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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.

Context: Quantitative estimates of the magnitude, direction, and rate of change of health inequalities play a crucial role in creating and assessing policies aimed at eliminating the disproportionate burden of disease in disadvantaged populations. It is generally assumed that the measurement of health inequalities is a value-neutral process, providing objective data that are then interpreted using normative judgments about whether a particular distribution of health is just, fair, or socially acceptable., Methods: We discuss five examples in which normative judgments play a role in the measurement process itself, through either the selection of one measurement strategy to the exclusion of others or the selection of the type, significance, or weight assigned to the variables being measured., Findings: Overall, we find that many commonly used measures of inequality are value laden and that the normative judgments implicit in these measures have important consequences for interpreting and responding to health inequalities., Conclusions: Because values implicit in the generation of health inequality measures may lead to radically different interpretations of the same underlying data,we urge researchers to explicitly consider and transparently discuss the normative judgments underlying their measures. We also urge policymakers and other consumers of health inequalities data to pay close attention to the measures on which they base their assessments of current and future health policies.