap and water [Proporción de la población que utiliza: a) servicios de saneamiento gestionados sin riesgos y b) instalaciones para el lavado de manos con agua y jabón].
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 18.d Proportion of population using safely managed sanitation services, including a hand-washing facility with soap and water
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
WHO/UNICEF. WASH in the 2030 Agenda, 2017. Available from:
https://data.unicef.org/resources/wash-2030-agenda
United Nations. UN Water. Available from:
https://www.unwater.org/
World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF). Progress on drinking water, sanitation, and hygiene: 2017 update and SDG baselines. Geneva, 2017. Available from:
https://data.unicef.org/resources/progress-drinking-water-sanitation-hygiene-2017-update-sdg-baselines/
https://iris.paho.org/handle/10665.2/53918
WHO/UNICEF. WASH in the 2030 Agenda, 2017. Available from:
https://data.unicef.org/resources/wash-2030-agenda
United Nations. UN Water. Available from:
https://www.unwater.org/
World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF). Progress on drinking water, sanitation, and hygiene: 2017 update and SDG baselines. Geneva, 2017. Available from:
https://data.unicef.org/resources/progress-drinking-water-sanitation-hygiene-2017-update-sdg-baselines/
Population using improved water supplies; safely managed (%)
Domain
Risk factor
Subdomain
Environment
Definition
The proportion of the population of a specific country, territory, or geographic area using improved source water supplies that are located on-site, available when needed, and free of fecal and chemical contamination. Expressed as a percentage.
Improved water supplies are those that, by the nature of their design and construction, have the potential to supply drinking water. They include running water, boreholes or tube wells, protected dug wells, protected springs, and bottled or distributed water.
Improved water supplies are those that, by the nature of their design and construction, have the potential to supply drinking water. They include running water, boreholes or tube wells, protected dug wells, protected springs, and bottled or distributed water.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/predicted
Purpose
Access to drinking water for personal and household use is a human right and one of the basic fundamentals of public health. It determines the success or failure of various strategies for reducing morbidity and mortality in a population, especially among children. Many diseases are waterborne and have the potential to cause outbreaks and a high number of deaths, including cholera, typhoid fever, diarrheal diseases, viral hepatitis A, dysentery, and Guinea worm disease. Lack of access to water supplies increases the risk of contracting such diseases.
Searching for a water supply adversely impacts health and a variety of other areas. Women and children generally perform this task, which takes up time that could be spent on other activities, such as education, childcare, labor market participation, and rest and recreation, among others.
This indicator reflects a population’s living conditions and level of development. It reflects progress in the fight against poverty, diseases, and death. Depending on the case, it can boost economic productivity.
This indicator is part of the Monitoring Framework for Universal Health in the Americas, to measure national progress in implementing policies aimed at strengthening health systems and achieving universal health.
Searching for a water supply adversely impacts health and a variety of other areas. Women and children generally perform this task, which takes up time that could be spent on other activities, such as education, childcare, labor market participation, and rest and recreation, among others.
This indicator reflects a population’s living conditions and level of development. It reflects progress in the fight against poverty, diseases, and death. Depending on the case, it can boost economic productivity.
This indicator is part of the Monitoring Framework for Universal Health in the Americas, to measure national progress in implementing policies aimed at strengthening health systems and achieving universal health.
Estimation method
This indicator’s value is from estimates by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP). This program is the custodian of global water, sanitation, and hygiene (WASH) data, routinely collecting information on water supply availability and quality and its regulation by relevant authorities through consultation with government departments responsible for drinking water supply provision and regulation. Household surveys and censuses provide information on the types of basic water supplies and also indicate whether they are located on-site. These data sources often have information on water availability, as well as water quality at the household level, through direct analysis of drinking water to detect fecal or chemical contamination. These data are combined with data on the availability of and compliance with drinking water quality standards (fecal and chemical) from regulatory bodies or administrative reports.
The population using improved water supplies and the proportion with improved water supplies on-site are estimated using linear regression. Additionally, regressions are created to independently estimate three parameters of the level of service: (1) the proportion of the population that drinks water from improved water supplies that are accessible on-site; (2) the proportion of the population that drinks water from improved water supplies that are available when needed (i.e., households can access sufficient amounts of water when needed); and (3) the proportion of the population that drinks water from improved water supplies that meet relevant national or local standards (if no standards are available, the WHO Guidelines for Drinking Water Quality are used). Estimates for urban and rural areas are done independently. National estimates are generated as weighted averages of urban and rural estimates, using population data from the most recent United Nations Population Division report.
Applying this standard classification and estimation method allows the JMP to compare progress across countries, regions, and the world. The JMP routinely consults with national authorities before publishing country estimates.
More background on the methodology in:
The WHO/UNICEF Joint Monitoring Programme (JMP). Methods. Available from:
https://washdata.org/monitoring/methods
The population using improved water supplies and the proportion with improved water supplies on-site are estimated using linear regression. Additionally, regressions are created to independently estimate three parameters of the level of service: (1) the proportion of the population that drinks water from improved water supplies that are accessible on-site; (2) the proportion of the population that drinks water from improved water supplies that are available when needed (i.e., households can access sufficient amounts of water when needed); and (3) the proportion of the population that drinks water from improved water supplies that meet relevant national or local standards (if no standards are available, the WHO Guidelines for Drinking Water Quality are used). Estimates for urban and rural areas are done independently. National estimates are generated as weighted averages of urban and rural estimates, using population data from the most recent United Nations Population Division report.
Applying this standard classification and estimation method allows the JMP to compare progress across countries, regions, and the world. The JMP routinely consults with national authorities before publishing country estimates.
More background on the methodology in:
The WHO/UNICEF Joint Monitoring Programme (JMP). Methods. Available from:
https://washdata.org/monitoring/methods
Interpretation - example
In 2019, 89% of the population of country A used improved water supplies. This means that nearly 9 out of 10 people use improved, uncontaminated water supplies located on-site and available when needed.
Desagregation
By urban, rural area
Limitations
The JMP’s estimate for this indicator is based on national data sources approved as official statistics. There may be discrepancies with the value calculated by the countries due to differences in the indicators’ definitions and the methods used to calculate national coverage. In some cases, national estimates are based on the most recent data point and not on the regression of all data points, which is the JMP’s approach. This indicator’s value is also affected by the fact that national calculations could be based on administrative sector data, rather than nationally representative surveys and censuses, which are the sources consulted by the JMP.
Data source(s)
The WHO/UNICEF Joint Monitoring Programme (JMP). Available from:
https://washdata.org/
https://washdata.org/
Update periodicity PAHO
2 to 3 years
Link to SDG / SP20-25
United Nations. Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 6.1.1 Proportion of population using safely managed drinking water services.
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 18.c Proportion of population using safely managed drinking water services
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 6.1.1 Proportion of population using safely managed drinking water services.
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 18.c Proportion of population using safely managed drinking water services
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
WHO/UNICEF. WASH in the 2030 Agenda, 2017. Available from:
https://data.unicef.org/resources/wash-2030-agenda/
United Nations. UN Water. Available from:
https://www.unwater.org/
World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF). Progress on drinking water, sanitation, and hygiene: 2017 update and SDG baselines. Geneva, 2017. Available from:
https://data.unicef.org/resources/progress-drinking-water-sanitation-hygiene-2017-update-sdg-baselines/
https://iris.paho.org/handle/10665.2/53918
WHO/UNICEF. WASH in the 2030 Agenda, 2017. Available from:
https://data.unicef.org/resources/wash-2030-agenda/
United Nations. UN Water. Available from:
https://www.unwater.org/
World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF). Progress on drinking water, sanitation, and hygiene: 2017 update and SDG baselines. Geneva, 2017. Available from:
https://data.unicef.org/resources/progress-drinking-water-sanitation-hygiene-2017-update-sdg-baselines/
Postneonatal mortality rate (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
The quotient between deaths of children that occurred between the age of 28 and 364 days in a given country, territory, or geographic area for a specific calendar year, and the total number of live births for the same population and year. Expressed per 1 000 live births.
Live birth means the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, that, after such expulsion or extraction, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered a live birth.
Live birth means the complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of pregnancy, that, after such expulsion or extraction, breathes or shows any other evidence of life, such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. Each product of such childbirth that meets these conditions is considered a live birth.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Crude
Purpose
This indicator reflects the risk of a child born alive dying between the age of 28 and 364 days and highlights the significance of postneonatal mortality as a public health problem.
The estimated postneonatal mortality rate reflects the health status and social, economic, and environmental conditions in which a population lives, particularly the infant population. It makes it possible to identify health inequities and populations with specific risk factors such as environmental risks, malnutrition, or multidimensional poverty and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health, particularly prenatal and neonatal care. Its value helps strengthen professional care for childbirth, breastfeeding, and the Expanded Program on Immunization.
It allows for analysis of the geographic and time trend of neonatal mortality in a given population.
The estimated postneonatal mortality rate reflects the health status and social, economic, and environmental conditions in which a population lives, particularly the infant population. It makes it possible to identify health inequities and populations with specific risk factors such as environmental risks, malnutrition, or multidimensional poverty and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health, particularly prenatal and neonatal care. Its value helps strengthen professional care for childbirth, breastfeeding, and the Expanded Program on Immunization.
It allows for analysis of the geographic and time trend of neonatal mortality in a given population.
Estimation method
The postneonatal mortality rate uses deaths between the age of 28 and 364 days of children who were born alive in a given country, territory, or geographic area, as reported by the national health agency for a specific calendar year as the numerator, and the total number of live births for the same population and year as the denominator.
Formula:
(A/B) x 1 000 live births
Numerator (A):
Number of children born alive who died between the age of 28 and 364 days in a given country, territory and geographic area during year z
Denominator (B):
Total number of live births in the same population, during year z
Formula:
(A/B) x 1 000 live births
Numerator (A):
Number of children born alive who died between the age of 28 and 364 days in a given country, territory and geographic area during year z
Denominator (B):
Total number of live births in the same population, during year z
Interpretation - example
Country A’s postneonatal mortality rate for 2019 was 14.3 per 1 000 live births; that is, 14 children born alive died between the age of 28 and 364 days per 1 000 live births in country A.
Desagregation
No disaggregation
Limitations
The postneonatal mortality rate requires a civil registry system with good coverage, and births and deaths must be recorded in a timely manner in this system; otherwise, this indicator will not be sufficiently accurate.
The value of this indicator may differ from each country’s value due to methodological differences, such as the application of methods to correct underreporting of births and deaths.
The value of this indicator may differ from each country’s value due to methodological differences, such as the application of methods to correct underreporting of births and deaths.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.2.2 Neonatal mortality rate.
Available from: https://sdgs.un.org/goals
Indicator 3.2.2 Neonatal mortality rate.
Available from: https://sdgs.un.org/goals
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization, Health Information and Analysis Unit (HA). Regional Core Health Data Initiative. Glossary of Indicators. Washington D.C., June 2015. Available from: https://www.paho.org/hq/dmdocuments/2015/glossary-eng-2014.pdf
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization, Health Information and Analysis Unit (HA). Regional Core Health Data Initiative. Glossary of Indicators. Washington D.C., June 2015. Available from: https://www.paho.org/hq/dmdocuments/2015/glossary-eng-2014.pdf
Poverty headcount ratio at $ 2.15 day (2017 PPP) (% of population)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The percentage of the population of a given country, territory, or geographical area living below the international poverty line in a specific period, usually one year. The international poverty line is set at US$ 2.15 per day per person at international prices according to the last round of the International Comparison Program (ICP) 2017 and reflects the minimum monetary level below which the population is unable to meet its basic needs. The local currency is adjusted based on purchasing power parities (PPP).
Purchasing Power Parity (PPP) is a conversion rate between currencies that considers both exchange rate differences and discrepancies in price levels between countries. Therefore, when used to deflate the corresponding national accounts aggregates, they measure the real size of economies considering the purchasing power of the currency in each country.
Purchasing Power Parity (PPP) is a conversion rate between currencies that considers both exchange rate differences and discrepancies in price levels between countries. Therefore, when used to deflate the corresponding national accounts aggregates, they measure the real size of economies considering the purchasing power of the currency in each country.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
This indicator quantifies in monetary terms the number of inhabitants of a given country who cannot afford their basic needs, such as food, clothing, and housing, among others. The international poverty line allows analysis of changes over time in global extreme poverty.
Monitoring poverty allows assessment of the socioeconomic wellbeing of a population and the impact of implementing socioeconomic policies designed to reduce poverty at the international level. It helps to focus efforts and implement specific measures for the poorest population.
Monitoring poverty allows assessment of the socioeconomic wellbeing of a population and the impact of implementing socioeconomic policies designed to reduce poverty at the international level. It helps to focus efforts and implement specific measures for the poorest population.
Estimation method
The World Bank prepares the international poverty line based on the consumption and income data it collects. The international poverty line is determined by applying a common standard for measuring extreme poverty and adjusting for differences in purchasing power between countries. This line should be updated periodically using new PPP price data to reflect changes in the cost of living over time. The last change was in 2022, when the current extreme poverty line was set at $2.15 per day in terms of 2017 PPP.
Interpretation - example
According to 2019 data, the poverty incidence rate of country A was 25%. This means that, in 2019, 25% of the resident population in country A lived in extreme poverty, with an income of less than US$2.15 per day per person.
Desagregation
No disaggregation
Limitations
As a result of PPP exchange rate revisions, individual country poverty rates cannot be compared with poverty rates reported in previous World Bank reviews.
One of the factors hindering the comparability of international poverty estimates is countries’ different definitions of poverty. Local poverty lines tend to have higher purchasing power in rich countries, which use more generous standards than poor countries.
Because the World Bank obtains income data through household surveys, the accuracy of this indicator is affected by the timeliness, frequency, quality, and comparability of the surveys used. Another factor affecting estimates is the low availability of monitoring data, which complicates analysis of changes in poverty in some countries.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
One of the factors hindering the comparability of international poverty estimates is countries’ different definitions of poverty. Local poverty lines tend to have higher purchasing power in rich countries, which use more generous standards than poor countries.
Because the World Bank obtains income data through household surveys, the accuracy of this indicator is affected by the timeliness, frequency, quality, and comparability of the surveys used. Another factor affecting estimates is the low availability of monitoring data, which complicates analysis of changes in poverty in some countries.
The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from: https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 1.1.1 Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)
Available from: https://sdgs.un.org/goals
Indicator 1.1.1 Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)
Available from: https://sdgs.un.org/goals
References
World Bank Poverty Group. Available from:
https://www.worldbank.org/en/topic/poverty
https://www.worldbank.org/en/topic/poverty
Poverty headcount ratio at national poverty line (% of population)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The national poverty rate is the percentage of the population living below the national poverty line for a given country, territory, or geographic area, in a specific period, usually one year. National estimates are based on weighted subgroup estimates per population, derived from household surveys. The operational definition of the national poverty line usually varies from country to country and represents the amount of income that allows each household to meet the basic needs of all its members.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
This indicator quantifies in monetary terms the number of inhabitants of a given country who cannot afford their basic needs, such as food, clothing, and housing. The national poverty line allows analysis of changes over time in poverty in a given country.
Monitoring poverty allows us to assess the socioeconomic wellbeing of a population and the impact of implementing socioeconomic policies designed to reduce poverty at the national level. It helps to focus efforts and implement specific measures for the poorest population.
Monitoring poverty allows us to assess the socioeconomic wellbeing of a population and the impact of implementing socioeconomic policies designed to reduce poverty at the national level. It helps to focus efforts and implement specific measures for the poorest population.
Estimation method
The value of this indicator comes from estimates made by the World Bank from national poverty lines, which are specific to each country. National poverty lines are calculated using data from nationally representative household surveys and are sufficiently detailed to obtain a comprehensive estimate of total household income or consumption (including consumption or income from own production). This is then used to create a weighted consumption or per capita income distribution.
Almost all national poverty lines in developing economies are based on the cost of a basic food basket that meets the nutritional needs of the population. This basket also considers consumption habits, the effective availability of food in the country and its relative prices, and an estimate of the resources required by households to meet a set of basic non-food needs. Some countries define the extreme poverty line by taking into account only the cost of the basic food basket (i.e., excluding basic non-food needs).
Almost all national poverty lines in developing economies are based on the cost of a basic food basket that meets the nutritional needs of the population. This basket also considers consumption habits, the effective availability of food in the country and its relative prices, and an estimate of the resources required by households to meet a set of basic non-food needs. Some countries define the extreme poverty line by taking into account only the cost of the basic food basket (i.e., excluding basic non-food needs).
Interpretation - example
According to 2019 data, the poverty incidence rate of country A was 25%. This means that, in 2019, 55% of the resident population in country A lived below the national poverty line, with an insufficient income level to afford the current basic basket.
Desagregation
No disaggregation
Limitations
Estimates of national poverty lines are affected by methodological differences in household surveys used to collect consumption and income data. They also depend on the quality, timeliness, and availability of the data collected.
Estimation methodologies and the definition of poverty line differ from one country to another, so national estimates should not be compared between countries. Another factor complicating comparison is that national poverty lines generally rise alongside a country's average income, and therefore do not provide a uniform measure for comparing poverty rates between countries.
Because diets and consumption baskets change over time, national poverty lines must be recalculated periodically using updated data from new household surveys. The new poverty lines need to be adjusted so that inflation between survey years remains constant in real terms, allowing valid comparisons of poverty over time.
Poverty lines are theoretical limits that allow the population to be classified based on monetary aspects of consumption and income, therefore, the number of inhabitants in poverty will increase or decrease depending on where this line is set.
Estimation methodologies and the definition of poverty line differ from one country to another, so national estimates should not be compared between countries. Another factor complicating comparison is that national poverty lines generally rise alongside a country's average income, and therefore do not provide a uniform measure for comparing poverty rates between countries.
Because diets and consumption baskets change over time, national poverty lines must be recalculated periodically using updated data from new household surveys. The new poverty lines need to be adjusted so that inflation between survey years remains constant in real terms, allowing valid comparisons of poverty over time.
Poverty lines are theoretical limits that allow the population to be classified based on monetary aspects of consumption and income, therefore, the number of inhabitants in poverty will increase or decrease depending on where this line is set.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from: https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 1.2.1 Proportion of population living below the national poverty line, by sex and age
Available from: https://sdgs.un.org/goals
Indicator 1.2.1 Proportion of population living below the national poverty line, by sex and age
Available from: https://sdgs.un.org/goals
References
World Bank Poverty Group. Available from:
https://www.worldbank.org/en/topic/poverty
https://www.worldbank.org/en/topic/poverty
Prevalence of anemia in women of reproductive age
Domain
Risk factor
Subdomain
Maternal and reproductive health
Definition
Percentage of women aged 15 to 49 with a hemoglobin concentration less than 120 g/L for non-pregnant and breastfeeding women and less than 110 g/L for pregnant women, adjusted for altitude and tobacco use. This indicator considers the total number of women of reproductive age with anemia, regardless of severity (mild, moderate, or severe).
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected, predicted
Purpose
Anemia is a noncommunicable disease with a high prevalence worldwide, especially in women of childbearing age, girls, and boys. Iron deficiency is the most frequent cause, but it can also be due to deficiency of riboflavin, folic acid, zinc, vitamin B12, and vitamin A or non-nutritional causes. It has serious consequences for health and for social and economic development. In children, it is associated with poor cognitive and motor development, which impairs academic performance. In adulthood, anemia decreases physical work capacity, affecting people’s productivity. In pregnant women, it increases the risk of maternal and perinatal mortality, preterm birth, and having babies with low birth weight and increased risk of death.
The prevalence of anemia in women of childbearing age highlights this disease as a public health problem and makes it possible to identify populations at higher risk that should be targeted by prevention and control strategies.
This indicator is part of the Global Nutrition Surveillance Framework that monitors countries’ progress towards achieving the targets set out in the Comprehensive Implementation Plan on Maternal Infant and Young Child Nutrition, which include reducing anemia in women of childbearing age by 50%.
The prevalence of anemia in women of childbearing age highlights this disease as a public health problem and makes it possible to identify populations at higher risk that should be targeted by prevention and control strategies.
This indicator is part of the Global Nutrition Surveillance Framework that monitors countries’ progress towards achieving the targets set out in the Comprehensive Implementation Plan on Maternal Infant and Young Child Nutrition, which include reducing anemia in women of childbearing age by 50%.
Estimation method
This indicator’s value is from World Health Organization (WHO) estimates, based on data collected mainly through nationally representative population surveys that consider the measurement of hemoglobin concentration in blood in women aged 15 to 49. These surveys include Demographic and Health Surveys, Multiple Indicator Cluster Surveys, Reproductive Health Surveys, and Malaria Indicator Surveys. Data are collected in WHO’s Vitamin and Mineral Nutrition Information System (VMNIS) (https://www.who.int/teams
utrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system), which also has data on other micronutrients in the population.
Hemoglobin distribution is estimated by applying hierarchical Bayesian models, which are also used to analyze missing data, nonlinear temporal trends, and data representativeness. The model calculates estimates for each country and year, informed by data from that country and year, if available, and by data from other years in the same country and from other countries with data for similar time periods, especially countries in the same region.
Hemoglobin values should be adjusted downwards for smokers or people who live more than 1000 meters above sea level, because hemoglobin rises in both cases and could lead to underestimating the prevalence of anemia in these populations.
Before the results are published, they are sent to the countries for consultation, together with an explanation of the methodology used.
More details on the methodology in:
Stevens G., Finucane M., and on behalf of Nutrition Impact Model Study Group (Anaemia). Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data. Lancet Glob Health. Jul 1 (1): e16-e25. Available from:
https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(13)70001-9/fulltext
utrition-and-food-safety/databases/vitamin-and-mineral-nutrition-information-system), which also has data on other micronutrients in the population.
Hemoglobin distribution is estimated by applying hierarchical Bayesian models, which are also used to analyze missing data, nonlinear temporal trends, and data representativeness. The model calculates estimates for each country and year, informed by data from that country and year, if available, and by data from other years in the same country and from other countries with data for similar time periods, especially countries in the same region.
Hemoglobin values should be adjusted downwards for smokers or people who live more than 1000 meters above sea level, because hemoglobin rises in both cases and could lead to underestimating the prevalence of anemia in these populations.
Before the results are published, they are sent to the countries for consultation, together with an explanation of the methodology used.
More details on the methodology in:
Stevens G., Finucane M., and on behalf of Nutrition Impact Model Study Group (Anaemia). Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data. Lancet Glob Health. Jul 1 (1): e16-e25. Available from:
https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(13)70001-9/fulltext
Interpretation - example
In 2019, the prevalence of anemia in women of childbearing age in country A was 24.8%. This means that 25 out of every 100 women aged 15 to 49 had blood hemoglobin levels less than 120 g/L (non-pregnant or breastfeeding women) or less than 110 g/L (pregnant women).
Desagregation
No disaggregation
Limitations
Because this indicator is estimated based on the application of advanced statistical methods, the results may differ from the countries’ values. Among the factors that influence it are those related to the survey that obtained the data, for example, the sampling error and non-sampling error, due to problems with the sample design or measurement. It should also be noted that the estimate is limited by the low amount of data on blood hemoglobin concentrations currently available.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every 2-3 years
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 2.2.3 Prevalence of anaemia in women aged 15 to 49, by pregnancy status (percentage).
Available from: https://sdgs.un.org/
Indicator 2.2.3 Prevalence of anaemia in women aged 15 to 49, by pregnancy status (percentage).
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core Indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Global nutrition monitoring framework: operational guidance for tracking progress in meeting targets for 2025. Geneva, 2018. Available from:
https://www.who.int/es/publications/i/item/9789241513609
World Health Organization (WHO). Comprehensive implementation plan on maternal, infant, and young child nutrition. Geneva, 2014. Available from: https://apps.who.int/iris/bitstream/handle/10665/113048/WHO_NMH_NHD_14.1_eng.pdf
https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Global nutrition monitoring framework: operational guidance for tracking progress in meeting targets for 2025. Geneva, 2018. Available from:
https://www.who.int/es/publications/i/item/9789241513609
World Health Organization (WHO). Comprehensive implementation plan on maternal, infant, and young child nutrition. Geneva, 2014. Available from: https://apps.who.int/iris/bitstream/handle/10665/113048/WHO_NMH_NHD_14.1_eng.pdf
Prevalence of children under age 5 who are stunted (%)
Domain
Risk factor
Subdomain
Child health
Definition
Percentage of children under 5 years of age who are stunted, defined as a height (or length) for age less than -2 standard deviations below the median World Health Organization (WHO) Child Growth Standards, in a given country, territory, or geographic area, in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Child stunting is the result of poor nutritional status. It is associated with lower physical and cognitive development, greater susceptibility to common infectious diseases, greater nutritional decline and stunting, and an increased risk of death. It is closely related to poor nutritional status of the mother.
This indicator reflects the health status of a population, enabling identification of disadvantaged populations and inequalities in human development. It contributes to designing and evaluating public policies aimed at reducing health inequities, especially in maternal and child nutrition.
The percentage of stunted children is one of the indicators of the Global Nutrition Surveillance Framework (GNMF) on maternal, infant, and child nutrition. It is used to monitor countries' progress towards achieving the World Health Assembly nutrition targets and implementing the Comprehensive Implementation Plan on Maternal, Infant, and Young Child Nutrition.
This indicator reflects the health status of a population, enabling identification of disadvantaged populations and inequalities in human development. It contributes to designing and evaluating public policies aimed at reducing health inequities, especially in maternal and child nutrition.
The percentage of stunted children is one of the indicators of the Global Nutrition Surveillance Framework (GNMF) on maternal, infant, and child nutrition. It is used to monitor countries' progress towards achieving the World Health Assembly nutrition targets and implementing the Comprehensive Implementation Plan on Maternal, Infant, and Young Child Nutrition.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO) based on data from national population surveys. Estimates of the indicator are based on a standardized methodology that uses WHO child growth patterns and forecasts future trends.
UNICEF, WHO, and the World Bank Group are jointly reviewing new data sources to update country-level estimates. These sources include the WHO Global Database on Child Growth and Malnutrition [https://www.who.int/teams
utrition-and-food-safety/databases
utgrowthdb], Country Reporting on Indicators for Goals (CRING), and the World Bank's Living Standard Measurement Surveys (LSMS) [https://www.worldbank.org/en/programs/lsms]. Each of these sources of information involves advanced statistical methods that enable reliable and internationally comparable data.
Further details on the estimation method:
UNICEF-WHO-The World Bank Group. 2021 Joint child malnutrition estimates - Levels and trends in child malnutrition. Available from: https://www.who.int/publications/i/item/9789240025257
De Onis, Bloessner M. The World Health Organization Global Database on Child Growth and Malnutrition: methodology and applications. International Journal of Epidemiology. 2003; 32:518–26. Available from: https://academic.oup.com/ije/article/32/4/518/666947?login=false
De Onis M, Blössner M, Borghi E, Morris R, Frongillo EA. Methodology for estimating regional and global trends of child malnutrition. International Journal of Epidemiology. 2004;33: 1260–70. Available from: https://academic.oup.com/ije/article/33/6/1260/866406
Further background on child growth patterns:
World Health Organization (WHO). Child growth standards. Available from: https://www.who.int/toolkits/child-growth-standards
UNICEF, WHO, and the World Bank Group are jointly reviewing new data sources to update country-level estimates. These sources include the WHO Global Database on Child Growth and Malnutrition [https://www.who.int/teams
utrition-and-food-safety/databases
utgrowthdb], Country Reporting on Indicators for Goals (CRING), and the World Bank's Living Standard Measurement Surveys (LSMS) [https://www.worldbank.org/en/programs/lsms]. Each of these sources of information involves advanced statistical methods that enable reliable and internationally comparable data.
Further details on the estimation method:
UNICEF-WHO-The World Bank Group. 2021 Joint child malnutrition estimates - Levels and trends in child malnutrition. Available from: https://www.who.int/publications/i/item/9789240025257
De Onis, Bloessner M. The World Health Organization Global Database on Child Growth and Malnutrition: methodology and applications. International Journal of Epidemiology. 2003; 32:518–26. Available from: https://academic.oup.com/ije/article/32/4/518/666947?login=false
De Onis M, Blössner M, Borghi E, Morris R, Frongillo EA. Methodology for estimating regional and global trends of child malnutrition. International Journal of Epidemiology. 2004;33: 1260–70. Available from: https://academic.oup.com/ije/article/33/6/1260/866406
Further background on child growth patterns:
World Health Organization (WHO). Child growth standards. Available from: https://www.who.int/toolkits/child-growth-standards
Interpretation - example
In 2019, the percentage of children under 5 years of age with stunting was 35.6% in country A. This means that of the total number of children aged 0 to 59 months living in country A, 36% are considered stunted.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data used for calculation. The main source of the data are surveys. As a result, limiting factors include survey coverage, completeness, frequency of updates, and the time of year in which it is applied. Because stunting is a result of chronic or recurring malnutrition, other factors that can affect its value are the availability of food at the time of the survey, presence of seasonal diseases, and natural disasters or conflicts, all of which may mean the temporal trend of the indicator at the country level is not always comparable.
Advanced statistical methods were applied to estimate this indicator; therefore, its value may differ from the calculations made by each country.
Advanced statistical methods were applied to estimate this indicator; therefore, its value may differ from the calculations made by each country.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 2.2.1: Prevalence of stunting (height for age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14.a Prevalence of stunting in children under 5 years of age
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 2.2.1: Prevalence of stunting (height for age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14.a Prevalence of stunting in children under 5 years of age
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Indicators for the Global Monitoring Framework on Maternal, Infant and Young Child Nutrition, 2014. Available from: https://cdn.who.int/media/docs/default-source
utritionlibrary/global-targets-2025/indicators_monitoringframework_miycn_background.pdf?sfvrsn=b1934036_6
World Health Organization (WHO). Global Nutrition Monitoring Framework – Operational guidance for tracking progress in meeting targets for 2025. Geneva, 2018. Available from: https://www.who.int/publications/i/item/9789241513609
World Health Organization (WHO). Maternal, infant, and young child nutrition, December 2019. Available from: https://apps.who.int/gb/ebwha/pdf_files/EB146/B146_24-en.pdf
UNICEF Data: Monitoring the situation of children and women. Available from: https://data.unicef.org/
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Indicators for the Global Monitoring Framework on Maternal, Infant and Young Child Nutrition, 2014. Available from: https://cdn.who.int/media/docs/default-source
utritionlibrary/global-targets-2025/indicators_monitoringframework_miycn_background.pdf?sfvrsn=b1934036_6
World Health Organization (WHO). Global Nutrition Monitoring Framework – Operational guidance for tracking progress in meeting targets for 2025. Geneva, 2018. Available from: https://www.who.int/publications/i/item/9789241513609
World Health Organization (WHO). Maternal, infant, and young child nutrition, December 2019. Available from: https://apps.who.int/gb/ebwha/pdf_files/EB146/B146_24-en.pdf
UNICEF Data: Monitoring the situation of children and women. Available from: https://data.unicef.org/
Prevalence of current tobacco use in adolescents (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
Percentage of adolescents in a given country, territory, or geographic area who currently use any tobacco product. Current users are defined as those who used any tobacco product, smoked or smokeless, at least once during the 30 days prior to the survey.
Adolescents are defined as students aged 13 to 15 or according to the definition of each country.
"Smoked tobacco products" include cigarettes, beedis, cigars, pipes, water pipes (hookah, shisha), finely cut smoking tobacco (roll-your-own cigarettes), kreteks, or any other form of smoking tobacco.
"Smokeless tobacco" includes moist tobacco, creamy tobacco, solvents, dry tobacco, gul, loose leaves, red tooth powder, snus, chimo, gutka, khaini, gudakhu, zarda, qiwam, dohra, tuibur, nasway, naas
aswar, shammah, betel quid, toombak, pan (betel quid), iq'mik, mishri, tapkeer, tombol, and any other tobacco product that is inhaled, kept in the mouth, or chewed.
Also included as tobacco products are heated tobacco products (HTPs, known by the brands Iqos, glo, Ploom TECH).
Electronic nicotine delivery or non-nicotine delivery systems known as "e-cigarettes," "e-hookahs," JUUL, and "e-pipes" are not classified as tobacco products because they do not contain this substance.
Adolescents are defined as students aged 13 to 15 or according to the definition of each country.
"Smoked tobacco products" include cigarettes, beedis, cigars, pipes, water pipes (hookah, shisha), finely cut smoking tobacco (roll-your-own cigarettes), kreteks, or any other form of smoking tobacco.
"Smokeless tobacco" includes moist tobacco, creamy tobacco, solvents, dry tobacco, gul, loose leaves, red tooth powder, snus, chimo, gutka, khaini, gudakhu, zarda, qiwam, dohra, tuibur, nasway, naas
aswar, shammah, betel quid, toombak, pan (betel quid), iq'mik, mishri, tapkeer, tombol, and any other tobacco product that is inhaled, kept in the mouth, or chewed.
Also included as tobacco products are heated tobacco products (HTPs, known by the brands Iqos, glo, Ploom TECH).
Electronic nicotine delivery or non-nicotine delivery systems known as "e-cigarettes," "e-hookahs," JUUL, and "e-pipes" are not classified as tobacco products because they do not contain this substance.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude/corrected
Purpose
Tobacco use is one of the leading risk factors for acquiring and dying from noncommunicable diseases. Tobacco addiction usually begins in adolescence, before the perception of risk develops. Therefore, the prevalence of smoking in adolescents helps to identify populations in need of improved comprehensive health care and focused strategies for smoking prevention and control, as well as promotion of healthy lifestyles.
This indicator is part of the Global Monitoring Framework on NCDs. It allows country progress on implementing strategies and national plans to control noncommunicable diseases to be monitored. It is also used to monitor the impact of implementing the Framework Convention of the World Health Organization (WHO) for Tobacco Control or tobacco control policies in countries not yet party to this convention, and to monitor country progress on achieving the Sustainable Health Agenda for the Americas 2018–2030.
Its result contributes to developing public policies to prevent and control noncommunicable diseases and to allocate human, economic, and technological resources to this field. The indicator contributes to multidisciplinary research in the field of noncommunicable diseases.
This indicator is part of the Global Monitoring Framework on NCDs. It allows country progress on implementing strategies and national plans to control noncommunicable diseases to be monitored. It is also used to monitor the impact of implementing the Framework Convention of the World Health Organization (WHO) for Tobacco Control or tobacco control policies in countries not yet party to this convention, and to monitor country progress on achieving the Sustainable Health Agenda for the Americas 2018–2030.
Its result contributes to developing public policies to prevent and control noncommunicable diseases and to allocate human, economic, and technological resources to this field. The indicator contributes to multidisciplinary research in the field of noncommunicable diseases.
Estimation method
The prevalence of current tobacco use in adolescents is obtained from data from national and subnational school-based population surveys compiled by the PAHO Department of Noncommunicable Diseases and Mental Health (NMH). The main sources of data are the Global Youth Tobacco Survey (GYTS) and the Global School-based Student Health Survey (GSHS). The estimation of this indicator for each country, and separately for men and women, is obtained from weighted samples adjusted for unequal selection probabilities, lack of response, and disproportionate selection of different population groups, including by sex and grade. When reporting point estimates, it is suggested to also report the upper and lower bound 95% confidence interval. For GYTS point estimates, the minimum recommended sample size (n) of the denominator is 35 unweighted cases.
Calculation with data from each country
Formula:
(A/B) x 100%
Numerator (A):
Number of adolescent students who respond to the survey and who currently use* tobacco (daily or less frequently).
*"Current users" include students who have used smoked or smokeless tobacco at any time in the 30 days prior to the survey.
Denominator (B):
Total number of adolescents (students) who respond to the survey.
Further background on the methodology:
WHO global report on trends in prevalence of tobacco use 2000–2025, third edition. Geneva: World Health Organization; 2019. Available from: https://www.who.int/publications/i/item/who-global-report-on-trends-in-prevalence-of-tobacco-use-2000-2025-third-edition
Calculation with data from each country
Formula:
(A/B) x 100%
Numerator (A):
Number of adolescent students who respond to the survey and who currently use* tobacco (daily or less frequently).
*"Current users" include students who have used smoked or smokeless tobacco at any time in the 30 days prior to the survey.
Denominator (B):
Total number of adolescents (students) who respond to the survey.
Further background on the methodology:
WHO global report on trends in prevalence of tobacco use 2000–2025, third edition. Geneva: World Health Organization; 2019. Available from: https://www.who.int/publications/i/item/who-global-report-on-trends-in-prevalence-of-tobacco-use-2000-2025-third-edition
Interpretation - example
According to 2019 data, in country A, the prevalence of current tobacco use in adolescents is 20.9%. This means that, in this country, 21 out of every 100 adolescent students aged 13 to 15 consume a smoked or smokeless tobacco product daily or occasionally.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the quality of the data and the frequency of updated responses from school-based population surveys. It is also worth noting that tobacco use is measured by self-reporting from the respondents, which is affected by different biases, e.g., memory bias or level of understanding of the questions included in the survey.
Although several countries collect data on youth tobacco use through the GYTS, and the survey uses a protocol with sampling methodology and standardized questionnaire, estimates between countries should be compared with caution.
Although several countries collect data on youth tobacco use through the GYTS, and the survey uses a protocol with sampling methodology and standardized questionnaire, estimates between countries should be compared with caution.
Data source(s)
National health authority
Global Tobacco Surveillance System (GTSS). Available from: https://www.cdc.gov/tobacco/global/gtss/gtssdata/index.html
Youth surveys tobacco use and smoking. Available from:
https://www.who.int/publications/i/item/WHO-HEP-HPR-TFI-2021.11.3
Global Tobacco Surveillance System (GTSS). Available from: https://www.cdc.gov/tobacco/global/gtss/gtssdata/index.html
Youth surveys tobacco use and smoking. Available from:
https://www.who.int/publications/i/item/WHO-HEP-HPR-TFI-2021.11.3
Update periodicity PAHO
At least every 5 years
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from: https://iris.paho.org/handle/10665.2/53918
Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. Global Youth Tobbaco Survey. Disponible en: https://www.who.int/teams
oncommunicable-diseases/surveillance/systems-tools/global-youth-tobacco-survey
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from: https://iris.paho.org/handle/10665.2/53918
Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. Global Youth Tobbaco Survey. Disponible en: https://www.who.int/teams
oncommunicable-diseases/surveillance/systems-tools/global-youth-tobacco-survey
Prevalence of current tobacco use in adults (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
Percentage of people aged 15 and above in a given country, territory, or geographic area, who currently use any tobacco product. Current users are defined as those who used any tobacco product, smoked or smokeless, at least once during the 30 days prior to the survey.
"Smoked tobacco products" include cigarettes, beedis, cigars, pipes, water pipes (hookah, shisha), finely cut smoking tobacco (roll-your-own cigarettes), kreteks, or any other form of smoking tobacco.
"Smokeless tobacco" includes moist tobacco, creamy tobacco, solvents, dry tobacco, gul, loose leaves, red tooth powder, snus, chimo, gutka, khaini, gudakhu, zarda, qiwam, dohra, tuibur, nasway, naas
aswar, shammah, betel quid, toombak, pan (betel quid), iq'mik, mishri, tapkeer, tombol, and any other tobacco product that is inhaled, kept in the mouth, or chewed.
Also included as tobacco products are heated tobacco products (HTPs, known by the brands Iqos, glo, Ploom TECH).
Electronic nicotine delivery or non-nicotine delivery systems known as "e-cigarettes," "e-hookahs," JUUL, and "e-pipes" are not classified as tobacco products because they do not contain this substance.
"Smoked tobacco products" include cigarettes, beedis, cigars, pipes, water pipes (hookah, shisha), finely cut smoking tobacco (roll-your-own cigarettes), kreteks, or any other form of smoking tobacco.
"Smokeless tobacco" includes moist tobacco, creamy tobacco, solvents, dry tobacco, gul, loose leaves, red tooth powder, snus, chimo, gutka, khaini, gudakhu, zarda, qiwam, dohra, tuibur, nasway, naas
aswar, shammah, betel quid, toombak, pan (betel quid), iq'mik, mishri, tapkeer, tombol, and any other tobacco product that is inhaled, kept in the mouth, or chewed.
Also included as tobacco products are heated tobacco products (HTPs, known by the brands Iqos, glo, Ploom TECH).
Electronic nicotine delivery or non-nicotine delivery systems known as "e-cigarettes," "e-hookahs," JUUL, and "e-pipes" are not classified as tobacco products because they do not contain this substance.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/predicted
Purpose
Tobacco use is one of the leading risk factors for acquiring and dying from noncommunicable diseases, therefore, the prevalence of current tobacco use in adults helps identify populations at greater risk. This indicator is part of the Global Monitoring Framework on NCDs. It allows country progress to be monitored on the implementation of strategies and national plans to control noncommunicable diseases. It is also used to monitor the impact of implementing the WHO Framework Convention on Tobacco Control or tobacco control policies in countries not yet party to this convention, and to monitor country progress on achieving the Sustainable Health Agenda for the Americas 2018–2030.
As the calculations adjust for age and other variables that can skew the results, the value of the indicator is used to make comparisons between countries or within the same country over time. The indicator contributes to multidisciplinary research in the field of noncommunicable diseases.
As the calculations adjust for age and other variables that can skew the results, the value of the indicator is used to make comparisons between countries or within the same country over time. The indicator contributes to multidisciplinary research in the field of noncommunicable diseases.
Estimation method
The value of this indicator comes from the estimates made by the World Health Organization (WHO) based on data from national population surveys that include self-reporting on tobacco consumption. The prevalence of current tobacco use for each country, and separately for men and women, is obtained using a statistical model based on a Bayesian negative binomial meta regression.
Adjusting the model also makes it possible to estimate trends and projections of the indicator between 1990 and 2030. Depending on the completeness of survey data from a given country, data from other countries in the same UN subregion may be used to fill in information gaps. The resulting trend lines are used to derive estimates for individual years, so that a number can be reported even if the country did not conduct a survey in that year. Results are weighted by population size and standardized by age based on the WHO standard population.
Further background on the methodology:
Bilano V, Gilmour S, Moffiet T, et al. Global trends and projections for tobacco use, 1990–2025: an analysis of smoking indicators from the WHO Comprehensive Information Systems for Tobacco Control. Lancet. 2015 Mar 14;385(9972):966–76. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)60264-1/fulltext
Adjusting the model also makes it possible to estimate trends and projections of the indicator between 1990 and 2030. Depending on the completeness of survey data from a given country, data from other countries in the same UN subregion may be used to fill in information gaps. The resulting trend lines are used to derive estimates for individual years, so that a number can be reported even if the country did not conduct a survey in that year. Results are weighted by population size and standardized by age based on the WHO standard population.
Further background on the methodology:
Bilano V, Gilmour S, Moffiet T, et al. Global trends and projections for tobacco use, 1990–2025: an analysis of smoking indicators from the WHO Comprehensive Information Systems for Tobacco Control. Lancet. 2015 Mar 14;385(9972):966–76. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)60264-1/fulltext
Interpretation - example
According to 2019 data, in country A, the prevalence of current tobacco use in adults is 19.3%. This means that, 18 out of every 100 people aged 15 and over who live in this country currently consume a smoked or smokeless tobacco product.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on data quality and frequency of updated population surveys. It should also be kept in mind that self-reported tobacco consumption is affected by different biases, such as memory bias.
Because advanced statistical methods are applied to estimate current tobacco use in adults in order to make results comparable between countries and over time, the value of this indicator may differ from the results obtained by each country. Another factor impacting results is the standard population used for age adjustment. It is also worth noting that estimates for countries with irregular surveys or with many data gaps will have large ranges of uncertainty, so the results should be interpreted with caution.
The current prevalence of tobacco use in adults is an age-standardized rate whose objective is to compare the indicator between different countries. However, it is a hypothetical value that could differ considerably from the crude prevalence and not fully reflect the real magnitude of the problem in a given country, especially if the country's age structure differs greatly from the standard WHO population structure.
Because advanced statistical methods are applied to estimate current tobacco use in adults in order to make results comparable between countries and over time, the value of this indicator may differ from the results obtained by each country. Another factor impacting results is the standard population used for age adjustment. It is also worth noting that estimates for countries with irregular surveys or with many data gaps will have large ranges of uncertainty, so the results should be interpreted with caution.
The current prevalence of tobacco use in adults is an age-standardized rate whose objective is to compare the indicator between different countries. However, it is a hypothetical value that could differ considerably from the crude prevalence and not fully reflect the real magnitude of the problem in a given country, especially if the country's age structure differs greatly from the standard WHO population structure.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every two years
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.a.1 Age-standardized prevalence of current tobacco use among persons aged 15 years and older.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 13.a Age-standardized prevalence of current tobacco use among persons aged 15 years and older
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.a.1 Age-standardized prevalence of current tobacco use among persons aged 15 years and older.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 13.a Age-standardized prevalence of current tobacco use among persons aged 15 years and older
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
WHO global report on trends in prevalence of tobacco use 2000–2025, third edition. Geneva: World Health Organization; 2019. Available from: https://www.who.int/publications/i/item/who-global-report-on-trends-in-prevalence-of-tobacco-use-2000-2025-third-edition
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C. 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
WHO global report on trends in prevalence of tobacco use 2000–2025, third edition. Geneva: World Health Organization; 2019. Available from: https://www.who.int/publications/i/item/who-global-report-on-trends-in-prevalence-of-tobacco-use-2000-2025-third-edition
Prevalence of insufficient physical activity in adolescents (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
The percentage of adolescents in a given country, territory, or geographic area who engage in less than 60 minutes per day of moderate to vigorous physical activity, during a specific period.
Adolescents are defined as people between the ages of 11 and 17, or according to the definition of the country.
Adolescents are defined as people between the ages of 11 and 17, or according to the definition of the country.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Insufficient physical activity is one of the main risk factors associated with the development of cardiovascular diseases, diabetes mellitus 2, and cancer. It also increases the risk of death from noncommunicable diseases. In adolescents it is associated with their cognitive ability and mental health. This indicator makes it possible to identify populations for targeted strategies to promote healthy lifestyles and prevent noncommunicable diseases.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to make comparisons between countries or within the same country over time.
The indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It can also be used in conjunction with the Monitoring Framework for Universal Health in the Americas and the Sustainable Health Agenda for the Americas 2018–2030. It is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to make comparisons between countries or within the same country over time.
The indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It can also be used in conjunction with the Monitoring Framework for Universal Health in the Americas and the Sustainable Health Agenda for the Americas 2018–2030. It is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO) based on data from national or subnational population surveys. Among the main sources of information are the Global School-based Student Health Survey (GSHS).
Estimates use advanced statistical methods to standardize the definition of physical activity (if different than the definition used in the indicator) and to adjust for age, over-reporting of physical activity on the IPAQ, and survey coverage (if applied only in urban areas). The estimates are weighted according to the population size of each country.
Estimates use advanced statistical methods to standardize the definition of physical activity (if different than the definition used in the indicator) and to adjust for age, over-reporting of physical activity on the IPAQ, and survey coverage (if applied only in urban areas). The estimates are weighted according to the population size of each country.
Interpretation - example
According to 2019 data, the prevalence of insufficient activity in adolescents in country A was 34.2%; i.e., in this country, 34 out of every 100 adolescents aged 11 to 17 years do less than 60 minutes a day of moderate to vigorous physical activity.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data. One factor impacting the result is that the evaluation of physical activity is based on standardized questionnaires that rely on self-reporting of physical activity, with no objective measurement.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate insufficient physical activity prevalence in adults. Therefore, the value of this indicator may differ from the results obtained by each country.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate insufficient physical activity prevalence in adults. Therefore, the value of this indicator may differ from the results obtained by each country.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
World Health Organization (WHO). Global school-based student health survey (GSHS). Available from: https://www.who.int
cds/surveillance/gshs/en/
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
World Health Organization (WHO). Global school-based student health survey (GSHS). Available from: https://www.who.int
cds/surveillance/gshs/en/
Prevalence of insufficient physical activity in adults (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
The percentage of adults aged 18 years or older who engage in insufficient physical activity, in a given country, territory or geographical area during a specific period.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Predicted
Purpose
Insufficient physical activity is one of the main risk factors associated with cardiovascular diseases, type 2 diabetes mellitus, and cancer, while also contributing to increased mortality from noncommunicable diseases. This indicator makes it possible to identify populations for targeted strategies to promote healthy lifestyles and prevent noncommunicable diseases.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to make comparisons between countries or within the same country over time.
Prevalence of insufficient physical activity is included in the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. The indicator is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases. The metrics allows also the monitoring of the Sustainable Health Agenda for the Americas 2018–2030 and the Monitoring Framework for Universal Health in the Americas.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to make comparisons between countries or within the same country over time.
Prevalence of insufficient physical activity is included in the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. The indicator is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases. The metrics allows also the monitoring of the Sustainable Health Agenda for the Americas 2018–2030 and the Monitoring Framework for Universal Health in the Americas.
Estimation method
Insufficient physical activity is defined as less than 150 minutes of moderate physical activity per week or less than 75 minutes of vigorous physical activity per week or a combination of moderate and vigorous physical activity equivalent to less than 600 MET minutes per week.
MET is the metabolic equivalent of task. It is the relationship between a person's working and resting metabolic rates. MET is defined as the energy expended while sitting quietly and is equivalent to a caloric intake of 1 Kcal/kg/hour. Physical activities are often classified by their intensity, using MET as a reference.
The value of this indicator comes from estimates made by the World Health Organization (WHO) based on data from national, subnational, or community surveys that include self-reporting of physical activity, including the Global Physical Activity Questionnaire (GPAQ), the International Physical Activity Questionnaire (IPAQ), the STEPS Survey or similar questionnaires covering physical activity performed at work, at home, for transportation, and during leisure time.
The estimation method uses advanced statistical methods to standardize the definition of physical activity (if different from the definition used in the indicator) and to adjust for age, over-reporting of physical activity on the IPAQ, and survey coverage. The estimates are weighted according to the population of each country. The WHO Global Standard Population is used for age adjustment.
MET is the metabolic equivalent of task. It is the relationship between a person's working and resting metabolic rates. MET is defined as the energy expended while sitting quietly and is equivalent to a caloric intake of 1 Kcal/kg/hour. Physical activities are often classified by their intensity, using MET as a reference.
The value of this indicator comes from estimates made by the World Health Organization (WHO) based on data from national, subnational, or community surveys that include self-reporting of physical activity, including the Global Physical Activity Questionnaire (GPAQ), the International Physical Activity Questionnaire (IPAQ), the STEPS Survey or similar questionnaires covering physical activity performed at work, at home, for transportation, and during leisure time.
The estimation method uses advanced statistical methods to standardize the definition of physical activity (if different from the definition used in the indicator) and to adjust for age, over-reporting of physical activity on the IPAQ, and survey coverage. The estimates are weighted according to the population of each country. The WHO Global Standard Population is used for age adjustment.
Interpretation - example
According to 2019 data, in country A the prevalence of insufficient physical activity in adults was 37.7%. This means that out of every 100 people aged 18 and above living in this country, 38 got less than 150 minutes of moderate physical activity or 75 minutes of vigorous physical activity per week.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data. One factor impacting the result is that the evaluation of physical activity is based on standardized questionnaires that rely on self-reporting of physical activity, with no objective measurement.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate insufficient physical activity prevalence in adults. Therefore, the value of this indicator may differ from the results obtained by each country. Another element impacting results is the standard population used for age adjustment.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate insufficient physical activity prevalence in adults. Therefore, the value of this indicator may differ from the results obtained by each country. Another element impacting results is the standard population used for age adjustment.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 13.e Age-standardized prevalence of insufficiently physically active persons aged 18+ years
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 13.e Age-standardized prevalence of insufficiently physically active persons aged 18+ years
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Prevalence of obesity in children/adolescents aged 10-19 years (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
The percentage of children and adolescents in a given country, territory, or geographical area who are obese (this condition is defined as a body mass index greater than two standard deviations, for age and sex), according to the growth patterns established by World Health Organization (WHO) for school-age children and adolescents. Adolescents are defined as people between the ages of 10 and 19, or according to the definition of the country.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Obesity in childhood and adolescence are associated with an increased likelihood of early development of noncommunicable diseases, such as cardiovascular disease and type 2 diabetes mellitus. They also affect mental health, quality of life, and academic performance. The indicator is applied when designing and evaluating specific nutritional programs for these age groups, as well as programs promoting adequate physical activity and prevention of noncommunicable diseases.
Because estimates of this indicator apply advanced statistical methods to control for some variables that could skew the results, its values are comparable between countries and within the same country over time. The indicator is used to analyze temporal and geographical trends of childhood and adolescent obesity, identifying populations in need of improved access to health care and focused interventions to prevent and treat obesity, and prevention and early detection of complications (insulin resistance, dyslipidemia, arterial hypertension, among others).
It is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It can also be used in conjunction with the Monitoring Framework for Universal Health in the Americas and the Sustainable Health Agenda for the Americas 2018–2030. It is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases.
Because estimates of this indicator apply advanced statistical methods to control for some variables that could skew the results, its values are comparable between countries and within the same country over time. The indicator is used to analyze temporal and geographical trends of childhood and adolescent obesity, identifying populations in need of improved access to health care and focused interventions to prevent and treat obesity, and prevention and early detection of complications (insulin resistance, dyslipidemia, arterial hypertension, among others).
It is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It can also be used in conjunction with the Monitoring Framework for Universal Health in the Americas and the Sustainable Health Agenda for the Americas 2018–2030. It is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO) in conjunction with NCD Risk Factor Collaboration (NCD-RisC). These are based on data from national, subnational, or community population surveys that include weight and height measurements among the population aged 5 to 19 years.
Estimates of the indicator apply advanced statistical models to adjust for different variables that could skew the results, such as survey coverage and year of distribution. The models are also used to estimate the trend of this indicator in different countries and years.
Further background on the methodology:
NCD-RisC. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet, December 2017, vol 390. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32129-3/fulltext
Further background on child growth patterns:
de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bulletin World Health Organization. 2007; 85: 660–66. Available from: https://apps.who.int/iris/handle/10665/270023
Estimates of the indicator apply advanced statistical models to adjust for different variables that could skew the results, such as survey coverage and year of distribution. The models are also used to estimate the trend of this indicator in different countries and years.
Further background on the methodology:
NCD-RisC. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet, December 2017, vol 390. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32129-3/fulltext
Further background on child growth patterns:
de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bulletin World Health Organization. 2007; 85: 660–66. Available from: https://apps.who.int/iris/handle/10665/270023
Interpretation - example
According to 2019 data, the prevalence of obesity in children and adolescents in country A was 25%; i.e., in this country, 1 in 4 children and adolescents is obese.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate prevalence of obesity. Therefore, the value of this indicator may differ from the results obtained by each country. Another element impacting results is the standard population used for age adjustment.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate prevalence of obesity. Therefore, the value of this indicator may differ from the results obtained by each country. Another element impacting results is the standard population used for age adjustment.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14.d Prevalence of childhood and adolescent obesity (5-19 years of age)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 14.d Prevalence of childhood and adolescent obesity (5-19 years of age)
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Global Monitoring Framework: Indicator Definitions and Specifications. Available from: https://cdn.who.int/media/docs/default-source/inaugural-who-partners-forum/gmf_indicator_definitions_version_nov2014438a791b-16d3-46f3-a398-88dee12e796b.pdf?sfvrsn=4b337764_1&download=true
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Global Monitoring Framework: Indicator Definitions and Specifications. Available from: https://cdn.who.int/media/docs/default-source/inaugural-who-partners-forum/gmf_indicator_definitions_version_nov2014438a791b-16d3-46f3-a398-88dee12e796b.pdf?sfvrsn=4b337764_1&download=true
Prevalence of overweight and obesity in adults (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
The percentage of adults aged 18 years or older with a body mass index (BMI) of 25 kg/m2 or higher, in a given country, territory, or geographical area. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Overweight and obesity are risk factors for the development of noncommunicable diseases, such as cardiovascular disease, type 2 diabetes mellitus, and cancer. They also affect cognitive ability, quality of life, and mental health. The indicator is applied when designing and evaluating public policies to control noncommunicable diseases, allocating resources, and implementing surveillance systems.
Because estimates of this indicator apply advanced statistical methods to control for some variables that could skew the results, its values are comparable between countries and within the same country over time. The indicator is used to analyze temporal and geographical trends of childhood and adolescent obesity, because it allows to identify populations in need of improved access to health care and focused interventions to prevent and treat obesity and overweight, and prevention and early detection of complications (insulin resistance, dyslipidemia, arterial hypertension, among others).
The indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It can also be used in conjunction with the Monitoring Framework for Universal Health in the Americas, to measure national progress in implementing policies aimed at strengthening health systems and achieving universal health.
Because estimates of this indicator apply advanced statistical methods to control for some variables that could skew the results, its values are comparable between countries and within the same country over time. The indicator is used to analyze temporal and geographical trends of childhood and adolescent obesity, because it allows to identify populations in need of improved access to health care and focused interventions to prevent and treat obesity and overweight, and prevention and early detection of complications (insulin resistance, dyslipidemia, arterial hypertension, among others).
The indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It can also be used in conjunction with the Monitoring Framework for Universal Health in the Americas, to measure national progress in implementing policies aimed at strengthening health systems and achieving universal health.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO) in conjunction with NCD Risk Factor Collaboration (NCD-RisC). These are based on data from national, subnational, or community population surveys that include weight and height measurements among the population 18 and above.
Estimates of the indicator apply advanced statistical models to adjust for different variables that could skew the results, such as survey coverage and the year of distribution. Models are also used to estimate the trend of this indicator in different countries and years. The WHO Global Standard Population is used for age adjustment.
Further background on the methodology:
NCD-RisC. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet, December 2017, vol 390. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32129-3/fulltext
Estimates of the indicator apply advanced statistical models to adjust for different variables that could skew the results, such as survey coverage and the year of distribution. Models are also used to estimate the trend of this indicator in different countries and years. The WHO Global Standard Population is used for age adjustment.
Further background on the methodology:
NCD-RisC. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet, December 2017, vol 390. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32129-3/fulltext
Interpretation - example
According to 2019 data, country A has a prevalence of overweight and obesity in adults of 49.9%. This means that, in this country, 50 out of every 100 people aged 18 and over are overweight or obese.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data. Another aspect to consider is that the estimates only consider surveys with an objective measurement of weight and height, excluding those based on self-reporting.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate prevalence of overweight and obesity in adults. Therefore, the value may differ from the results obtained by each country. Another element impacting results is the standard population used for age adjustment.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate prevalence of overweight and obesity in adults. Therefore, the value may differ from the results obtained by each country. Another element impacting results is the standard population used for age adjustment.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14.e Prevalence of overweight and obesity in persons 18+ years of age
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 14.e Prevalence of overweight and obesity in persons 18+ years of age
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Diseases Surveillance, Monitoring and Reporting. NCD Global Monitoring Framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Prevalence of overweight in children under age 5 (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
The percentage of children under 5 years of age who are overweight, defined as a weight for height greater than 2 standard deviations from the mean established by the World Health Organization (WHO) child growth standards.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Predicted
Purpose
Childhood overweight and obesity are forms of malnutrition that tend to persist as the child grows, increasing the risk of prematurely developing noncommunicable diseases such as cardiovascular disease, high blood pressure, or type 2 diabetes mellitus. This is one of the indicators of the World Health Assembly nutrition targets and facilitates monitoring of the implementation and effectiveness of national strategies that promote healthy eating and physical activity. It reflects a population's state of health and living conditions, especially among children.
This indicator is used to analyze geographic and temporal trends of overweight in children from 0 to 5 years old. It is applied to identify at-risk groups in order to focus resources to increase access to health care, implement surveillance systems, and encourage multidisciplinary research.
This indicator is used to analyze geographic and temporal trends of overweight in children from 0 to 5 years old. It is applied to identify at-risk groups in order to focus resources to increase access to health care, implement surveillance systems, and encourage multidisciplinary research.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO), in conjunction with the United Nations Children's Fund (UNICEF), and the World Bank. These estimates are based on data from population surveys using a standardized methodology derived from WHO child growth patterns. UNICEF, WHO, and the World Bank Group are jointly reviewing new data sources to update country-level estimates. The information collected is validated and adjusted for variables that can skew the results, in order to obtain comparable estimates between different countries and years.
Further background on the methodology:
WHO Global Database on Child Growth and Malnutrition. Available from: https://www.who.int/teams
utrition-and-food-safety/databases
utgrowthdb
UNICEF-WHO-The World Bank: Joint child malnutrition estimates. Available from: https://data.unicef.org/resources/jme-report-2021/
De Onis et al. Estimates of global prevalence of childhood underweight in 1990 and 2015. JAMA. 2004;291(21):2600–2606. Available from: https://jamanetwork.com/journals/jama/fullarticle/198842
De Onis, Bloessner M. The World Health Organization Global Database on Child Growth and Malnutrition: methodology and applications. International Journal of Epidemiology 2003; 32: 518–526. Available from: https://academic.oup.com/ije/article/32/4/518/666947
World Health Organization (WHO) child growth standards: Available from: https://www.who.int/toolkits/child-growth-standards
Further background on the methodology:
WHO Global Database on Child Growth and Malnutrition. Available from: https://www.who.int/teams
utrition-and-food-safety/databases
utgrowthdb
UNICEF-WHO-The World Bank: Joint child malnutrition estimates. Available from: https://data.unicef.org/resources/jme-report-2021/
De Onis et al. Estimates of global prevalence of childhood underweight in 1990 and 2015. JAMA. 2004;291(21):2600–2606. Available from: https://jamanetwork.com/journals/jama/fullarticle/198842
De Onis, Bloessner M. The World Health Organization Global Database on Child Growth and Malnutrition: methodology and applications. International Journal of Epidemiology 2003; 32: 518–526. Available from: https://academic.oup.com/ije/article/32/4/518/666947
World Health Organization (WHO) child growth standards: Available from: https://www.who.int/toolkits/child-growth-standards
Interpretation - example
According to 2019 data, the prevalence of overweight in children under five years of age in country A is 10%. This means that, in this country, 10 out of 100 children aged 0 to 5 are overweight.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data. One factor to consider when interpreting estimates of overweight prevalence in children under 5 years of age is the scarcity of data for high-income countries, which can affect global and even regional outcomes.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate insufficient physical activity prevalence in adults. Therefore, the value of this indicator may differ from the results obtained by each country.
In order to make results comparable between countries and over time, advanced statistical methods are applied to estimate insufficient physical activity prevalence in adults. Therefore, the value of this indicator may differ from the results obtained by each country.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-overweight-(-weight-for-height-2-sd)
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-jme-country-children-aged-5-years-overweight-(-weight-for-height-2-sd)
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from:
https://iris.paho.org/handle/10665.2/53918
Prevalence of raised blood glucose/diabetes in adults (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
Age-standardized prevalence of people aged 18 years or older who have a fasting blood glucose level ≥ 126 mg/dL (7.0 mmol/L) or on medications for raised blood glucose, in a given country, territory, or geographic area in a specific period. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
The prevalence of diabetes mellitus is used to plan, implement, and evaluate intervention strategies designed for prevention, early diagnosis, and treatment. Its value is used to identify needs, allocate economic, human, and technological resources for diabetes mellitus control, and prevent associated complications.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to analyze geographical and temporal trends in diabetes mellitus and to compare situations between countries or within the same country over time.
This indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases, and implementing policies to strengthen health systems and achieve universal health.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to analyze geographical and temporal trends in diabetes mellitus and to compare situations between countries or within the same country over time.
This indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases, and implementing policies to strengthen health systems and achieve universal health.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO) in conjunction with NCD Risk Factor Collaboration (NCD-RisC). These are based on data from national, subnational, or community health surveys that measure biomarkers for diabetes mellitus (fasting plasma glucose, plasma glucose at two hours in an oral glucose tolerance test, glycated hemoglobin). Advanced statistical models are applied when estimating the indicator to standardize both the definition of diabetes mellitus and the values obtained for different biomarkers included in the surveys. These models are also used to adjust for other variables that could skew the results and to estimate the trend of this disease in different countries and years. The WHO Global Standard Population is used for age adjustment.
More background on the methodology used:
NCD-RisC. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. 2016. The Lancet 387 (10027), 1513–1530. Available from: https://doi.org/10.1016/S0140-6736(16)00618-8
More background on the methodology used:
NCD-RisC. Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. 2016. The Lancet 387 (10027), 1513–1530. Available from: https://doi.org/10.1016/S0140-6736(16)00618-8
Interpretation - example
According to 2019 data, in country A the prevalence of diabetes mellitus in adults was 8.9%; i.e., in this country, nine out of every 100 people aged 18 and over have a fasting glycemia ≥ 126 mg/dL (7.0 mmol/L) or are undergoing pharmacological treatment for diabetes mellitus.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data.
As advanced statistical methods are applied to calculate diabetes prevalence in adults, the value of this indicator may differ from the results obtained by each country. Another factor that may lead to differences is that WHO estimates the indicator based on biomarkers for diabetes mellitus and not on self-reporting of the condition. The value obtained will also vary according to the reference population used for age adjustment.
As advanced statistical methods are applied to calculate diabetes prevalence in adults, the value of this indicator may differ from the results obtained by each country. Another factor that may lead to differences is that WHO estimates the indicator based on biomarkers for diabetes mellitus and not on self-reporting of the condition. The value obtained will also vary according to the reference population used for age adjustment.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/raised-fasting-blood-glucose-(-=-7-0-mmol-l-or-on-medication)-(crude-estimate)
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Disease, Surveillance, Monitoring and Reporting: NCD global monitoring framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Disease, Surveillance, Monitoring and Reporting: NCD global monitoring framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Prevalence of raised blood pressure in adults (%)
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
Age-standardized prevalence of people aged 18 and over who have systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mm/Hg, in a given country, territory, or geographical area, in a specific period. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Because raised blood pressure is one of the main risk factors for developing cardiovascular disease and chronic kidney disease, this indicator helps identify populations at higher risk of getting sick or dying from these pathologies.
The indicator reflects the health status of a given population and contributes to planning, implementing, and evaluating strategies to prevent and control raised blood pressure.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to analyze geographical and temporal trends in blood pressure and to compare situations between countries or within the same country over time.
The indicator is used to develop targeted strategies promoting healthy lifestyles and to allocate economic, human, and technological resources to treat raised blood pressure and prevent associated conditions.
It is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas and is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases, and in implementing policies to strengthen health systems and achieve universal health.
The indicator reflects the health status of a given population and contributes to planning, implementing, and evaluating strategies to prevent and control raised blood pressure.
Since the calculation of this indicator is adjusted for age and other variables that can skew the results, its value is used to analyze geographical and temporal trends in blood pressure and to compare situations between countries or within the same country over time.
The indicator is used to develop targeted strategies promoting healthy lifestyles and to allocate economic, human, and technological resources to treat raised blood pressure and prevent associated conditions.
It is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas and is used to monitor the progress made by countries in implementing national strategies and plans to control noncommunicable diseases, and in implementing policies to strengthen health systems and achieve universal health.
Estimation method
The value of this indicator comes from estimates made by the World Health Organization (WHO) in conjunction with NCD Risk Factor Collaboration (NCD-RisC). These are based on data from national, subnational, or community health surveys that include blood pressure measurement.
Advanced statistical models are applied when estimating the prevalence of raised blood pressure to control for different variables that could skew the results and to include covariables that help predict blood pressure, such as educational level, the proportion of the national population living in urban areas, and a summary measure of the availability of different types of food for human consumption. These models are also applied to estimate blood pressure trends in countries. The WHO Global Standard Population is used for age adjustment.
More background on the methodology used:
NCD-RisC. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants.2017. The Lancet 389 (10064), 37–55. Available from: https://doi.org/10.1016/S0140-6736(16)00618-8
Advanced statistical models are applied when estimating the prevalence of raised blood pressure to control for different variables that could skew the results and to include covariables that help predict blood pressure, such as educational level, the proportion of the national population living in urban areas, and a summary measure of the availability of different types of food for human consumption. These models are also applied to estimate blood pressure trends in countries. The WHO Global Standard Population is used for age adjustment.
More background on the methodology used:
NCD-RisC. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants.2017. The Lancet 389 (10064), 37–55. Available from: https://doi.org/10.1016/S0140-6736(16)00618-8
Interpretation - example
According to 2019 data, the prevalence of high blood pressure in adults is 34.2% in country A. This means that, in this country, 34 out of 100 people aged 18 and above have systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mm/Hg.
Desagregation
By sex
Limitations
The accuracy of this indicator depends on the regularity of population health surveys and the quality of the resulting data.
As advanced statistical methods are applied to calculate raised blood pressure prevalence in adults, the value of this indicator may differ from the results obtained by each country. Another factor that may lead to differences is that WHO estimates the indicator based on blood pressure measurements and not on self-reporting of the condition. The value obtained will also vary according to the reference population used for age adjustment.
As advanced statistical methods are applied to calculate raised blood pressure prevalence in adults, the value of this indicator may differ from the results obtained by each country. Another factor that may lead to differences is that WHO estimates the indicator based on blood pressure measurements and not on self-reporting of the condition. The value obtained will also vary according to the reference population used for age adjustment.
Data source(s)
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/data/indicators/indicator-details/GHO/raised-blood-pressure-(sbp-=140-or-dbp-=90)-(age-standardized-estimate)
https://www.who.int/data/gho/data/indicators/indicator-details/GHO/raised-blood-pressure-(sbp-=140-or-dbp-=90)-(age-standardized-estimate)
Update periodicity PAHO
Every 4-5 years
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Disease, Surveillance, Monitoring and Reporting: NCD global monitoring framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). Noncommunicable Disease, Surveillance, Monitoring and Reporting: NCD global monitoring framework. Available from: https://www.who.int/teams
cds/surveillance/monitoring-capacity/gmf
Private health expenditure as % of GDP
Domain
Health system
Subdomain
Health system
Definition
Private health expenditure financed by voluntary sources of funding, including private insurance premiums (prepaid), non-profit institutions, and out-of-pocket expenditure on health goods and services at the time of care (direct payment), for a given year, in a given country, territory, or geographic area. Expressed as a proportion of gross domestic product.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
This indicator provides a measure of the private sector resources that are channeled into health, relative to other uses. It allows analysis of health expenditure levels and trends. It reflects the importance of this sector in a country's economy, allows comparisons with mandated health expenditure (from the government and social security sector), and helps to identify populations in need of strengthened general health financing from the government. The indicator helps to assess a country’s progress towards universal health coverage.
Estimation method
The value of this indicator comes from the calculation based on two indicators: Current Health Expenditure (CHE) as % Gross Domestic Product (GDP) and Domestic Private Health Expenditure (PVT-D) as % Current Health Expenditure (CHE); data reported by countries to the World Health Organization (WHO), collected from health accounts (HA). Because not all countries have or update their HA, WHO may eventually obtain the data through technical contacts in countries or through publicly available documents and reports synced with the HA framework.
Missing values are estimated using various accounting techniques based on the data available for each country. The main international references come from financial statistics from the International Monetary Fund (IMF), health data from the Organisation for Economic Cooperation and Development (OECD), and national accounts statistics from the United Nations. National sources include HA reports, national accounts reports, comprehensive financing studies, Classification of Individual Consumption According to Purpose (COICOP) reports, and institutional reports from private entities that provide or finance health care, in actuarial and financial reports from private companies and health insurance agencies. Additional sources include household and business surveys, and economic censuses. Ad hoc surveys are another possible source of data.
The indicator is known as domestic private health expenditure (PVT-D), expressed as a percentage of GDP.
Data collected by WHO feeds into its Global Health Expenditure Database (GHED), which serves as a global benchmark for information on health expenditure in WHO Member States.
Formula:
(AxB)/ 100
Numerator (A):
Current Health Expenditure (CHE) as % Gross Domestic Product (GDP)
Denominator (B):
Domestic Private Health Expenditure (PVT-D) as % Current Health Expenditure (CHE)
For more details on methodology, see:
World Health Organization (WHO) Methodology for the update of the Global Health Expenditure Database, 2000–2018. Technical note. Version December 2020. Available from: https://apps.who.int
ha/database/DocumentationCentre/Index/en
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
Note: OECD member States: Mexico, Chile, Colombia, Costa Rica, and Argentina; Associate member: Brazil.
Missing values are estimated using various accounting techniques based on the data available for each country. The main international references come from financial statistics from the International Monetary Fund (IMF), health data from the Organisation for Economic Cooperation and Development (OECD), and national accounts statistics from the United Nations. National sources include HA reports, national accounts reports, comprehensive financing studies, Classification of Individual Consumption According to Purpose (COICOP) reports, and institutional reports from private entities that provide or finance health care, in actuarial and financial reports from private companies and health insurance agencies. Additional sources include household and business surveys, and economic censuses. Ad hoc surveys are another possible source of data.
The indicator is known as domestic private health expenditure (PVT-D), expressed as a percentage of GDP.
Data collected by WHO feeds into its Global Health Expenditure Database (GHED), which serves as a global benchmark for information on health expenditure in WHO Member States.
Formula:
(AxB)/ 100
Numerator (A):
Current Health Expenditure (CHE) as % Gross Domestic Product (GDP)
Denominator (B):
Domestic Private Health Expenditure (PVT-D) as % Current Health Expenditure (CHE)
For more details on methodology, see:
World Health Organization (WHO) Methodology for the update of the Global Health Expenditure Database, 2000–2018. Technical note. Version December 2020. Available from: https://apps.who.int
ha/database/DocumentationCentre/Index/en
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
Note: OECD member States: Mexico, Chile, Colombia, Costa Rica, and Argentina; Associate member: Brazil.
Interpretation - example
According to 2019 data, private health expenditure as a percentage of the GDP of country A was 8.1%; that is, during that year private health expenditure was equivalent to 8.1% of the gross domestic product (GDP) of country A.
Desagregation
No disaggregation
Limitations
In most health accounts, there are significant problems measurement problems when estimating this indicator, mainly due to measurement methods used by countries. This limits the comparability of results at the international level, particularly household surveys used by PAHO to calculate direct or out-of-pocket expenditure. Among the challenges in calculating private health expenditure are issues with data, including frequency of update, coverage, and level of detail.
Due to the methodology used, the value of the indicator estimated by WHO may differ from the results obtained by each country.
Due to the methodology used, the value of the indicator estimated by WHO may differ from the results obtained by each country.
Data source(s)
World Health Organization. Global Health Expenditure database. Available from: https://apps.who.int
ha/database/Home/Index/en
ha/database/Home/Index/en
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or income
Available from: https://unstats.un.org/sdgs/metadata/?Text=&Goal=3&Target=3.8
Sustainable Development Goals (SDG).
Indicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or income
Available from: https://unstats.un.org/sdgs/metadata/?Text=&Goal=3&Target=3.8
References
World Health Organization (WHO). Global Health Expenditure Database. Available from: https://apps.who.int
ha/database/Home/Index/en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Organisation for Economic Cooperation and Development (OECD).
Health Expenditure. Available from: https://www.oecd.org/els/health-systems/health-expenditure.htm#:~:text=Latest%20OECD%20estimates%20point%20to,previous%20years%20at%20around%208.8%25.
ha/database/Home/Index/en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Organisation for Economic Cooperation and Development (OECD).
Health Expenditure. Available from: https://www.oecd.org/els/health-systems/health-expenditure.htm#:~:text=Latest%20OECD%20estimates%20point%20to,previous%20years%20at%20around%208.8%25.
Proportion of population with HIV who receive ART
Domain
Health service coverage
Subdomain
Communicable diseases
Definition
The proportion of adults and children living with HIV who are currently receiving antiretroviral therapy (ART) at the end of the reporting period, in a given country, territory, or geographical area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude/corrected
Purpose
Antiretroviral therapy (ART) combines least three antiretroviral drugs to maximize HIV suppression and slow its progression. Early ART greatly reduces morbidity and mortality among people living with HIV and prevents mother-to-child transmission of the disease during pregnancy, childbirth, and lactation. Those receiving ART and who have suppressed the virus will not transmit it to their sexual partners.
This indicator assesses progress in providing ART to all people with HIV.
The proportion of people receiving antiretroviral therapy among those living with HIV in the same age group serves as a benchmark for monitoring global goals over time and comparing progress across countries. It helps analyze progress towards ART coverage, visualize trends, and identify at-risk populations in need of strengthened access to ART. The indicator is applied when determining the economic, human, and technological resources necessary to address HIV.
This indicator assesses progress in providing ART to all people with HIV.
The proportion of people receiving antiretroviral therapy among those living with HIV in the same age group serves as a benchmark for monitoring global goals over time and comparing progress across countries. It helps analyze progress towards ART coverage, visualize trends, and identify at-risk populations in need of strengthened access to ART. The indicator is applied when determining the economic, human, and technological resources necessary to address HIV.
Estimation method
The numerator comes from data reported by ministries of health to the Pan American Health Organization (PAHO). The denominator, people living with HIV, is derived from UNAIDS/WHO estimates, using Spectrum software.
Formula:
(A/B) x 100
Numerator (A):
People receiving antiretroviral therapy in a given country, territory or geographic area.
Denominator (B):
People living with HIV in a given country, territory or geographic area.
Formula:
(A/B) x 100
Numerator (A):
People receiving antiretroviral therapy in a given country, territory or geographic area.
Denominator (B):
People living with HIV in a given country, territory or geographic area.
Interpretation - example
According to 2019 data, the proportion of people receiving ART in country A was 75%. In other words, of every 100 people living with HIV in that country, 75 received ART that year.
Desagregation
By sex
Limitations
The accuracy of this indicator is impacted by the effectiveness and coverage of HIV technical programs and national surveillance systems, in addition to the quality of the data from these sources. Its value can be underestimated, for example, due to lack of information or reporting delays in national health centers. It can be overestimated if people are not removed from the records who have suspended their treatment, passed away, or discontinued follow-up, among other factors.
This metric only monitors the number of people receiving ART. It does not consider treatment cost, quality, efficacy, or adherence, all of which vary within and between countries.
This metric only monitors the number of people receiving ART. It does not consider treatment cost, quality, efficacy, or adherence, all of which vary within and between countries.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 4.b Antiretroviral treatment (ART) coverage among persons living with HIV
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 4.b Antiretroviral treatment (ART) coverage among persons living with HIV
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
UNAIDS. 90–90-90: Treatment for all. Available from: https://www.unaids.org/en/resources/909090
UNAIDS. Global AIDS monitoring 2021. Indicators and questions for monitoring progress on the 2021 United Nations Political Declaration on HIV and AIDS. Available from: https://www.unaids.org/sites/default/files/media_asset/global-aids-monitoring_en.pdf
Quick Start Guide for Spectrum. Available from: https://www.unaids.org/sites/default/files/media_asset/QuickStartGuide_Spectrum_en.pdf
World Health Organization (WHO). The Global Health Observatory (GHO). Available from:
https://www.who.int/data/gho/indicator-metadata-registry
UNAIDS. 90–90-90: Treatment for all. Available from: https://www.unaids.org/en/resources/909090
UNAIDS. Global AIDS monitoring 2021. Indicators and questions for monitoring progress on the 2021 United Nations Political Declaration on HIV and AIDS. Available from: https://www.unaids.org/sites/default/files/media_asset/global-aids-monitoring_en.pdf
Quick Start Guide for Spectrum. Available from: https://www.unaids.org/sites/default/files/media_asset/QuickStartGuide_Spectrum_en.pdf
Proportion of voluntary non remunerated blood donation (%)
Domain
Health service coverage
Subdomain
Health service
Definition
Number of voluntary unpaid blood donations, expressed as a percentage of total blood donations as of 31 December in the indicated year, in a given country, territory or geographic area.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Safe blood, blood products, and transfusion are fundamental aspects of health care. However, access to safe blood is limited, especially in developing countries. Having voluntary, unpaid blood donors who donate regularly contributes to reducing this gap.
This indicator reflects awareness among the population about voluntary and unpaid blood donation, as well as the level of community solidarity and social cohesion. It also indicates the availability of well-organized and coordinated blood collection and transfusion services. The indicator is applied when developing and prioritizing health policies that favor increased blood collection through voluntary unpaid blood donation.
This indicator reflects awareness among the population about voluntary and unpaid blood donation, as well as the level of community solidarity and social cohesion. It also indicates the availability of well-organized and coordinated blood collection and transfusion services. The indicator is applied when developing and prioritizing health policies that favor increased blood collection through voluntary unpaid blood donation.
Estimation method
This indicator is calculated based on data collected by countries from blood bank records and public and private health institutions and reported to the Pan American Health Organization (PAHO).
Interpretation - example
In 2019, the proportion of voluntary unpaid blood donation in country A was 35%, that is, out of every 100 blood donations registered in this country, 35 came from voluntary unpaid donors.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage, recency, and quality of the data used for calculation. In general, the public health sector tends to keep more complete records, which can lead to an underestimation of the proportion of voluntary unpaid blood donations in the private health sector.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO) / Pan American Health Organization (PAHO). CD58/INF/8 - Plan of Action for Universal Access to Safe Blood: Final Report. September 2020. Available from: https://www.paho.org/en/documents/cd58inf8-plan-action-universal-access-safe-blood-final-report
World Health Organization (WHO) / Pan American Health Organization (PAHO). CD58/INF/8 - Plan of Action for Universal Access to Safe Blood: Final Report. September 2020. Available from: https://www.paho.org/en/documents/cd58inf8-plan-action-universal-access-safe-blood-final-report
Prostate cancer mortality rate (age-adjusted per 100 000 pop); male
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from prostate cancer, in the population of men in a given country, territory or geographic area, during a specific year, divided by total men in that population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps identify populations at higher risk of dying from prostate cancer.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, diagnosis, treatment, and control of prostate cancer and the distribution of economic, human, and technological resources for this disease, among others.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from prostate cancer, in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, diagnosis, treatment, and control of prostate cancer and the distribution of economic, human, and technological resources for this disease, among others.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from prostate cancer, in the same population or across populations.
Estimation method
The numerator of this indicator uses prostate cancer deaths from the World Health Organization (WHO) Global Health Estimates (GHE), and the civil registry and national vital statistics systems of the countries of the Region of the Americas.
The underlying cause of death corresponds to code C61 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the prostate cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The prostate cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The underlying cause of death corresponds to code C61 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the prostate cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The prostate cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The age-adjusted prostate cancer mortality rate for 2019 was 19 per 100 000 population in country A and 7 per 100 000 population in country B; that is, in that year 19 men died from prostate cancer, per 100 000 men in country A, compared to country B, where 7 men died per 100 000. This means that, after removing the effect of differences in the age structure in the two countries, the risk of dying from prostate cancer in 2019 was higher in men in country A.
Desagregation
No disaggregation
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted prostate cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the prostate cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted prostate cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the prostate cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Public health expenditure as % of GDP
Domain
Health system
Subdomain
Health system
Definition
Public health expenditure is the sum of health expenditure paid in cash or in kind by government entities, such as the Ministry of Health, other ministries, parastatal organizations, or social security institutions (without double counting of government transfers to social security and extra budgetary funds), for a given year, in a given country, territory, or geographic area. Expressed as a percentage of the gross domestic product. It includes all expenditures incurred by these entities with domestic resources, recorded in categories FS1 and FS3 (domestic transfers and social security contributions) of System of Health Accounts (SHA) 2011 and excludes financing from external sources (FS2).
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
Public health expenditure provides a measure of the public sector resources that are channeled into health, allowing analysis of geographical and temporal levels and trends.
This indicator reflects the importance of the health sector in a country’s economy and helps to identify inequalities and populations in need of strengthened health financing. It facilitates monitoring of country progress toward achieving universal coverage and, in combination with private expenditure, gives an idea of the level of expenditure and adequacy of public financing.
This indicator reflects the importance of the health sector in a country’s economy and helps to identify inequalities and populations in need of strengthened health financing. It facilitates monitoring of country progress toward achieving universal coverage and, in combination with private expenditure, gives an idea of the level of expenditure and adequacy of public financing.
Estimation method
The value of this indicator comes from data reported by countries to the World Health Organization (WHO), collected from health accounts (HA). Because not all countries have or update their HA, WHO may eventually obtain the data through technical contacts in countries or through publicly available documents and reports synced with the HA framework. Missing values are estimated using various accounting techniques based on the data available for each country. The main international references come from financial statistics from the International Monetary Fund (IMF), health data from the Organisation for Economic Cooperation and Development (OECD), and national accounts statistics from the United Nations. National sources include HA reports, national accounts reports, and comprehensive financing studies. Other possible sources of data are ad hoc surveys.
The indicator is known as domestic general government health expenditure (GGHE-D), expressed as a percentage of GDP.
Data collected by WHO feed into its Global Health Expenditure Database (GHED), which serves as a global benchmark for information on health expenditures in WHO Member States.
For more details on methodology, see:
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
World Health Organization (WHO) Methodology for the update of the Global Health Expenditure Database, 2000–2018. Technical note. Version December 2020. Available from: https://apps.who.int
ha/database/DocumentationCentre/Index/en
The indicator is known as domestic general government health expenditure (GGHE-D), expressed as a percentage of GDP.
Data collected by WHO feed into its Global Health Expenditure Database (GHED), which serves as a global benchmark for information on health expenditures in WHO Member States.
For more details on methodology, see:
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
World Health Organization (WHO) Methodology for the update of the Global Health Expenditure Database, 2000–2018. Technical note. Version December 2020. Available from: https://apps.who.int
ha/database/DocumentationCentre/Index/en
Interpretation - example
Public health expenditure as a % of the GDP of country A during 2019 was 5.4%. This means that health expenditure financed by public sources such as taxes and social security contributions were equivalent to 5.4% of the gross domestic product of country A.
Desagregation
No disaggregation
Limitations
There may be measurement issues when estimating this indicator, mainly due to the availability and disaggregation of information from external resources that passes through government. Some information should be excluded from this measurement, limiting the comparability of results at the international level.
Due to the methodology used, the value of the indicator estimated by WHO may differ from the results obtained by each country.
Due to the methodology used, the value of the indicator estimated by WHO may differ from the results obtained by each country.
Data source(s)
World Health Organization. Global Health Expenditure database. Available from: https://apps.who.int
ha/database/Home/Index/en
ha/database/Home/Index/en
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). Global Health Expenditure Database. Available from: https://apps.who.int
ha/database/Home/Index/en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Organisation for Economic Cooperation and Development. Health Expenditure. Available from: https://www.oecd.org/els/health-systems/health-expenditure.htm#:~:text=Latest%20OECD%20estimates%20point%20to,previous%20years%20at%20around%208.8%25
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
ha/database/Home/Index/en
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Organisation for Economic Cooperation and Development. Health Expenditure. Available from: https://www.oecd.org/els/health-systems/health-expenditure.htm#:~:text=Latest%20OECD%20estimates%20point%20to,previous%20years%20at%20around%208.8%25
OECD/Eurostat/WHO (2017). A System of Health Accounts 2011: Revised edition, OECD Publishing, Paris, System of Health Accounts 2011 (SHA 2011). Available from: https://www.oecd.org/publications/a-system-of-health-accounts-2011-9789264270985-en.htm
Respiratory diseases mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from respiratory diseases in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from respiratory diseases and to assess the presence of risk factors such as those associated with the environment or lifestyle.
Its result is applicable to the design, implementation, and evaluation of health policies related to respiratory diseases and the distribution of economic, human, and technological resources for this group of pathologies, among others. Its applications include, for example, evaluating the impact of strategies to reduce smoking on mortality from respiratory diseases.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from respiratory diseases, in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies related to respiratory diseases and the distribution of economic, human, and technological resources for this group of pathologies, among others. Its applications include, for example, evaluating the impact of strategies to reduce smoking on mortality from respiratory diseases.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from respiratory diseases, in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from respiratory diseases from the World Health Organization (WHO) Global Health Estimates (GHE) and the civil registry and national vital statistics systems of the countries of the Region of the Americas.
The underlying causes of death correspond to codes J30 – J98 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the respiratory diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The respiratory diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The underlying causes of death correspond to codes J30 – J98 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the respiratory diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
The respiratory diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
Interpretation - example
The age-adjusted respiratory diseases mortality rate for 2019 was 28 per 100 000 population in country A and 14 per 100 000 population in country B; that is, in that year respiratory diseases caused the death of 28 people per 100 000 population in country A, compared to country B, where 14 people died from the same group of causes per 100 000 population. This means that, in 2019, the population of country A had a higher risk of dying from respiratory diseases than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted respiratory diseases mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the respiratory diseases mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted respiratory diseases mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the respiratory diseases mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Road injury mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from road injury in the population, in a given country or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution of the population. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
The age-adjusted road injury mortality rate reflects access to and timeliness of health services, along with a population’s health status and socio-economic development.
This indicator makes it possible to identify populations with greater risk factors for this cause of death and to encourage interdisciplinary research in this area, for example, by analyzing the relationship between the road injury mortality rate and a population’s consumption of alcohol and other drugs.
Its result is applicable to the design, implementation, and evaluation of public policies aimed at evaluating the safety of modes of land transportation and promoting road safety, public safety, and effective urban planning.
The age-adjusted road injury mortality rate allows for the comparison of this indicator across populations or in the same population over time.
This indicator makes it possible to identify populations with greater risk factors for this cause of death and to encourage interdisciplinary research in this area, for example, by analyzing the relationship between the road injury mortality rate and a population’s consumption of alcohol and other drugs.
Its result is applicable to the design, implementation, and evaluation of public policies aimed at evaluating the safety of modes of land transportation and promoting road safety, public safety, and effective urban planning.
The age-adjusted road injury mortality rate allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from road injury, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on multiple sources, for example, data from police sources and civil registries provided by countries, standardized surveys sent by WHO to countries, and data from the United Nations and interagency groups, among others.
The underlying causes of death correspond to codes V01 - V04, V06, V09 - V80, V87, V89, and V99 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted road injury mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from:
https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The road injury mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes V01 - V04, V06, V09 - V80, V87, V89, and V99 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted road injury mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from:
https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The road injury mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted road injury mortality rate for 2019 was 13 per 100 000 population in country A and 8 per 100 000 population in country B; that is, in 2019 road injuries were responsible for the death of 13 people per 100 000 population of country A, compared to country B, where eight people died from the same group of causes per 100 000 population. This means that, after removing the effect of differences in the age distribution in the two countries, the risk of dying from road injury in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted road injury mortality rate is a fictitious value, the main purpose of which is to allow for the comparison across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted road injury mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different group of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating this indicator requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality, including the type of accident; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted road injury mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the use of a different group of ICD-10 codes and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating this indicator requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality, including the type of accident; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.6.1 Death rate due to road traffic injuries.
Available from: https://sdgs.un.org/goals
Indicator 3.6.1 Death rate due to road traffic injuries.
Available from: https://sdgs.un.org/goals
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global status report on road safety 2018. Geneva, 2018. Available from:https://www.who.int/publications/i/item/9789241565684
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global status report on road safety 2018. Geneva, 2018. Available from:https://www.who.int/publications/i/item/9789241565684
Stomach cancer incidence rate (age-adjusted per 100 000 pop)
Domain
Morbidity
Subdomain
Cancer
Definition
The ratio of new cases of stomach cancer (ICD-10 codes: C16) arising in a given country, territory, or geographical area during a specific period (usually one year), to the total population of the same year.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Predicted
Purpose
Alongside mortality and prevalence estimates, stomach cancer incidence rates provide a comprehensive assessment of the impact of the disease in 185 countries or territories, including the Region of the Americas.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of the disease in the population.
The age-adjusted stomach cancer incidence rate provides an estimate of the average risk the population has of developing this type of cancer in a particular country, territory, or geographic area. Results of the indicator are used to planning and allocating economic, human, and technological resources to fight stomach cancer.
As the stomach cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. It helps to identify at-risk populations, determine the presence of specific risk factors such as those associated with diet, environment, or lifestyle, and assess access to timely diagnosis and treatment.
This indicator is essential for planning and evaluating stomach cancer screening, early diagnosis, treatment, and control programs. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
The data used to estimate this indicator come from cancer registries that identify new cases occurring in a well-defined population, generating statistics to evaluate and control the impact of the disease in the population.
The age-adjusted stomach cancer incidence rate provides an estimate of the average risk the population has of developing this type of cancer in a particular country, territory, or geographic area. Results of the indicator are used to planning and allocating economic, human, and technological resources to fight stomach cancer.
As the stomach cancer incidence rate is age-adjusted, comparisons can be made both within and between populations over time. It helps to identify at-risk populations, determine the presence of specific risk factors such as those associated with diet, environment, or lifestyle, and assess access to timely diagnosis and treatment.
This indicator is essential for planning and evaluating stomach cancer screening, early diagnosis, treatment, and control programs. It helps to focus efforts such as identifying the need to strengthen and implement cancer registries in a given population.
Estimation method
Data for this indicator come from national or subnational population cancer registries that are routinely maintained in the Global Cancer Observatory (GCO) 2020: Cancer Incidence in Five Continents (CI5) database.
The methods used to estimate the stomach cancer incidence rate in a specific country depend on the data sources available. These include projections on observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The stomach cancer incidence rate is age-standardized, and results are based on rates in populations with a standard age structure.
The age-standardized rate is a summary measure of the rate that would have been observed in a population with a standard age structure. Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
The methods used to estimate the stomach cancer incidence rate in a specific country depend on the data sources available. These include projections on observed incidence rates at the national level, statistical models derived from incidence rates in the country's cancer registries or neighboring country registries, and average incidence rates in neighboring countries, among other metrics.
The stomach cancer incidence rate is age-standardized, and results are based on rates in populations with a standard age structure.
The age-standardized rate is a summary measure of the rate that would have been observed in a population with a standard age structure. Standardization is necessary when comparing several populations that differ in age, as age is a major factor when determining cancer risk. An age-standardized rate is a weighted average of age-specific rates based on a standard population distribution.
Interpretation - example
In 2019, the stomach cancer incidence rate in country A was 64.2 per 100 000 pop. In country B, it was 115.9 per 100 000. In other words, the probability of developing stomach cancer in country A in 2019 was 64.2 per 100 000 pop. The risk of developing this type of cancer in country B is twice as high as in country A.
Desagregation
By sex
Limitations
The data presented at the Global Cancer Observatory are the best available for each country. However, the indicator should be interpreted with caution considering current limitations in the quality and coverage of cancer data, particularly in low- and middle-income countries.
In addition, the age-adjusted stomach cancer incidence rate is not a real value. Its main purpose is to allow comparisons over time between different populations or within the same population. It reflects the average risk of developing this type of cancer in a particular country. This should not be interpreted as the individual risk of developing the cancer.
The estimated value of the age-adjusted stomach cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
In addition, the age-adjusted stomach cancer incidence rate is not a real value. Its main purpose is to allow comparisons over time between different populations or within the same population. It reflects the average risk of developing this type of cancer in a particular country. This should not be interpreted as the individual risk of developing the cancer.
The estimated value of the age-adjusted stomach cancer incidence rate depends on the standard population used for its adjustment; it may therefore differ from the calculations made by each country.
Data source(s)
Global Cancer Observatory (GCO). Cancer Incidence in Five Continents (CI5). Available from: https://ci5.iarc.fr/CI5plus/Default.aspx
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
International Agency for Research on Cancer (IARC), CANCER TODAY. Available from: https://gco.iarc.fr/today/home
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from:
https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Disponible en: https://apps.who.int/iris/handle/10665/259951
Bray F, Colombet M, Mery L, Piñeros M, Znaor A, Zanetti R, Ferlay J, editors (2021). Cancer Incidence in Five Continents, Vol. XI. IARC Scientific Publication No. 166. Lyon: International Agency for Research on Cancer. Available from:
https://publications.iarc.fr/597
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Global Reference List of 100 Core Health Indicators (plus health-related SDGs). Disponible en: https://apps.who.int/iris/handle/10665/259951
Stomach cancer mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from stomach cancer in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from stomach cancer and to evaluate the presence of potential risk factors, such as those associated with diet, environment, or lifestyle.
The age-adjusted stomach cancer mortality rate is applicable to the design, implementation, and evaluation of health policies on stomach cancer and the distribution of economic, human, and technological resources for this disease, among others.
Removing the effect of a different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from stomach cancer, in the same population or across populations.
The age-adjusted stomach cancer mortality rate is applicable to the design, implementation, and evaluation of health policies on stomach cancer and the distribution of economic, human, and technological resources for this disease, among others.
Removing the effect of a different age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from stomach cancer, in the same population or across populations.
Estimation method
The numerator of this indicator uses stomach cancer deaths from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to code C16 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the stomach cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The stomach cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to code C16 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the stomach cancer mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The stomach cancer mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted stomach cancer mortality rate for 2019 is 17 per 100 000 population in country A and 12 per 100 000 population in country B; that is, in that year 17 people died from stomach cancer per 100 000 population in country A, compared to country B, where 12 people died from that cause per 100 000 population. This means that, after removing the effect of differences in the age structure in the two countries, in 2019 the population of country A had a higher risk of dying from stomach cancer than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted stomach cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the stomach cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted stomach cancer mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the stomach cancer mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Stroke diseases mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from stroke diseases in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator helps to identify populations at higher risk of dying from stroke diseases and to assess the presence of comorbidity that increases the risk of dying from this cause or of risk factors such as those associated with lifestyle.
Its result is applicable to the design, implementation, and evaluation of health policies on stroke diseases and the distribution of economic, human, and technological resources for this disease, among others. Its applications include evaluating, over time, the effect of media campaigns on recognizing strokes early and in reducing mortality from this cause.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from stroke diseases, in the same population or across populations.
Its result is applicable to the design, implementation, and evaluation of health policies on stroke diseases and the distribution of economic, human, and technological resources for this disease, among others. Its applications include evaluating, over time, the effect of media campaigns on recognizing strokes early and in reducing mortality from this cause.
Removing the effect of differences in the age distribution by using a standard population makes it possible to analyze the time trend and geographic distribution of deaths from stroke diseases, in the same population or across populations.
Estimation method
The numerator of this indicator uses deaths from stroke diseases, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes I60 – I69 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the stroke diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The stroke diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes I60 – I69 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the stroke diseases mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The stroke diseases mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted stroke diseases mortality rate for 2019 was 30 per 100 000 population in country A and 21 per 100 000 population in country B; that is, in that year stroke diseases caused the death of 30 people per 100 000 population in country A, compared to country B, where 21 people died from that cause per 100 000 population. This means that in 2019 the population of country A had a higher risk of dying from stroke diseases than country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The value of the age-adjusted stroke diseases mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the stroke diseases mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The value of the age-adjusted stroke diseases mortality rate will depend on the standard population used for adjustment.
The estimated value of this indicator may differ from each country’s estimates due to methodological considerations, such as the method used to prepare the population estimates and projections or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the stroke diseases mortality rate requires a civil registry system with good coverage. Deaths must be recorded in a timely manner in this system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Suicide mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths by suicide, also known as self-harm, in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July) after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
This indicator reflects a population’s lifestyles, socio-economic development, and health status. Its analysis makes it possible to identify populations with greater risk factors for suicide and encourage research in this area.
Its result is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and rehabilitation of this group of causes, among others. Its applications include, for example, evaluating over time the effectiveness of mental health interventions, of early diagnostic strategies aimed at identifying suicide risk behaviors, and of rehabilitation from such events.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and rehabilitation of this group of causes, among others. Its applications include, for example, evaluating over time the effectiveness of mental health interventions, of early diagnostic strategies aimed at identifying suicide risk behaviors, and of rehabilitation from such events.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from self-harm, from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes X60 - X84, Y870 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The suicide mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes X60 - X84, Y870 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The suicide mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted suicide mortality rate for 2019 was 23 per 100 000 population in country A and 16 per 100 000 population in country B; that is, in that year suicide was responsible for the death of 23 people per 100 000 population in country A, compared to country B, where 16 people died from the same group of causes per 100 000 population. This means that, after removing the effect of differences in the age distribution in the two countries, the risk of dying by suicide in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted suicide mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the method for making population estimates and projections, the use of a different group of ICD-10 codes, and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the suicide mortality rate requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality and include the method of suicide; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted suicide mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the method for making population estimates and projections, the use of a different group of ICD-10 codes, and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the suicide mortality rate requires a civil registry system with good coverage. Deaths from this group of causes must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality and include the method of suicide; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.4.2 Suicide mortality rate.
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14. Mortality rate due to suicide
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.4.2 Suicide mortality rate.
Available from: https://sdgs.un.org/goals
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 14. Mortality rate due to suicide
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
Tetanus neonatorum cases
Domain
Morbidity
Subdomain
Child health
Definition
Number of confirmed cases of neonatal tetanus in a given country, territory, or geographical area, in a specific calendar year. The neonatal period extends from birth to 28 days of age.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Tetanus is an immunopreventable disease that is acquired when Clostridium tetani bacteria spores infect a wound or umbilical stump. The number of confirmed cases of neonatal tetanus quantifies the impact of a preventable disease on morbidity and mortality in the neonatal period and makes this issue visible as a public health problem. The indicator facilitates monitoring of the program for the elimination of neonatal tetanus.
It supports decision making in public policies aimed at improving maternal and child health. It helps identify areas that require new or improved neonatal tetanus surveillance systems and strengthened tetanus vaccination programs.
This indicator reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and risk factors for Clostridium tetani infection.
The indicator is applied for designing, implementing, and assessing health policies to prevent, treat, and control neonatal tetanus and distribute economic, human, and technological resources to fight the disease, as well as other purposes.
Among its main applications are promoting vaccination, improving childbirth and postpartum hygiene, and training health workers in disease prevention. It helps strengthen access to quality maternal and child health care.
It supports decision making in public policies aimed at improving maternal and child health. It helps identify areas that require new or improved neonatal tetanus surveillance systems and strengthened tetanus vaccination programs.
This indicator reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and risk factors for Clostridium tetani infection.
The indicator is applied for designing, implementing, and assessing health policies to prevent, treat, and control neonatal tetanus and distribute economic, human, and technological resources to fight the disease, as well as other purposes.
Among its main applications are promoting vaccination, improving childbirth and postpartum hygiene, and training health workers in disease prevention. It helps strengthen access to quality maternal and child health care.
Estimation method
The number of confirmed cases of neonatal tetanus (ICD-10 code: A33) in the resident population is obtained from data collected by national disease surveillance and control systems and reported by countries in the Region of the Americas. The neonatal period extends from birth to 28 days of age.
Interpretation - example
In 2019, there were 36 confirmed cases of neonatal tetanus in live births aged 0 to 28 days residing in country A.
Desagregation
No disaggregation
Limitations
Estimates of confirmed cases of neonatal tetanus are affected by the effectiveness of national neonatal tetanus surveillance and control systems and by the quality of the data collected.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.
Available from: https://sdgs.un.org/
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
Total alcohol per capita (age 15+ years) consumption
Domain
Risk factor
Subdomain
Noncommunicable diseases
Definition
Total alcohol per capita consumption (APC) is defined as the total amount of alcohol (sum of the three-year recorded consumption average and the three-year unrecorded consumption average, adjusted for the three-year average of tourist consumption) consumed per person among adults 15 years or older during a calendar year in a given country. Expressed in liters of pure alcohol.
Recorded alcohol consumption (recorded APC) refers to official statistics (production, import, export, and sales or taxes). Unrecorded alcohol consumption refers to untaxable alcohol that does not go through quality control measures. As such, it is generally not regulated or subject to State control (informal production, smuggling, alcohol not intended for human consumption, cross-border purchases). Tourism consumption considers tourists visiting the country and the country’s population travelling abroad.
Recorded alcohol consumption (recorded APC) refers to official statistics (production, import, export, and sales or taxes). Unrecorded alcohol consumption refers to untaxable alcohol that does not go through quality control measures. As such, it is generally not regulated or subject to State control (informal production, smuggling, alcohol not intended for human consumption, cross-border purchases). Tourism consumption considers tourists visiting the country and the country’s population travelling abroad.
Measurement Unit
Liters of pure alcohol per capita
Type of measurement
Average
Type of statistics
Corrected
Purpose
Alcohol can create dependency and is a major risk factor for mental and behavioral diseases and non-communicable diseases, such as cirrhosis of the liver, some types of cancer, and cardiovascular diseases, among others. It also increases the risk of trauma and violence. Beyond health consequences, the harmful use of alcohol has significant socioeconomic impact on individuals and society at large.
APC is a key indicator for estimating the disease burden and deaths attributable to alcohol. This indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It is used to monitor countries and their efforts to implement national alcohol-related strategies and plans. It is also one of the indicators used in SDG 3.5 (3.5.2), to foster prevention and coverage of treatment for disorders due to alcohol consumption.
APC is a key indicator for estimating the disease burden and deaths attributable to alcohol. This indicator is part of the Global Monitoring Framework on NCDs and the Monitoring Framework for Universal Health in the Americas. It is used to monitor countries and their efforts to implement national alcohol-related strategies and plans. It is also one of the indicators used in SDG 3.5 (3.5.2), to foster prevention and coverage of treatment for disorders due to alcohol consumption.
Estimation method
This indicator comes from the WHO Global Information System on Alcohol and Health (GISAH). Recorded APC is calculated from several sources, with preference given to government statistics on sales of alcoholic beverages during a calendar year or data on production, export, and import of alcohol in different beverage categories. When national statistics are non-existent, the data comes from public sources in the private sector. If data are inconsistent, country-specific data from the Food and Agriculture Organization of the United Nations (FAOSTAT; http://www.fao.org/faostat/en/#home) are used.
Unrecorded APC is estimated as a proportion of total consumption. Unrecorded consumption at the national level is estimated using regression analysis. Unrecorded consumption estimates were calculated from four sources: 1) a WHO survey collecting expert opinions on whether any change in unrecorded consumption has occurred since 2010 (since the publication of the WHO Global Status Report on Alcohol and Health 2014), as well as the magnitude and documentation of these changes; 2) a survey conducted by WHO and the PAHO/WHO Collaborating Centre CAMH (Center for Addiction and Mental Health) with selected experts and using the 2013 Delphi methodology to assess the proportion of unrecorded consumption in 46 WHO Member States (response rate: 74%) in which unrecorded consumption was relatively high; (3) a second WHO/CAMH survey with experts from 49 Member States (response rate 86%); and (4) WHO STEPwise approach to surveillance surveys.
Based on these data, the proportion of unrecorded consumption was calculated with a regression analysis. Data for tourism consumption estimates come from the Institute for Health Metrics and Evaluation (IHME). Its calculations are based on data from the United Nations World Tourism Organization (UNWTO) (World Tourism Organization, 2016; www.healthdata.org). The liters of alcohol consumed by tourists in a country are derived from the number of tourists visiting a country, average time spent in the country, and how much the tourists consume, on average, in their own countries of origin. In addition, tourist alcohol consumption includes the people of the country in question who consume alcohol when travelling abroad (based on the average time spent abroad and alcohol consumption in the country of origin). These estimates assume the following: 1) tourists drink the same amount of alcohol as when they are in their own countries, and 2) total global tourist consumption equals 0 (meaning consumption by tourists can be negative or positive) (WHO, 2018).
Formula:
(A/B)
Three-year average from consecutive years.
Numerator (A):
The sum of recorded and unrecorded alcohol consumption in the population of a country during a calendar year.
Denominator (B):
Number of inhabitants of the same country 15 years of age or older. Data obtained from the United Nations Population Division.
Unrecorded APC is estimated as a proportion of total consumption. Unrecorded consumption at the national level is estimated using regression analysis. Unrecorded consumption estimates were calculated from four sources: 1) a WHO survey collecting expert opinions on whether any change in unrecorded consumption has occurred since 2010 (since the publication of the WHO Global Status Report on Alcohol and Health 2014), as well as the magnitude and documentation of these changes; 2) a survey conducted by WHO and the PAHO/WHO Collaborating Centre CAMH (Center for Addiction and Mental Health) with selected experts and using the 2013 Delphi methodology to assess the proportion of unrecorded consumption in 46 WHO Member States (response rate: 74%) in which unrecorded consumption was relatively high; (3) a second WHO/CAMH survey with experts from 49 Member States (response rate 86%); and (4) WHO STEPwise approach to surveillance surveys.
Based on these data, the proportion of unrecorded consumption was calculated with a regression analysis. Data for tourism consumption estimates come from the Institute for Health Metrics and Evaluation (IHME). Its calculations are based on data from the United Nations World Tourism Organization (UNWTO) (World Tourism Organization, 2016; www.healthdata.org). The liters of alcohol consumed by tourists in a country are derived from the number of tourists visiting a country, average time spent in the country, and how much the tourists consume, on average, in their own countries of origin. In addition, tourist alcohol consumption includes the people of the country in question who consume alcohol when travelling abroad (based on the average time spent abroad and alcohol consumption in the country of origin). These estimates assume the following: 1) tourists drink the same amount of alcohol as when they are in their own countries, and 2) total global tourist consumption equals 0 (meaning consumption by tourists can be negative or positive) (WHO, 2018).
Formula:
(A/B)
Three-year average from consecutive years.
Numerator (A):
The sum of recorded and unrecorded alcohol consumption in the population of a country during a calendar year.
Denominator (B):
Number of inhabitants of the same country 15 years of age or older. Data obtained from the United Nations Population Division.
Interpretation - example
According to 2019 data, the total alcohol per capita consumption in adults is 10.9 liters., i.e., in this country, average consumption per adult 15 years and above is 10.9 liters (recorded and unrecorded).
Desagregation
By sex
Limitations
Estimates of alcohol consumption per capita are impacted by different factors, including access to various sources of information, which are not always regularly available in some countries, and incomplete administrative records. Data from the private sector are not independently verifiable through transparent methods. Another challenge is collecting unrecorded alcohol consumption data.
Correct interpretation of this indicator requires the use of additional indicators derived from population surveys, such as the prevalence of alcohol consumption.
Correct interpretation of this indicator requires the use of additional indicators derived from population surveys, such as the prevalence of alcohol consumption.
Data source(s)
World Health Organization (WHO). Global Information System on Alcohol and Health. Available from: https://www.who.int/data/gho/data/themes/global-information-system-on-alcohol-and-health
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG)
Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 13.b Total (recorded and unrecorded) alcohol per capita (APC) consumption among persons 15+ years of age within a calendar year in liters of pure alcohol, adjusted for tourist consumption
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcohol.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 13.b Total (recorded and unrecorded) alcohol per capita (APC) consumption among persons 15+ years of age within a calendar year in liters of pure alcohol, adjusted for tourist consumption
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global status report on alcohol and health 2018. Geneva: World Health Organization; 2018. Available from: https://www.who.int/publications/i/item/9789241565639
Pan American Health Organization (PAHO). Monitoring Framework for Universal Health in the Americas. Washington, D.C., 2021. Available from: https://iris.paho.org/handle/10665.2/53918
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global status report on alcohol and health 2018. Geneva: World Health Organization; 2018. Available from: https://www.who.int/publications/i/item/9789241565639
Total fertility rate (live births per woman)
Domain
Sociodemographic
Subdomain
Demographic
Definition
The expected average number of live births that a woman would have during her lifetime, if during her reproductive years she experienced the age-specific fertility rates prevalent in a given year or period, in a given country, territory, or geographical area.
The overall fertility rate is a synthetic measure that expresses, in a single figure, the fertility of all women during a given stage.
The overall fertility rate is a synthetic measure that expresses, in a single figure, the fertility of all women during a given stage.
Measurement Unit
Live births per woman
Type of measurement
Rate
Type of statistics
Corrected/Predicted
Purpose
Because the overall fertility rate is not affected by the population's age structure, it is possible to analyze the level of fertility and compare trends over time and between different populations and geographical areas.
The indicator is used to produce population estimates and projections.
It contributes to public policies on health, education, work, and social security.
The indicator is used to produce population estimates and projections.
It contributes to public policies on health, education, work, and social security.
Estimation method
The overall fertility rate is calculated directly as the sum of fertility rates for all ages considered (usually referring to women between 15 and 49 years old) and multiplying the result by the size of the interval in which the ages were grouped, usually in five-year periods.
A specific fertility rate by age or age group is calculated as the ratio between the annual number of births to women of a certain age or age group and the population of women of the same age or age group, in the same year, in a given country, territory, or geographical area.
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
A specific fertility rate by age or age group is calculated as the ratio between the annual number of births to women of a certain age or age group and the population of women of the same age or age group, in the same year, in a given country, territory, or geographical area.
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
Interpretation - example
In 2018, country A had an overall fertility rate of 2.4 births per woman, that is, if 2018 fertility rates by age had remained unchanged, women in A who turned 15 in 2018 would have an average of 2.4 live births during their reproductive years.
Desagregation
By women 15-19 years of age
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths and births in the system, and the integrity of the registry.
The calculation of this indicator assumes that fertility rates by age remain constant over time, however, they actually gradually fluctuate year by year, which may reflect changes in the pace of births, rather than changes in the average number of children women have. Other factors influencing these fluctuations are the health of the population, level of urbanization, educational level, availability and use of contraceptive methods, infant mortality, and women's participation in the labor force.
The estimated value of this indicator may differ from country statistics due to factors, such as methodological differences in developing population estimates and projections.
The calculation of this indicator assumes that fertility rates by age remain constant over time, however, they actually gradually fluctuate year by year, which may reflect changes in the pace of births, rather than changes in the average number of children women have. Other factors influencing these fluctuations are the health of the population, level of urbanization, educational level, availability and use of contraceptive methods, infant mortality, and women's participation in the labor force.
The estimated value of this indicator may differ from country statistics due to factors, such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Glossary. Available from: https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Total population (thousands)
Domain
Sociodemographic
Subdomain
Demographic
Definition
The total number of inhabitants who actually live within the borders of the country, territory, or geographical area at a specific point in time, usually at mid-year. The population at mid-year refers to the de facto population on 1 July.
Measurement Unit
Thousands
Type of measurement
Magnitude
Type of statistics
Corrected/Predicted
Purpose
The total number of inhabitants is used to develop population estimates and projections, alongside other population-based demographic indicators such as the overall mortality rate, death rate by cause, birth rate, share of the population under 15 years of age, and share of the population aged 65 and over.
This indicator helps determine the size of the target population receiving government actions and services, and contributes to budgetary allocation and planning, management, and evaluation of public policies related to health, education, work, and social security.
This indicator helps determine the size of the target population receiving government actions and services, and contributes to budgetary allocation and planning, management, and evaluation of public policies related to health, education, work, and social security.
Estimation method
The value of this indicator comes from United Nations estimates, based on data representing estimated mid-year values, obtained by linear interpolation of the corresponding United Nations five-year medium-variant population projections.
The estimated population is calculated using a change model component that incorporates information on the natural increase (births, deaths) and net migration (net national migration, net international migration) in an area since the last decennial census.
The de facto population is taken on 1 July, assuming that the reference period is year z and that births are evenly distributed throughout that period. The average population will be represented by a mid-year estimate on 1 July.
The estimated population is calculated using a change model component that incorporates information on the natural increase (births, deaths) and net migration (net national migration, net international migration) in an area since the last decennial census.
The de facto population is taken on 1 July, assuming that the reference period is year z and that births are evenly distributed throughout that period. The average population will be represented by a mid-year estimate on 1 July.
Interpretation - example
The total population of country A during 2018 was 36 953 800.
Desagregation
By sex: Male, Female
By age: 0–1 year
1-4 years
5-14 years
15-24 years
25-44 years
45-64 years
65-74 years
75+ years
By age: 0–1 year
1-4 years
5-14 years
15-24 years
25-44 years
45-64 years
65-74 years
75+ years
Limitations
The international comparability of this indicator may be affected by the frequency of population censuses and demographic surveys. Population censuses are usually conducted every 10 years and survey frequency varies between different countries, with sampling error also a factor. The value of the indicator also depends on adequate coverage of the civil registry (greater than 90 percent), timely registration of deaths and births in the system, and the integrity of the national registry.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Departament of Economic and Social Affiars, Population Division. World Population Prospects. Available from:
https://population.un.org/wpp/
https://population.un.org/wpp/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from:
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Available from:
https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from:
https://www.paho.org/bra/dmdocuments/indicadores.pdf
https://www.who.int/data/gho/indicator-metadata-registry
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United States Census Bureau. International Database. Available from:
https://www.census.gov/glossary/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Pan American Health Organization. Basic Health Indicators in Brazil: concepts and applications, 2nd edition. Brasilia, 2008. Available from:
https://www.paho.org/bra/dmdocuments/indicadores.pdf
Tuberculosis incidence rate (100 000 pop)
Domain
Morbidity
Subdomain
Communicable diseases
Definition
The number of new and relapse cases of tuberculosis and cases with unknown history of previous treatment, reported in a specific year in the population of a given country, territory, or geographical area. It covers all forms of TB. Expressed per 100 000 population.
Measurement Unit
100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that is transmitted when infected people expel bacteria into the air, for example by coughing, sneezing, or talking. It is one of the leading causes of morbidity and mortality worldwide and the leading cause of death by a single infectious agent (ahead of HIV/AIDS). It usually affects the lungs (pulmonary TB) and can compromise other organs (extrapulmonary TB).
The incidence of TB indicates a population's level of health and development, reflects the intensity of the impact of the disease in any of its clinical forms on the population, helps to identify persistent risk factors that facilitate transmission, and focuses targeted preventive and control strategies. These strategies include strengthening TB vaccination programs and surveillance activities.
This indicator makes it possible to monitor country progress toward achieving the goals of the Global End TB Strategy 2016-2035. Its value is applied when analyzing temporal and geographical TB trends in a population, planning and assessing public policies to fight the disease, and allocating economic, human, and technological resources.
The incidence of TB indicates a population's level of health and development, reflects the intensity of the impact of the disease in any of its clinical forms on the population, helps to identify persistent risk factors that facilitate transmission, and focuses targeted preventive and control strategies. These strategies include strengthening TB vaccination programs and surveillance activities.
This indicator makes it possible to monitor country progress toward achieving the goals of the Global End TB Strategy 2016-2035. Its value is applied when analyzing temporal and geographical TB trends in a population, planning and assessing public policies to fight the disease, and allocating economic, human, and technological resources.
Estimation method
The tuberculosis (TB) incidence rate comes from estimates made by the World Health Organization (WHO) based on a consultative process of analyzing the data officially reported by countries' Ministries of Health. These estimates are derived from annual case reports, assessments of the quality and coverage of TB reporting data, national disease prevalence surveys, and information from death registration systems.
Incidence estimates for each country are calculated using one or more of the following approaches, depending on the available data:
1. Incidence= reported cases / estimated proportion of detected cases
2. Capture-recapture modelling
3. Incidence = prevalence / duration of the illness
The TB incidence rate represents the best estimates that WHO can obtain. All estimates are communicated to countries prior to publication. The process of revising estimates is based on the comments provided by the countries. The final set is reviewed by WHO prior to publication to verify compliance with specific international standards and to harmonize breakdowns by age group and sex.
For more details on methodology, see:
World Health Organization (WHO). Policy and recommendations for how to assess the epidemiological burden of TB and the impact of TB control. Geneva, 2009. Available from: https://apps.who.int/iris/handle/10665/44231
World Health Organization (WHO). Global tuberculosis reports. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports
Incidence estimates for each country are calculated using one or more of the following approaches, depending on the available data:
1. Incidence= reported cases / estimated proportion of detected cases
2. Capture-recapture modelling
3. Incidence = prevalence / duration of the illness
The TB incidence rate represents the best estimates that WHO can obtain. All estimates are communicated to countries prior to publication. The process of revising estimates is based on the comments provided by the countries. The final set is reviewed by WHO prior to publication to verify compliance with specific international standards and to harmonize breakdowns by age group and sex.
For more details on methodology, see:
World Health Organization (WHO). Policy and recommendations for how to assess the epidemiological burden of TB and the impact of TB control. Geneva, 2009. Available from: https://apps.who.int/iris/handle/10665/44231
World Health Organization (WHO). Global tuberculosis reports. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports
Interpretation - example
In 2010, the incidence rate of tuberculosis in country A was 22.9 per 100 000 population; i.e., in that year 23 new or relapse cases of tuberculosis were reported, in any of its clinical forms, per 100 000 population in country A.
Desagregation
No disaggregation
Limitations
The accuracy of this indicator depends on the coverage and performance of tuberculosis surveillance and control systems, which may be affected by insufficient public access to laboratory tests for diagnosis, leading to underreporting of cases.
The value of this indicator may differ from country estimates due to small methodological differences, such as different sources of information used to obtain the total population of each country.
One factor to consider when interpreting this indicator is that people can acquire the infection several years before developing the disease. As a result, the effect of tuberculosis control strategies on incidence is less rapid than the effect on prevalence or mortality. It should be kept in mind that the value of the TB incidence rate may shift due to changes in the rate at which people become infected with M. tuberculosis or the rate at which infected persons develop TB disease.
The value of this indicator may differ from country estimates due to small methodological differences, such as different sources of information used to obtain the total population of each country.
One factor to consider when interpreting this indicator is that people can acquire the infection several years before developing the disease. As a result, the effect of tuberculosis control strategies on incidence is less rapid than the effect on prevalence or mortality. It should be kept in mind that the value of the TB incidence rate may shift due to changes in the rate at which people become infected with M. tuberculosis or the rate at which infected persons develop TB disease.
Data source(s)
World Health Organization (WHO). Global Tuberculosis Programme. Available from: https://www.who.int/teams/global-tuberculosis-programme/data
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.3.2 Tuberculosis incidence per 100 000 population
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 20. Incidence rate of tuberculosis
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.3.2 Tuberculosis incidence per 100 000 population
Available from: https://sdgs.un.org/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 20. Incidence rate of tuberculosis
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). Global Tuberculosis Programme: The End TB Strategy 2016 – 2035. Available from: https://www.who.int/teams/global-tuberculosis-programme/the-end-tb-strategy
Tuberculosis mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from tuberculosis in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The age-adjusted tuberculosis mortality rate reflects a population’s health status and socio-economic development. Its analysis makes it possible to identify populations with greater risk factors for dying from tuberculosis or having a co-infection with other pathologies and to encourage research in this area.
Its result is applicable to the design, implementation, and evaluation of health policies on tuberculosis and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and rehabilitation of this disease, among others. Its applications include, for example, evaluating over time the effectiveness of antimicrobial therapies, early diagnostic strategies, and the evolution of human immunodeficiency virus (HIV) co-infection.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies on tuberculosis and the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and rehabilitation of this disease, among others. Its applications include, for example, evaluating over time the effectiveness of antimicrobial therapies, early diagnostic strategies, and the evolution of human immunodeficiency virus (HIV) co-infection.
Adjusting for age allows for the comparison of this indicator across populations or in the same population over time.
Estimation method
The numerator of this indicator uses tuberculosis deaths from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the Global TB Programme, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes A15 - A19, B90 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted tuberculosis mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The tuberculosis mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes A15 - A19, B90 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted tuberculosis mortality rate are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
The tuberculosis mortality rate is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted tuberculosis mortality rate for 2019 was 33 per 100 000 population in country A and 26 per 100 000 population in country B; that is, in that year tuberculosis was responsible for the death of 33 people per 100 000 population of country A, compared to country B, where 26 people died from this disease per 100 000 population. This means that, after removing the effect of differences in the age distribution in the two countries, the risk of dying from tuberculosis in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted tuberculosis mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the method used for making population estimates and projections, the use of a different group of ICD-10 codes, and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the tuberculosis mortality rate requires a civil registry system with good coverage. Deaths from this disease must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
The estimated value of the age-adjusted tuberculosis mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the method used for making population estimates and projections, the use of a different group of ICD-10 codes, and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the tuberculosis mortality rate requires a civil registry system with good coverage. Deaths from this disease must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global tuberculosis report 2020. Geneva, 2020. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Global tuberculosis report 2020. Geneva, 2020. Available from: https://www.who.int/teams/global-tuberculosis-programme/tb-reports
Under age 5 deaths due to acute respiratory infections (%)
Domain
Mortality
Subdomain
Child health
Definition
Proportion of deaths in children under 5 years of age from acute respiratory infections (ARI), in a given country, territory or geographic area during a specific calendar year, in relation to the total number of deaths in the same population, age group and year. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
This indicator makes it possible to compare the relative weight of deaths from acute respiratory infections with that of deaths in children under 5 from other pathologies.
It reflects the health status and socio-economic development of a population, and makes it possible to identify inequities in health.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of acute respiratory infections in children and the distribution of economic, human, and technological resources for this group of pathologies, among others. These applications include the planning and evaluation of programs on vaccination, nutrition, and prioritization of timely access, as well as coverage and quality of health care for children.
It reflects the health status and socio-economic development of a population, and makes it possible to identify inequities in health.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of acute respiratory infections in children and the distribution of economic, human, and technological resources for this group of pathologies, among others. These applications include the planning and evaluation of programs on vaccination, nutrition, and prioritization of timely access, as well as coverage and quality of health care for children.
Estimation method
To estimate the percentage of deaths from acute respiratory infections in children under 5, the number of deaths from this group of pathologies is used as the numerator and the total number of deaths from all causes in the same population and year as the denominator.
Underlying causes of death correspond to codes J00 – J22 of the International Classification of Diseases, Tenth Revision.
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, estimates of the cause of death are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
Formula
(A/B) x 100
Numerator (A):
Number of deaths in children < 5 from ARI during year z in a given country, territory or geographic area.
Denominator (B):
Total number of deaths in children < 5 during year z in a given country, territory or geographic area.
Underlying causes of death correspond to codes J00 – J22 of the International Classification of Diseases, Tenth Revision.
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, estimates of the cause of death are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
Formula
(A/B) x 100
Numerator (A):
Number of deaths in children < 5 from ARI during year z in a given country, territory or geographic area.
Denominator (B):
Total number of deaths in children < 5 during year z in a given country, territory or geographic area.
Interpretation - example
According to 2019 data, 14% of deaths in children under 5 in country A were due to acute respiratory infections; that is, during that year, for every 100 deaths in children under 5 in country A, 14 were from an acute respiratory infection.
Desagregation
No disaggregation
Limitations
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations such as the use of a different group of ICD-10 codes for the underlying cause of death or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the proportion of deaths from acute respiratory infections in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system and information on causes of death must be recorded; otherwise the estimates will not be sufficiently accurate.
Estimating the proportion of deaths from acute respiratory infections in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system and information on causes of death must be recorded; otherwise the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
Under age 5 deaths due to infectious intestinal disease (%)
Domain
Mortality
Subdomain
Child health
Definition
Proportion of deaths in children under 5 years of age from infectious intestinal disease (acute diarrheal disease, ADD), in a given country, territory or geographic area during a specific calendar year, in relation to total deaths in the same population, age group, and year. Expressed as a percentage.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
This indicator makes it possible to compare the relative weight of deaths from infectious intestinal disease in children under 5 with those deaths from other pathologies.
It reflects the health status and socio-economic development of a population and makes it possible to identify inequities in health.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of infectious intestinal disease in children and the distribution of economic, human, and technological resources for this group of pathologies, among others. These applications include the planning and evaluation of programs on nutrition, access to safe drinking water, environmental sanitation and hygiene, and prioritization of timely access, as well as coverage and quality of health care for children.
It reflects the health status and socio-economic development of a population and makes it possible to identify inequities in health.
Its result is applicable to the design, implementation, and evaluation of health policies for the prevention, treatment, and control of infectious intestinal disease in children and the distribution of economic, human, and technological resources for this group of pathologies, among others. These applications include the planning and evaluation of programs on nutrition, access to safe drinking water, environmental sanitation and hygiene, and prioritization of timely access, as well as coverage and quality of health care for children.
Estimation method
To estimate the percentage of deaths from infectious intestinal disease (ADD) in children under 5, the number of deaths from this group of pathologies is used as the numerator and total deaths from all causes in the same population and year as the denominator.
Underlying causes of death correspond to codes A00, A01, A03, A04, A06-A09 of the International Classification of Diseases, Tenth Revision.
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, estimates of the cause of death are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
Formula
(A/B) x 100
Numerator (A):
Number of deaths in children < 5 from ADD during year z in a given country, territory or geographic area.
Denominator (B):
Total number of deaths in children < 5 during year z in a given country, territory or geographic area.
Underlying causes of death correspond to codes A00, A01, A03, A04, A06-A09 of the International Classification of Diseases, Tenth Revision.
Data for this indicator are from the World Health Organization (WHO) Global Health Estimates (GHE), based on information from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, the Global Burden of Disease, and other scientific studies.
By applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death. For countries without high-quality death registration data, estimates of the cause of death are calculated using other data, for example, household surveys with verbal autopsy, sentinel registry systems, or special studies.
Formula
(A/B) x 100
Numerator (A):
Number of deaths in children < 5 from ADD during year z in a given country, territory or geographic area.
Denominator (B):
Total number of deaths in children < 5 during year z in a given country, territory or geographic area.
Interpretation - example
According to 2019 data, 22% of deaths in children under 5 in country A were from infectious intestinal diseases, that is, during that year, for every 100 deaths in children under 5 in country A, 22 were due to an infectious intestinal disease.
Desagregation
No disaggregation
Limitations
The estimated value of this indicator may differ from each country’s calculations due to methodological considerations, such as the use of a different group of ICD-10 codes for the underlying cause of death or the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the proportion of deaths from infectious intestinal disease in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system and information on causes of death must be recorded; otherwise, the estimates will not be sufficiently accurate.
Estimating the proportion of deaths from infectious intestinal disease in children under 5 requires a civil registry system with good coverage. Births and deaths must be recorded in a timely manner in this system and information on causes of death must be recorded; otherwise, the estimates will not be sufficiently accurate.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://cdn.who.int/media/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf?sfvrsn=37bcfacc_5
Under-five mortality (1 000 lb)
Domain
Mortality
Subdomain
Child health
Definition
The quotient between the number of deaths in children under 5 years of age in a given country, territory, or geographic area during a specific calendar year and the total number of live births for the same population and year. Expressed per 1 000 births.
Measurement Unit
1 000 live births
Type of measurement
Rate
Type of statistics
Crude
Purpose
This indicator highlights under-5 deaths as a public health problem for a given population or geographic area. This value makes it possible to identify health inequities and populations with specific risk factors.
It becomes relevant because most causes of death in this age group are preventable and treatable through simple, affordable interventions, such as immunization, adequate nutrition, professional childbirth, breastfeeding, and access to safe water and basic services.
Under-5 mortality reflects children’s health status and social, economic, and environmental conditions and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health.
It becomes relevant because most causes of death in this age group are preventable and treatable through simple, affordable interventions, such as immunization, adequate nutrition, professional childbirth, breastfeeding, and access to safe water and basic services.
Under-5 mortality reflects children’s health status and social, economic, and environmental conditions and is related to maternal and child health care access, quality, and timeliness.
This indicator is applicable to the design, implementation, and evaluation of health policies and the distribution of economic, human, and technological resources aimed at improving maternal and child health.
Estimation method
Under-5 mortality uses the number of deaths in children under five years of age as the numerator and the total number of live births for the same population and year as the denominator, from the country’s information system (civil registry and vital statistics, survey, etc.).
Calculated with data from each country
Formula:
(A/B) x 1 000 live births
Numerator (A):
Number of deaths in children under five years of age in a given country territory, or geographic area during year z.
Denominator (B):
Total number of live births in the same population during year z.
To estimate the indicator by sex, the following formula is applied:
(A/B) x 1 000 live births
Numerator (A):
Number of deaths in children under five years of age of a given sex, in a specific country, territory, or geographic area during year z.
Denominator (B):
Total number of live births in the same population during year z.
Calculated with data from each country
Formula:
(A/B) x 1 000 live births
Numerator (A):
Number of deaths in children under five years of age in a given country territory, or geographic area during year z.
Denominator (B):
Total number of live births in the same population during year z.
To estimate the indicator by sex, the following formula is applied:
(A/B) x 1 000 live births
Numerator (A):
Number of deaths in children under five years of age of a given sex, in a specific country, territory, or geographic area during year z.
Denominator (B):
Total number of live births in the same population during year z.
Interpretation - example
According to data for 2019, the under-5 mortality rate in country A was 8.3 per 1 000 live births; that is, in that year eight children died, before the age of five, per 1 000 live births in country A.
Desagregation
By sex
Limitations
Under-5 mortality requires a civil registry system with good coverage, and births and deaths must be recorded in a timely manner in this system; otherwise, this indicator will not be sufficiently accurate.
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths.
The value of this indicator may differ from each country’s value due to methodological differences such as the application of methods to correct underreporting of births and deaths.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.2.1: Under‐5 mortality rate.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 4. Under-5 mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
Indicator 3.2.1: Under‐5 mortality rate.
Available from: https://sdg.data.gov/
Pan American Health Organization. Strategic Plan of the Pan American Health Organization 2020-2025.
Indicator 4. Under-5 mortality rate
Available from: https://www.paho.org/en/documents/paho-strategic-plan-2020-2025
References
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
ICD-10, International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Vol 2. Available from: https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf
Unemployment rate (%)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The proportion of the economically active population that is out of work, yet available and seeking employment in a given country, territory, or geographical area, during a specific period, usually one year. This indicator is based on the labor force or the economically active portion of the population, not the total population.
An unemployed person is defined as one who is out of work, who was recently seeking work, and who is currently available to work. This includes people who lost their job or left voluntarily. People not looking for work but who have arrangements for future employment are also counted as unemployed.
An unemployed person is defined as one who is out of work, who was recently seeking work, and who is currently available to work. This includes people who lost their job or left voluntarily. People not looking for work but who have arrangements for future employment are also counted as unemployed.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected
Purpose
The unemployment rate reflects the socioeconomic development and health status of a country, the purchasing power of its population and, in some cases, marginalization from social security and health systems that are accessed through formal employment.
Its value allows analysis of the temporal and geographical trends in unemployment in a population and identification of at-risk groups for public policies promoting employment, health, education and social protection. It contributes to evaluating the effectiveness of labor insertion strategies and encourages research in this area.
Its value allows analysis of the temporal and geographical trends in unemployment in a population and identification of at-risk groups for public policies promoting employment, health, education and social protection. It contributes to evaluating the effectiveness of labor insertion strategies and encourages research in this area.
Estimation method
The World Bank estimates unemployment rates based on household survey data it collects. The numerator considers the economically active but unemployed population of both sexes or a given sex; the denominator includes the total economically active population of both sexes or a given sex, as appropriate, for a given country, territory, or geographical area at a specific point in time. The value of this indicator represents mid-year estimates.
Interpretation - example
The unemployment rate of country A in 2019 was 10.2%, meaning that 10.2% of the economically active population residing in the country did not have a job, but was currently available and looking for work.
Desagregation
By sex
Limitations
The definitions of labor force, economically active population, and unemployment differ from one country to the next, as do the reference periods, which makes it difficult to compare this indicator.
One of the factors affecting the value of the unemployment rate is the difficulty in quantifying informal employment in the absence of regulations, records, and tracking data. Another factor is the exclusion of people who want to work but are not looking for work (often called the "hidden unemployed" or "discouraged workers"), as this affects the unemployment numbers.
The value of the unemployment rate is affected by the timing of the household survey used to obtain the data. Agriculture, for example, is subject to seasonal variations in the level of unemployment.
The accuracy of this indicator depends on the timeliness, frequency, quality, and comparability of household surveys. The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
One of the factors affecting the value of the unemployment rate is the difficulty in quantifying informal employment in the absence of regulations, records, and tracking data. Another factor is the exclusion of people who want to work but are not looking for work (often called the "hidden unemployed" or "discouraged workers"), as this affects the unemployment numbers.
The value of the unemployment rate is affected by the timing of the household survey used to obtain the data. Agriculture, for example, is subject to seasonal variations in the level of unemployment.
The accuracy of this indicator depends on the timeliness, frequency, quality, and comparability of household surveys. The frequency with which countries update their data and transfer it to the World Bank for calculation affects the result of this indicator.
Data source(s)
World Bank. Open Data & Databank. World Development Indicators. Available from: https://data.worldbank.org/indicator
Update periodicity PAHO
For the most recent data, see the primary source (World Bank).
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals.
Indicator 8.5.2 Unemployment rate, by sex, age and persons with disabilities
Available from: https://sdgs.un.org/goals
Indicator 8.5.2 Unemployment rate, by sex, age and persons with disabilities
Available from: https://sdgs.un.org/goals
References
World Bank Open Data. Available from: https://data.worldbank.org/
International Labour Organization (ILOSTAT). Available from:
https://ilostat.ilo.org/data/
International Labour Organization (ILOSTAT). Available from:
https://ilostat.ilo.org/data/
Unintentional poisoning mortality rate (age-adjusted per 100 000 pop)
Domain
Mortality
Subdomain
Cause of death
Definition
The estimated total number of deaths from unintentional poisoning and exposure to harmful substances in the population, in a given country, territory or geographic area during a specific calendar year, divided by the total number of this population, generally estimated in the middle of the same year (1 July), after removing the effect of differences in the age distribution of the population. Expressed per 100 000 population.
Measurement Unit
Per 100 000 population
Type of measurement
Rate
Type of statistics
Corrected
Purpose
The age-adjusted unintentional poisoning mortality rate is directly related to improper handling of hazardous chemicals and pollution. It reflects the health system’s effectiveness and a population’s socio-economic development. Its analysis makes it possible to identify populations at higher risk of dying from unintentional poisoning and exposure to harmful substances and to determine the focus of public health actions, as well as encourage research in this area.
Its result is applicable to the design, implementation, and evaluation of health policies on unintentional poisoning and exposure to harmful substances and to the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and rehabilitation of this group of causes, among others. Its applications include, for example, evaluating over time the effectiveness of interventions to reduce environmental pollution and of early diagnostic strategies aimed at reducing morbidity and mortality from unintentional poisoning.
Adjusting for age allows for the comparison of the unintentional poisoning mortality rate across populations or in the same population over time.
Its result is applicable to the design, implementation, and evaluation of health policies on unintentional poisoning and exposure to harmful substances and to the distribution of economic, human, and technological resources for the prevention, diagnosis, treatment, and rehabilitation of this group of causes, among others. Its applications include, for example, evaluating over time the effectiveness of interventions to reduce environmental pollution and of early diagnostic strategies aimed at reducing morbidity and mortality from unintentional poisoning.
Adjusting for age allows for the comparison of the unintentional poisoning mortality rate across populations or in the same population over time.
Estimation method
The numerator of this indicator uses deaths from unintentional poisoning and exposure to harmful substances from the World Health Organization (WHO) Global Health Estimates (GHE). These estimates are based on data from multiple sources, such as national civil registry systems, estimates from WHO technical programs, the United Nations and inter-agency groups, and the Global Burden of Disease, among others.
The underlying causes of death correspond to codes X40, X43, X46 - X48, X49 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted mortality rate from unintentional poisoning and exposure of harmful substances are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The mortality rate from unintentional poisoning and exposure to harmful substances is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
The underlying causes of death correspond to codes X40, X43, X46 - X48, X49 of the International Classification of Diseases, Tenth Revision (ICD-10).
The populations used in the denominator of the age-adjusted mortality rate from unintentional poisoning and exposure of harmful substances are from estimates by the United Nations Population Division.
Applying advanced statistical models, and depending on the data source used and its quality, adjustments are made to avoid bias and ensure compliance with standards and comparability of results across countries. If the data are from civil registry systems, adjustments include, but are not limited to, underreporting of deaths, unknown age and sex, and ill-defined causes of death, as well as garbage codes.
For more details on methodology, see:
World Health Organization (WHO). Division of Data, Analytics and Delivery for Impact (DDI). WHO methods and data sources for country-level causes of death 2000-2019. December 2020. Available from: https://www.who.int/docs/default-source/gho-documents/global-health-estimates/ghe2019_cod_methods
The mortality rate from unintentional poisoning and exposure to harmful substances is adjusted for age by direct standardization, applying estimated age-specific mortality rates, for both sexes or for a given sex, to the World Health Organization (WHO) World Standard Population.
Interpretation - example
The age-adjusted mortality rate from unintentional poisoning and exposure to harmful substances for 2019 was 12.9 per 100 000 population in country A and 6.1 per 100 000 population in country B; that is, in that year unintentional poisoning was responsible for the death of 12 people per 100 000 population of country A, compared to country B, where six people died from the same group of causes per 100 000 population. This means that, after removing the effect of differences in the age structure in the two countries, the risk of dying from unintentional poisoning and exposure to harmful substances in 2019 was higher in the population of country A than in country B.
Desagregation
By sex
Limitations
The age-adjusted unintentional poisoning mortality rate is a fictitious value, the main purpose of which is to allow for the comparison of this indicator across populations or in the same population over time; therefore, it should be interpreted with caution.
The estimated value of the age-adjusted unintentional poisoning mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the method for making population estimates and projections, the use of a different group of ICD-10 codes, and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the unintentional poisoning mortality rate requires a civil registry system with good coverage. Deaths from this group of pathologies must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality and must include the substance responsible for the poisoning; otherwise, the estimates will not be sufficiently accurate. Another limitation is the low percentage of countries with poison centers.
The estimated value of the age-adjusted unintentional poisoning mortality rate will depend on the standard population used for its adjustment; therefore, it may differ from each country’s estimates. Other methodological considerations that influence its result are the method for making population estimates and projections, the use of a different group of ICD-10 codes, and the application of algorithms to correct underreporting and redistribute ill-defined causes, among others.
Estimating the unintentional poisoning mortality rate requires a civil registry system with good coverage. Deaths from this group of pathologies must be recorded in a timely manner in that system, and certification of the cause of death must be of good quality and must include the substance responsible for the poisoning; otherwise, the estimates will not be sufficiently accurate. Another limitation is the low percentage of countries with poison centers.
Data source(s)
World Health Organization (WHO). Global Health Estimates. Available from: https://www.who.int/data/global-health-estimates
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs. Sustainable Development Goals (SDG).
Indicator 3.9.3 Mortality rate attributed to unintentional poisoning.
Available from: https://sdgs.un.org/goals
Indicator 3.9.3 Mortality rate attributed to unintentional poisoning.
Available from: https://sdgs.un.org/goals
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). International Programme on Chemical Safety. The WHO Global Chemicals and Health Network. Available from: https://www.who.int/publications/m/item/global-chemicals-and-health-network-flyer
World Health Organization (WHO). International Programme on Chemical Safety. WHO Chemicals Road Map and Workbook. Available from: https://www.who.int/publications/i/item/9789241513630
Ahmad O., Boschi-Pinto C., Lopez A., Murray C., Lozano R., Inoue M. Age standardization of rates: a new WHO standard. GPE Discussion Paper Series: No. 31 EIP/GPE/EBD World Health Organization 2001. Available from: https://www.researchgate.net/publication/284696312_Age_Standardization_of_Rates_A_New_WHO_Standard
World Health Organization (WHO). World Health Data Platform. The Global Health Observatory. Indicators. Available from: https://www.who.int/data/gho/indicator-metadata-registry
World Health Organization (WHO). International Programme on Chemical Safety. The WHO Global Chemicals and Health Network. Available from: https://www.who.int/publications/m/item/global-chemicals-and-health-network-flyer
World Health Organization (WHO). International Programme on Chemical Safety. WHO Chemicals Road Map and Workbook. Available from: https://www.who.int/publications/i/item/9789241513630
Urban Population (%)
Domain
Sociodemographic
Subdomain
Demographic
Definition
Proportion of the population of a country, territory, or geographical area residing in areas defined as urban at a specific point in time, usually at mid-year (1 July). Expressed as a percentage of the total population.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Corrected/Predicted
Purpose
The proportion of the population residing in urban areas is used to measure the need for basic services. This supports budget allocation and planning, management, and evaluation of public policies on health, education, labor, and social security. The indicator also contributes to analyzing health inequalities.
Alongside the proportion of the population residing in rural areas, this indicator makes it possible to assess the effects of migratory flows by analyzing changes in the territorial and temporal population distribution.
Alongside the proportion of the population residing in rural areas, this indicator makes it possible to assess the effects of migratory flows by analyzing changes in the territorial and temporal population distribution.
Estimation method
Urban population data represent estimated mid-year values (1 July), obtained by linear interpolation of the corresponding five-year United Nations urban population projections, which are sufficiently uniform and consistent with the five-year United Nations population projections using the medium fertility variant.
Countries determine what areas are "urban" as part of their census procedures, and no single definition can be applied to all countries. It refers essentially to cities, towns, and other densely populated areas, also considering other criteria such as the size of the population in each location, the distance between common areas, the predominant economic activity, administrative or legal boundaries, and availability of and access to basic services.
Countries determine what areas are "urban" as part of their census procedures, and no single definition can be applied to all countries. It refers essentially to cities, towns, and other densely populated areas, also considering other criteria such as the size of the population in each location, the distance between common areas, the predominant economic activity, administrative or legal boundaries, and availability of and access to basic services.
Interpretation - example
The percentage of urban population in country A in 2018 was 80.8%, meaning that 8 out of 10 people resided in areas considered to be urban.
Desagregation
By urban, rural area
Limitations
There is no internationally agreed definition of urban-rural area and national operational definitions may vary from country to country and may change over time.
The international comparability of this indicator may be limited by factors such as the quality of population censuses, demographic surveys, and national civil registration systems used to calculate estimates.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
The international comparability of this indicator may be limited by factors such as the quality of population censuses, demographic surveys, and national civil registration systems used to calculate estimates.
The estimated value of this indicator may differ from country statistics due to factors such as methodological differences in developing population estimates and projections.
Data source(s)
United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects. Available from:
https://population.un.org/wup/
https://population.un.org/wup/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
United Nations, Department of Economic and Social Affairs. 2019 Demographic Yearbook, 70th Issue. New York, 2020. Available from: https://unstats.un.org/unsd/demographic-social/products/dyb/
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
United Nations, Department of Economic and Social Affairs, Population Division. Glossary of Demographic Terms. Available from:
https://population.un.org/wpp/GlossaryOfDemographicTerms/
Economic Commission for Latin America and the Caribbean (ECLAC) - CEPALSTAT. Statistical Databases and Publications. Available from:
https://statistics.cepal.org/portal/cepalstat/dashboard.html?lang=en
Wild poliomyelities cases
Domain
Morbidity
Subdomain
Child health
Definition
Number of confirmed cases of wild poliovirus in children under 5 years of age reported during a specific year, in a given country, territory, or geographical area. A case is confirmed if wild poliovirus is isolated from stool samples obtained from a case of acute flaccid paralysis (AFP).
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
This indicator facilitates monitoring of the achievements of the Comprehensive Strategic Plan for the Eradication of Polio and enables analysis of the disease's temporal and geographical trends in endemic areas. It supports decision making in public policies aimed at completely eradicating this disease worldwide.
The number of cases of wild poliovirus reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and populations with greater risk factors for endemic transmission of wild poliovirus.
The indicator is applied for designing, implementing, and assessing health policies to prevent, treat, and control polio in children and distribute economic, human, and technological resources to fight the disease, among other purposes. Among its main purposes is strengthening immunization programs.
The number of cases of wild poliovirus reflects the health status and health and socioeconomic development of a population. It helps to identify health inequities and populations with greater risk factors for endemic transmission of wild poliovirus.
The indicator is applied for designing, implementing, and assessing health policies to prevent, treat, and control polio in children and distribute economic, human, and technological resources to fight the disease, among other purposes. Among its main purposes is strengthening immunization programs.
Estimation method
The number of laboratory-confirmed cases of wild poliovirus is obtained from data collected by national disease surveillance and control systems and reported by countries in the Region of the Americas.
Interpretation - example
In 2019, there were 4 confirmed cases of wild poliovirus in the population residing in country A.
Desagregation
No disaggregation
Limitations
Estimates of wild poliovirus cases are affected by factors such as: the effectiveness of national polio surveillance and control systems, diagnostic suspicion, and the existence of adequate laboratory networks to confirm diagnoses.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
United Nations (UN). Department of Economic and Social Affairs.
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.
Available from: https://sdgs.un.org/
Sustainable Development Goals (SDG).
Indicator 3.b.1 Proportion of the target population covered by all vaccines included in their national programme.
Available from: https://sdgs.un.org/
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from: https://opendata.paho.org/en/core-indicators
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Indicadores básicos para a saúde no Brasil: conceitos e aplicações, 2ª edição [Core health indicators in Brazil: concepts and applications]. Pan American Health Organization. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
World Health Organization (WHO). The Global Health Observatory (GHO). Available from: https://www.who.int/data/gho/indicator-metadata-registry
Indicadores básicos para a saúde no Brasil: conceitos e aplicações, 2ª edição [Core health indicators in Brazil: concepts and applications]. Pan American Health Organization. Brasilia, 2008. Available from: https://www.paho.org/bra/dmdocuments/indicadores.pdf
Women accessing prenatal care since the first trimester (%)
Domain
Health service coverage
Subdomain
Maternal and reproductive health
Definition
The number of pregnant women who have received health care since the first trimester of pregnancy in relation to the total number of live births, expressed as a percentage, in a given country, territory, or geographical area, in a given year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude
Purpose
Prenatal care initiated early during pregnancy makes it possible to prevent and identify complications in the mother and child. As such, it is closely related to the reduction of fetal, maternal, and neonatal morbidity and mortality. Prenatal check-ups also increase the chances of access to birth care by trained staff and allow interventions related to health promotion and parenting skills development, along with social and psychological support for the expectant mother.
This indicator makes it possible to identify populations that need increased coverage, availability, and access to maternal and child health services, and raises awareness of inequities in health. It can be applied during planning, management, and evaluation of health policies and maternal and child health services.
This indicator makes it possible to identify populations that need increased coverage, availability, and access to maternal and child health services, and raises awareness of inequities in health. It can be applied during planning, management, and evaluation of health policies and maternal and child health services.
Estimation method
Numerator: Number of women with a live birth who have received medical care since the first trimester in a given year. Data includes all sectors, such as private, public and social.
Denominator: Total number women with a live birth during the same period, in a given year.
The data comes from countries' routine information systems.
Denominator: Total number women with a live birth during the same period, in a given year.
The data comes from countries' routine information systems.
Interpretation - example
During 2019, 87% of women who had a live birth during that year began prenatal checkups during the first trimester of pregnancy.
Desagregation
No disaggregation
Limitations
This indicator does not allow evaluation of the quality of health care received, nor the number of prenatal check-ups that a woman has during her pregnancy. It also does not allow differentiation between the professionals providing care (doctors, nurses, midwives) or the type of care received during prenatal check-ups, or evaluation of availability or accessibility of this type of health service.
The outcome of this indicator is limited by the country's ability to record in a timely manner all births and live births that occurred during a given period. The definition of live birth may differ between countries.
The outcome of this indicator is limited by the country's ability to record in a timely manner all births and live births that occurred during a given period. The definition of live birth may differ between countries.
Data source(s)
National health authority
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO)/World Health Organization (WHO). Core indicators Portal. Available from:
https://opendata.paho.org/en/core-indicators
World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. Geneva, 2016. Available from: https://www.who.int/publications/i/item/9789241549912
UNICEF. Antenatal care. Available from: https://data.unicef.org/topic/maternal-health/antenatal-care/
https://opendata.paho.org/en/core-indicators
World Health Organization. WHO recommendations on antenatal care for a positive pregnancy experience. Geneva, 2016. Available from: https://www.who.int/publications/i/item/9789241549912
UNICEF. Antenatal care. Available from: https://data.unicef.org/topic/maternal-health/antenatal-care/
Yellow fever cases
Domain
Morbidity
Subdomain
Communicable diseases
Definition
Number of yellow fever cases in the population of both sexes or a given sex, in a given country, territory, or geographical area, during a specific year. Includes cases confirmed clinically, epidemiologically, or by laboratory.
Measurement Unit
Cases
Type of measurement
Magnitude
Type of statistics
Crude
Purpose
Yellow Fever is an acute hemorrhagic disease caused by a Flavivirus borne by Aedes and Haemogogus mosquitoes and is endemic in the tropical areas of Africa and Central and South America. There is no specific treatment, but it can be prevented with a highly effective vaccine.
The number of cases reflects the intensity of the disease in a population. The indicator is used to analyze the disease's temporal and geographical trends, identify areas in need of intensified vector control strategies, and strengthen immunization programs and environmental and epidemiological surveillance. It facilitates allocation of economic, human, and technological resources to control yellow fever.
The indicator is part of the monitoring framework of the WHO Global Strategy to Eliminate Yellow Fever Epidemics (EYE).
Endemic countries in the Americas are: Bolivia, Brazil, Colombia, French Guiana, Guyana, Panama, Paraguay, Peru, Venezuela, Suriname, Trinidad and Tobago, and Venezuela.
The number of cases reflects the intensity of the disease in a population. The indicator is used to analyze the disease's temporal and geographical trends, identify areas in need of intensified vector control strategies, and strengthen immunization programs and environmental and epidemiological surveillance. It facilitates allocation of economic, human, and technological resources to control yellow fever.
The indicator is part of the monitoring framework of the WHO Global Strategy to Eliminate Yellow Fever Epidemics (EYE).
Endemic countries in the Americas are: Bolivia, Brazil, Colombia, French Guiana, Guyana, Panama, Paraguay, Peru, Venezuela, Suriname, Trinidad and Tobago, and Venezuela.
Estimation method
The data is primarily collected from national surveillance systems that regularly report to the Pan American Health Organization.
Interpretation - example
In country A, 140 cases of yellow fever were reported in 2019.
Desagregation
By sex
Limitations
The value of this indicator depends on the effectiveness of surveillance systems, which in turn may be affected by low diagnostic suspicion and underreporting of cases. Other factors include poor access to laboratories with appropriate methods to diagnose yellow fever, and availability of data on confirmed cases from private health centers.
Data source(s)
National yellow fever surveillance systems
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
Pan American Health Organization (PAHO). Technical report: Recommendations for scientific evidence-based yellow fever risk assessment in the Americas. Washington D.C. 2017. Available from: https://www.paho.org/en
ode/57737
World Health Organization (WHO). A global strategy to eliminate yellow fever epidemics (EYE) 2017–2026. Available from: https://apps.who.int/iris/handle/10665/272408
World Health Organization (WHO). Vaccine-Preventable Diseases. Surveillance Standard. Yellow Fever. 2020. Available from:
https://cdn.who.int/media/docs/default-source/immunization/vpd_surveillance/vpd-surveillance-standards-publication/who-surveillancevaccinepreventable-23-yellowfever-r1.pdf?sfvrsn=a8d50bc6_10&download=true
ode/57737
World Health Organization (WHO). A global strategy to eliminate yellow fever epidemics (EYE) 2017–2026. Available from: https://apps.who.int/iris/handle/10665/272408
World Health Organization (WHO). Vaccine-Preventable Diseases. Surveillance Standard. Yellow Fever. 2020. Available from:
https://cdn.who.int/media/docs/default-source/immunization/vpd_surveillance/vpd-surveillance-standards-publication/who-surveillancevaccinepreventable-23-yellowfever-r1.pdf?sfvrsn=a8d50bc6_10&download=true
Youth literacy rate (%)
Domain
Sociodemographic
Subdomain
Socioeconomic
Definition
The proportion of the population aged 15 to 24 years that is literate, compared to the total population of the same age group, in a given country, territory, or geographical area at a specific point in time, usually in the middle of the year.
Measurement Unit
Percentage
Type of measurement
Proportion
Type of statistics
Crude/Corrected
Purpose
This indicator reflects the achievements of primary education and literacy programs within the previous 10 years and thus provides a measure of the number of people within the population aged 15 to 24 who have who have gone through primary education and acquired basic literacy skills, the ability to communicate in daily life through the written word and to continue learning, as well such as arithmetic skills.
Literacy offers potential for intellectual improvement and contributes to a society's economic and sociocultural development. It is essential for promoting and extending sustainable development, enhancing the capacity to achieve their goals, develop their knowledge and potential, to address environmental and development issues, and improving effective participation in decision-making in their communities and societies.
Literacy deficits highlight the need to focus efforts on expanding literacy to the rest of the illiterate population.
Literacy offers potential for intellectual improvement and contributes to a society's economic and sociocultural development. It is essential for promoting and extending sustainable development, enhancing the capacity to achieve their goals, develop their knowledge and potential, to address environmental and development issues, and improving effective participation in decision-making in their communities and societies.
Literacy deficits highlight the need to focus efforts on expanding literacy to the rest of the illiterate population.
Estimation method
A person is considered "literate" if they can read, write, and understand a simple and short text about their daily life. Generally, "literacy" also encompasses the ability to perform simple arithmetic calculations.
Youth illiteracy is defined as the percentage of the population aged 15 to 24 years that cannot read, write, or understand a short, simple text about their daily life.
The youth literacy rate is obtained from data collected by the United Nations Educational, Scientific, and Cultural Organization (UNESCO), primarily national population censuses and household/active population surveys.
The definition of literacy rate is in line with the revised recommendations on the International Standardization of Educational Statistics, adopted by UNESCO. Data on literacy rates are derived from estimated values at mid-year.
Youth illiteracy is defined as the percentage of the population aged 15 to 24 years that cannot read, write, or understand a short, simple text about their daily life.
The youth literacy rate is obtained from data collected by the United Nations Educational, Scientific, and Cultural Organization (UNESCO), primarily national population censuses and household/active population surveys.
The definition of literacy rate is in line with the revised recommendations on the International Standardization of Educational Statistics, adopted by UNESCO. Data on literacy rates are derived from estimated values at mid-year.
Interpretation - example
Country A's youth literacy rate for 2019 was 90%. This means that 9 out of 10 people aged 15 to 24 are literate.
Desagregation
By sex
Limitations
Because the value of this indicator may be affected by demographic changes in a country, literacy rates should be presented and analyzed alongside the absolute number of adult illiterates. This is because literacy and illiteracy rates may increase at the same time, according to changes in the demographic structure.
The outcome of the literacy rate is based on the standard definition of literacy, however, the definitions and criteria of literacy used in some countries may differ from established international standards or be subject to changes between population censuses, which may skew the results. Bias when self-reporting on literacy is another factor that influences the accuracy of this indicator. Estimating literacy rate require conducting censuses and surveys under controlled conditions, since literacy is difficult to measure.
In countries where almost all people have completed basic education, the literacy rate provides limited information about the variation in literacy skills in the population.
The frequency with which countries update their data and transfer it to UNESCO for calculation affects the result of this indicator.
The outcome of the literacy rate is based on the standard definition of literacy, however, the definitions and criteria of literacy used in some countries may differ from established international standards or be subject to changes between population censuses, which may skew the results. Bias when self-reporting on literacy is another factor that influences the accuracy of this indicator. Estimating literacy rate require conducting censuses and surveys under controlled conditions, since literacy is difficult to measure.
In countries where almost all people have completed basic education, the literacy rate provides limited information about the variation in literacy skills in the population.
The frequency with which countries update their data and transfer it to UNESCO for calculation affects the result of this indicator.
Data source(s)
United Nations Educational, Scientific, and Cultural Organization (UNESCO). Institute for Statistics. Available from: http://uis.unesco.org/
Update periodicity PAHO
Annual
Link to SDG / SP20-25
Not applicable
References
UNESCO Education. Available from: https://www.unesco.org/en/education
UNESCO. Quick Guide to Education Indicators for SDG 4. Available from: http://uis.unesco.org/sites/default/files/documents/quick-guide-education-indicators-sdg4-2018-en.pdf
UNESCO. Glossary. Available from: http://uis.unesco.org/en/glossary
UNESCO. Quick Guide to Education Indicators for SDG 4. Available from: http://uis.unesco.org/sites/default/files/documents/quick-guide-education-indicators-sdg4-2018-en.pdf
UNESCO. Glossary. Available from: http://uis.unesco.org/en/glossary