Abstract
IntroductionAccurate measurement of health inequities is indispensable to track progress or to identify needs for health equity policy interventions. A key empirical task is to measure the extent to which observed inequality in health – a difference in health – is inequitable. Empirically operationalizing definitions of health inequity has generated an important question not considered in the conceptual literature on health inequity. Empirical analysis can explain only a portion of observed health inequality. This paper demonstrates that the treatment of unexplained inequality is not only a methodological but ethical question and that the answer to the ethical question – whether unexplained health inequality is unfair – determines the appropriate standardization method for health inequity analysis and can lead to potentially divergent estimates of health inequity.MethodsWe use the American sample of the 2002–03 Joint Canada/United States Survey of Health and measure health by the Health Utilities Index (HUI). We model variation in the observed HUI by demographic, socioeconomic, health behaviour, and health care variables using Ordinary Least Squares. We estimate unfair HUI by standardizing fairness, removing the fair component from the observed HUI. We consider health inequality due to factors amenable to policy intervention as unfair. We contrast estimates of inequity using two fairness-standardization methods: direct (considering unexplained inequality as ethically acceptable) and indirect (considering unexplained inequality as unfair). We use the Gini coefficient to quantify inequity.ResultsOur analysis shows that about 75% of the variation in the observed HUI is unexplained by the model. The direct standardization results in a smaller inequity estimate (about 60% of health inequality is inequitable) than the indirect standardization (almost all inequality is inequitable).ConclusionsThe choice of the fairness-standardization method is ethical and influences the empirical health inequity results considerably. More debate and analysis is necessary regarding which treatment of the unexplained inequality has the stronger foundation in equity considerations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12939-015-0138-2) contains supplementary material, which is available to authorized users.
Highlights
Accurate measurement of health inequities is indispensable to track progress or to identify needs for health equity policy interventions
We model variation in the observed Health Utilities Index (HUI) by demographic, socioeconomic, health behaviour, and health care variables using Ordinary Least Squares
Our analysis shows that about 75% of the variation in the observed HUI is unexplained by the model
Summary
Accurate measurement of health inequities is indispensable to track progress or to identify needs for health equity policy interventions. A key empirical task is to measure the extent to which observed inequality in health – a difference in health – is inequitable. Operationalizing definitions of health inequity has generated an important question not considered in the conceptual literature on health inequity. Accurate measurement of inequalities and inequities is indispensable to track progress or to identify needs for policy interventions [1,2]. Measurement of health inequities is more challenging than that of health inequalities for their requirements for data on determinants of health [3] and for ethical considerations. Alternative definitions of health inequity can be distinguished by the sources of health inequality each classified as ethically acceptable and unacceptable. Equal opportunity for health, a definition gaining popularity in the health economics literature [7,8,9,10], considers health inequality due
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