The Milbank Quarterly has an article examining the value judgements involved in measuring health inequalities, and it’s struck a chord with public health policy consultant Margo Saunders. She writes:
“Inequalities are a bit of an obsession with health policy people. They figure prominently in current Australian discussions of Indigenous health, as well as approaches to the health of specific population groups such as men, women, and rural populations.
Andrology Australia’s forthcoming men’s health forum has as its theme ‘Tackling the Inequities of Men’s Health’ and promises to focus on exploring ‘the disparities that exist between different groups of men’.
How straightforward a task is this likely to be, even before we get around to tackling the inequities? What are the values and assumptions that may be implicit in how we identify and discuss the perceived problems?
As an article in the March 2010 issue of The Milbank Quarterly points out, there are numerous hidden assumptions in the measurement of health inequalities, inequities and disparities.*
A team headed by Prof. Sam Harper at McGill University in Canada, and including John Lynch, Professor of Epidemiology and Population Health at the University of South Australia, provides a persuasive explanation of how these measurements shape popular and political understandings of health inequalities.
The authors go beyond acknowledging a distinction between relative and absolute inequality and argue that determining whether an inequality is increasing or decreasing is a normative as well as a mathematical exercise. The trend data on obesity, smoking and prostate cancer are among the examples used to illustrate their point about how very different impressions result when normative judgments are embedded in apparently objective descriptions of health trends.
As the Editor’s summary explains:
‘By measuring health-related differences among subgroups within populations, we can identify areas where improvement is needed and possible. But the measures we select sometimes reflect values in ways that are not recognized by researchers or policymakers, and they may incorporate normative judgments that affect how they are interpreted and used. …
‘Researchers can characterize health-related differences or disparities (the terms have different connotations) in a variety of ways: over time or by gender, region, race, ethnicity, or socioeconomic status within populations. Harper and his colleagues present five cases to show that the measures used or the way they are calculated can have a major effect on the nature and magnitude of differences that are found. … Their point is not that certain summary measures are necessarily preferable to other measures, because those used may depend on the purpose of the analysis or the characteristics of the data set. Instead, their point is that unrecognized value judgments can be built into measures of inequality.
Harper and his colleagues urge researchers to be aware of the implicit value judgments involved in the choice of measures, not to use a particular measure uncritically just because it is widely accepted, and to consider carefully the implicit normative judgments that may be embedded in any particular measure. They also urge researchers to strive for transparency and to be explicit about the judgments used in choosing the measure. The authors also advise policymakers and other users of information about health-related inequalities to consider carefully the measures used and to use more than one whenever possible.’
The article goes some way towards helping me understand my own niggling concerns about an inequalities-based approach to population health (concerns which relate primarily to the preoccupation with ‘closing gaps’ and the view that what matters across populations is a consistent level of healthiness or unhealthiness, rather than raising the level that we consider acceptable), and the accusations of political incorrectness with which those views tend to be met.
While the mathematically-minded, such as health economists and statisticians will be at an advantage, those seeking insights about the measurement and interpretation of health inequalities, and their use as the basis for policies and programs, will find much of value in this timely article.”
* Sam Harper, Nicholas B. King, Stephen C. Meersman, Marsha E. Reichman, Nancy Breen, and John Lynch, Implicit Value Judgments in the Measurement of Health Inequalities, , The Milbank Quarterly, Vol. 88, No. 1, 2010 (pp. 4–29), Wiley Periodicals Inc.