Equity-oriented monitoring in the context of universal health coverage

Abstract

Monitoring inequalities in health is fundamental to the equitable and progressive realization of universal health coverage (UHC). A successful approach to global inequality monitoring must be intuitive enough for widespread adoption, yet maintain technical credibility. This article discusses methodological considerations for equity-oriented monitoring of UHC, and proposes recommendations for monitoring and target setting. Inequality is multidimensional, such that the extent of inequality may vary considerably across different dimensions such as economic status, education, sex, and urban/rural residence. Hence, global monitoring should include complementary dimensions of inequality (such as economic status and urban/rural residence) as well as sex. For a given dimension of inequality, subgroups for monitoring must be formulated taking into consideration applicability of the criteria across countries and subgroup heterogeneity. For economic-related inequality, we recommend forming subgroups as quintiles, and for urban/rural inequality we recommend a binary categorization. Inequality spans populations, thus appropriate approaches to monitoring should be based on comparisons between two subgroups (gap approach) or across multiple subgroups (whole spectrum approach). When measuring inequality absolute and relative measures should be reported together, along with disaggregated data; inequality should be reported alongside the national average. We recommend targets based on proportional reductions in absolute inequality across populations. Building capacity for health inequality monitoring is timely, relevant, and important. The development of high-quality health information systems, including data collection, analysis, interpretation, and reporting practices that are linked to review and evaluation cycles across health systems, will enable effective global and national health inequality monitoring. These actions will support equity-oriented progressive realization of UHC.

Figure 1. Patterns of inequality by wealth quintile, illustrated using births attended by skilled health personnel.

Four characteristic patterns of inequality across household wealth quintiles for coverage of births attended by skilled health personnel in four countries are shown: Bangladesh (mass deprivation), Gambia (queuing), Jordan (complete coverage), and Viet Nam (marginal exclusion). Data were collected as part of Demographic and Health Surveys and Multiple Indicator Cluster Surveys, 2005–2007. Wealth quintiles were determined using household asset indices. Source: .

Figure 2. Visualization of sample targets for global inequality monitoring of health service coverage.

A visualization of two sample targets for global monitoring of health service coverage (Box 3), applied to antenatal care (at least one visit) and births attended by skilled health personnel, in 30–31 countries. (a) Absolute inequality at baseline between the richest and poorest quintiles (quintiles determined using household asset indices), and urban and rural areas, along with overall national coverage at baseline; (b) the relative change in absolute inequality over 10 years, along with the relative change in national coverage. Shapes represent countries; within each pane, each country is represented by one shape. Horizontal lines indicate median values of all countries within the pane. Data were collected as part of Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Because survey years were not consistent across countries, country-level data spanning 9–11 year intervals were drawn from surveys at two time points between 1993–2011.