15 Global coverage targets
15.1 Rationale, Approach and implementation
Rationale: Scientific basis for the analysis
Reducing geographic inequality is essential for equitable health systems and achieving the SDGs. Subnational inequalities reveal inconsistencies in service delivery and highlight systemic barriers to healthcare access. Tracking inequality indicators helps assess whether health systems are becoming more equitable and whether targeted interventions are working.
Approach: Description of analytical steps
Here we calculate Median Absolute Deviation from the Median (MADM) to quantify spread in coverage in districts within an admin1 level , compared with the coverage at the said Admin1 subnational unit.
The key statistical measures for sub-national inequalities are:
MADM: median absolute distance from the median. This measure gives an indication on whether the country has been successful in reducing inequalities between sub-national units.
Percent of sub-national units with coverage above a specific target or threshold: this indicator provides information on the extent to which a country has been successful in reaching universal coverage at the sub-national level.
Summary of district and regional performance
Progress towards international and national targets can be measured by computing the percentage of regions and districts that have achieved these targets. The goal is for all regions and districts to have met the target. Higher percentages mean less inequality.
15.2 Implementation: Conducting analysis in the Shiny App
Navigate to the Subnational Analysis tab =>Subnational Inequality and inspect the plotted regional and district results by year, with the median absolute distance from the median (MADM, see screenshot below), as the summary measure to assess if inequalities have reduced.