In This Issue

March 2010 | Bradford H. Gray

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. This is the central message of the first article in this issue, “Implicit Value Judgments in the Measurement of Health Inequalities,” by Sam Harper, Nicholas King, Stephen Meersman, Marsha Reichman, Nancy Breen, and John Lynch.

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. The choices may involve measuring relative or absolute inequality or using weighted or unweighted data, to cite two of their examples. 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.

Measurement is also the subject of “A Healthy Bottom Line: Healthy Life Expectancy as an Outcome Measure for Health Improvement Efforts” by Matthew Stiefel, Rocco Perla, and Bonnie Zell. This article focuses on the value and use of healthy life expectancy, a population health measure that combines length and quality of life. The authors review the literature and describe the ways in which this measure has been used for such purposes as assessing population-level interventions and calculating disparities. They also discuss the requirements that can limit the applicability of health life expectancy. Like any rate measure of morbidity or mortality, healthy life expectancy requires data on a defined population and can become unstable if the size of that population is small. Furthermore, the health status and mortality data needed to calculate healthy life expectancy are not always available. But when such data are available, healthy life expectancy has advantages over the more commonly used measure of mortality.

The next article in this issue is “Health Care Reform in Massachusetts: Implementation of Coverage Expansions and a Health Insurance Mandate” by Michael Doonan and Katharine Tull. Individual mandates were part of the major national health care reform bills considered in 2009. Such mandates seem necessary if anything approaching universal coverage is to be achieved in a country that has a mix of payers, and they should help reduce the number of people who do not seek coverage until they need expensive services. Doonan and Tull provide insights into the use of mandates by describing the experience of Massachusetts, the only state that now has an individual mandate and where the uninsured rate fell below 3 percent in 2009 (Long and Phadera 2009).

The creation and administration of an individual insurance mandate involves various and interrelated policy issues and trade-offs. An example is the tension between the kinds of coverage that will satisfy the requirement and the ways to make it affordable to individuals or taxpayers. To resolve this problem, incentives and penalties must be created, as coverage subsidized by tax dollars may attract people who already have coverage. Doonan and Tull describe how the many politically sensitive policy problems were handled in the Massachusetts coverage expansion and how well the reform has worked in the first four years.

The next article is “Understanding the Organization of Public Health Delivery Systems: An Empirical Typology” by Glen Mays, F. Douglas Scutchfield, Michelyn Bhandari, and Sharla Smith. Their purpose is to facilitate assessments of the activities of local public health agencies by identifying a set of structural characteristics that distinguish them from one another. Their analysis is based on data from a national longitudinal survey of agencies serving communities of at least 100,000 residents.

The authors observe seven different configurations based on which of twenty core public health activities are performed in the jurisdiction and whether the activities are carried out by the agency or delegated to some other entity. These configurations are based on the comprehensiveness of the jurisdiction’s services and the extent to which their performance is concentrated in the agencies themselves. The data also enable the authors to determine the frequency of the different arrangements and how this changed between 1998 and 2006. They found that perceptions of effectiveness are the most positive for the most comprehensive systems. They conclude that several organizational configurations can be used to provide a broad and diversified set of public health activities and that which structure works best may depend on local circumstances that affect the ability of public health agencies to engage other organizations. Mays and his colleagues also suggest that policymakers use their typology to decide which service delivery models are the most feasible and desirable for their state or community.

This issue concludes with Allan Horwitz’s historical account “How an Age of Anxiety Became an Age of Depression.” In the middle of the last century, anxiety was what Horwitz calls the “emblematic mental health problem in the United States.” But depression gradually took over that spot. Horwitz looks at how and why this happened. He finds the answer not in changes in underlying disorders but in trends in psychiatry (e.g., the need for greater diagnostic specificity, as well as the move toward biological psychiatry and away from the psychoanalytic approach) and the marketing needs of the pharmaceutical industry. Changes in third-party payment systems and the concerns of federal research agencies also played a role. He concludes by cautioning about the extent to which the marketability of labels in the mental health field can drive treatment, research, and policy.

Bradford H. Gray
Editor, The Milbank Quarterly


Long, S.K., and L. Phadera. 2009. Estimates of Health Insurance Coverage in Massachusetts from the 2009 Massachusetts Health Insurance Survey. Boston: Division of Health Care Finance and Policy.

Author(s): Bradford H. Gray

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Volume 88, Issue 1 (pages 1–3)
DOI: 10.1111/j.1468-0009.2010.00586.x
Published in 2010