socioeconomic differentials helps to identify high-risk groups to which health programs can be most efficiently directed.

This chapter reviews recent evidence about the extent and sources of socioeconomic differences in mortality and health among older persons in the United States, with some reference to other countries. That is, the principal focus is on the first concern addressed above, rather than on the use of socioeconomic information for epidemiologic or programmatic purposes. With the proliferation of well-designed epidemiologic studies of precisely measured risk factors, there is less and less need to use the rather crude information provided by socioeconomic variables to identify etiologic factors in disease. Indeed, some of the early efforts to do so yielded interpretations that proved to be seriously misleading (e.g., the supposed link between highly demanding intellectual activities and coronary heart disease in Ryle and Russell, 1949). And socioeconomic groups are also a rather amorphous basis for designing health interventions, for which geographic or organizational detail is often more salient.

Even if the focus is on inequality, we have to justify a concern with inequality among groups arrayed on variables such as education or income. Other measures of inequality, such as the variance in ages at death, are also available and make no reference to such variables (Illsley and LeGrand, 1987). The concern with structured inequality—that associated with an individual's socioeconomic position—appears to derive from a belief that society at large has some influence on the structure of social positions and on who occupies them. If inequalities in the outcomes associated with that structure are too great, a sense of collective responsibility can generate efforts to reduce inequality. No similar reaction would be generated if the principal source of variation in mortality were, say, the ownership of a motorcycle or left-handedness.

Unfortunately, the measurement of inequality in health and mortality is not straightforward. The principal issue is not choosing one of the many inequality measures available but rather deciding, as Sheps (1958) put it, whether to count the living or the dead. In comparing the extent of inequality across times and places, this distinction is often critical. For example, if the probability of death for manual workers declines from .10 to .05, and for nonmanual workers from .05 to .02, then the ratio of manual to nonmanual death probabilities has risen but the ratio of manual to nonmanual survival probabilities has also risen. Which group has become relatively worse off after the change? This question is hardly academic, since these kinds of changes are widely observed. Hansluwka (1986) shows, for example, that Gini coefficients of social class inequality in infant mortality in England and Wales rose between 1921 and 1970-1972 when expressed in terms of mortality, but fell when expressed in terms of survivorship. We believe that differences in age-specific survivorship—the desideratum—are more salient

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