Global Health Priorities
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Unexplained health inequality - is it unfair?

Identifying unfair health inequality is important in order to make correct priorities in health. But only a portion of observed health inequality can be explaind empircaly. A new paper explains why how we treat this unexplained inequality is not only a methodological question, but also an ethical one.

Unfair health inequalities

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Not all observed inequalitites in health are concidered unfair. In almost all societies women, on average, live longer lives than men. This is an observed health inequality, but most people don't see this as an unfair inequality and thereby it should not be a priority to try to reduce this inequality.

Simplified, unfair health inequalities are called health inequities, and this is the type of inequality that should concern us. When trying to measure inequities in health, empirical analysis can only explain a portion of the observed inequality. Typically, this has been seen as a question of methodology, but in this new paper co-author Ole Frithjof Norheim and colleagues argues that this also is an ethical question. Whether we see unexplained health inequality as fair or unfair determines the choice of method and can lead to substantial differences in estimates of health inequity.


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. 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.

We 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.

Our 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).

The 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.