Periodically reviewing progress toward meeting the SDG 3 targets is essential to guide health policy decisions and to be accountable for the commitment to “leave no one behind” on the path to sustainable development by ensuring health and well-being for all at all ages. To facilitate this critical task, PAHO has developed a dashboard to monitor indicators for the SDG 3 targets. This dashboard provides evidence of the current status and trends for the SDG3 indicators according to regional averages and social inequalities between countries, and facilities access to data. Specifically, the monitoring dashboard explores the following elements for each SDG 3 indicator at the regional level:
- Time series analysis of the regional averages for each indicator.
- Estimated time required to reach the 2030 target.
- Trends in social inequality between countries for each indicator.
- Time series forecasting for each indicator’s progress.
- Magnitude of the observed changes in the regional averages and changes in inequality for each indicator.
The dashboard uses the national-level indicator estimated values, produced by the World Health Organization and interagency groups of the United Nations System, such as the Interagency Group for Child Mortality Estimation. These estimates, which may differ from officially published national data, have the relative advantage of standardization to address the recorded data completeness and quality (e.g., underreporting and misclassification), which enables international comparability. In each dynamic view, the dashboard explicitly indicates the data source used.
For some SDG 3 indicators, information may not be available over time. In such a case, the dynamic view is blank to note the current absence of data, as this situation may change in the future and would allow the SDG3 indicator in question to be monitored.
Users interested in the methodological and statistical details related to this dashboard can consult the available methodological note.
Figure 1. Time series analysis of the regional averages for each indicator
This type of analysis makes it possible to describe the trend of an indicator in an observed time series, in addition to progress toward the target that has been established for the Americas Region. For each indicator, an evaluation of the trend and its progress was made. The progress assessment shows whether the indicator is moving towards or away from the established target, based on the time series from 2000 to 2014, and then from 2015 to the latest year with available data. In addition, two analyses of the average annual percentage change (AAPC) have been performed: one to show the change required to reach the 2030 target (AAPC required) and the other to show the observed change between 2015 and the latest year for which data are available (AAPC observed). The comparison between these two analyses allows us to assess the performance of the indicator. That is, what has been happening compared to what would be needed to achieve the specific 2030 target set for that indicator.
Figure 2. Estimated time required to reach the 2030 target
The average annual percentage change was estimated using the targets established for each indicator in the Americas Region as a reference, and the performance observed in the trend of the indicator from 2015 to the latest year available. Once that is done, it was possible to evaluate the time required to achieve these targets at the regional and subregional levels.
This analysis considers the average annual percentage change between the baseline year (2015) and the most recent year with available data for each indicator to estimate the overall picture of progress towards 2030. It should be noted that this analysis could only be performed for those indicators that had an established target, either relative or absolute.
The radar chart is organized in years. Each gray line is concentric to the origin points to a decade of time needed to reach the goals. The closer the point is to the center, the faster it will reach the established goals. The farther the point is away from the center, more time is required to reach the goals. The maximum time considered to establish the end of the graph is 50 years. Thus, all the points on the outermost radial line refer to indicators or regions that will take more than 50 years to reach the considered target; therefore, showing little or no progress toward achieving the SDG 3 targets.
Figure 3. Trends in social inequality between countries for each indicator
Using the Sustainable Development Index (SDI) as the equity stratifier, the trend for the absolute gap and the relative gap are shown for each SDG 3 health indicator.
The absolute gap is a simple summary metric of health inequality and corresponds to the arithmetic difference in the value of the health indicator between two socially determined population groups, usually extreme groups of social position. This metric is calculated according to the following expression:
where AG is the absolute gap; HI, the health indicator; q1, the most disadvantaged social position quintile; and q5, the most advantaged social position quintile. The absolute gap is expressed in the same units of measurement as the health indicator; a zero (0) absolute gap value denotes the absence of inequality.
The relative gap is a simple summary metric of health inequality and corresponds to the arithmetic quotient in the value of the health indicator between two socially determined population groups, usually extreme groups of social position. This metric is calculated according to the following expression:
where RG is the relative gap; HI, the health indicator; q1, the most disadvantaged social position quintile; and q5, the most advantaged social position quintile. The relative gap is expressed without units of measurement (i.e., its value represents the number of times the numerator is contained in the denominator). A relative gap value of one (1) denotes the absence of inequality.
Figure 4. Time series forecasting for each indicator’s progress
Predicting trends in SDG3 indicators performance helps to identify lagging indicators and priority areas. The forecast analysis (in this case, the analysis of the progress required to achieve the target set for each SDG 3 indicator) makes it possible to assess the expected values by considering the pattern in the trend between the observed value in 2000 and the most recent year with available data for each indicator or region. Thus, it is possible to compare the values expected in 2030 using the projection of the time series of indicators as a reference.
Machine learning techniques were used to perform the forecast analyses shown in the dashboard. In addition, confidence intervals produced by the selected model are presented. The red line represents the progress needed to reach the target set for 2030. This line does not appear if the selected region has already reached the target or if no target has been set for the selected indicator.
Figure 5. Magnitude of the observed changes in the regional averages and changes in inequality for each indicator
This visualization shows the average annual percentage change (observed between 2000 and the most recent year for which information is available) for the average value of an indicator and the absolute gap in social inequality for the same indicator in each of the America’s subregions. The combined analysis of these two values makes it possible to classify the subregions according to four categories presented in a quadrant chart:
- Quadrant 1: The subregional average improves and inequality increases among countries in the same subregion.
- Quadrant 2: The subregional average worsens and inequality increases among countries in the same subregion.
- Quadrant 3: The subregional average improves and inequality reduces among countries in the same subregion.
- Quadrant 4: The subregional average worsens and inequality decreases among countries in the same subregion.
Successful progress toward the SDG 3 targets, "leave no one behind", demands both average improvement and distributional improvement (Quadrant 3).
Average annual change pop-up
If we have the value of a health indicator (HI) at two defined times, t0 and t1, it is possible to calculate its average annual percentage change (AAPC) by means of the following expression, using natural logarithms.