8
Socioeconomic Differences in Adult Mortality and Health Status

Samuel H. Preston and Paul Taubman

INTRODUCTION

In most populations, people with more schooling, higher income, and more prestigious occupations enjoy better health and longer lives. It would be surprising if this were not so because healthiness and longevity are nearly universal goals, and higher-ranking social groups have, on average, more resources with which to pursue those goals.

Although the direction of relations between health and socioeconomic attributes accords with common sense, the magnitude of these relations has been the object of intense scientific scrutiny. There appear to be three major reasons for this attention. First and foremost, societies are concerned not only with the average levels of welfare-related variables such as income and health but also with their distribution among social groups. Although views may differ about the desirable or appropriate extent of inequality, few would argue that inequality is irrelevant or outside the suitable domain of government action. Second, the widely available data on socioeconomic differentials in mortality and health sometimes provide important clues regarding the etiology of particular diseases, as in the case of polio, breast and cervical cancer, and coronary heart disease. Third, evidence about

The authors are grateful to Ingrid Waldron for generously sharing her wisdom and her library with us. Her comments were instrumental in improving the chapter.



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Demography of Aging 8 Socioeconomic Differences in Adult Mortality and Health Status Samuel H. Preston and Paul Taubman INTRODUCTION In most populations, people with more schooling, higher income, and more prestigious occupations enjoy better health and longer lives. It would be surprising if this were not so because healthiness and longevity are nearly universal goals, and higher-ranking social groups have, on average, more resources with which to pursue those goals. Although the direction of relations between health and socioeconomic attributes accords with common sense, the magnitude of these relations has been the object of intense scientific scrutiny. There appear to be three major reasons for this attention. First and foremost, societies are concerned not only with the average levels of welfare-related variables such as income and health but also with their distribution among social groups. Although views may differ about the desirable or appropriate extent of inequality, few would argue that inequality is irrelevant or outside the suitable domain of government action. Second, the widely available data on socioeconomic differentials in mortality and health sometimes provide important clues regarding the etiology of particular diseases, as in the case of polio, breast and cervical cancer, and coronary heart disease. Third, evidence about The authors are grateful to Ingrid Waldron for generously sharing her wisdom and her library with us. Her comments were instrumental in improving the chapter.

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Demography of Aging 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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Demography of Aging (and certainly more analogous to inequalities in other desiderata such as income and nutrition). Nevertheless, we must conduct this review by reference mainly to negative indicators such as mortality and disability because that is the convention in nearly all writings on the subject. It is worth noting that neither relative nor absolute differences in survival can be recovered from estimates of relative risk; it is the difference between mortality rates, not their ratio, that determines the ratio of survival rates.1 SOCIOECONOMIC MEASURES The principal indicators of one's position in contemporary society are income, occupation, and educational attainment. These are closely related to Weber's (1946) more abstract conception of social position in terms of the three dimensions of class (a primarily economic concept), status (associated with occupational prestige), and power (a function of one's ability to mobilize resources on one's behalf). Liberatos et al. (1988) provide a useful discussion of how the three indicators have been used in epidemiologic studies. They find that epidemiologists are much more likely to use education as a ''control variable" than either income or occupation. The disadvantages of occupation are that many people do not have one (e.g., retired people, housewives) and that one's occupation—and labor force participation—may be determined by one's health status as an adult (Fox et al., 1985). Such reverse causation creates problems of interpretation; in particular, it is not sensible to treat one variable as dependent and the other as independent. This problem is even more serious for income, since disabilities can affect not only occupation but also hours of work. Unlike occupation, education is measured on an interval scale. Unlike income, it is not derived from multiple sources with very different implications (or, in the case of family income, from multiple individuals). Because of its stability, 1   The probability of survival from age x to x+n for group i is where µi (a) = death rate for group i at age a. Therefore, the log of the ratio is equal to the cumulative absolute difference in death rates between groups 1 and 2.

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Demography of Aging educational attainment is an especially valuable measure among those over age 65. However, even the amount of education one obtains may be influenced by a long-lasting disability, which can affect subsequent mortality and morbidity. So the use of education does not resolve all problems of reverse causation. Educational attainment has also become the measure of choice among demographers and statisticians who study socioeconomic differences in mortality (e.g., Kitagawa and Hauser, 1973). But we must be aware that although they are correlated with one another, education, income, and occupation tap different features of socioeconomic position that are relevant to health. Most directly, income indicates the amount of resources available to purchase health-related goods and services, including medical services themselves. It may also reflect on-the-job health risks. Occupation is associated with a variety of physical and psychosocial features of the workplace. Educational attainment is associated with the availability of information and with cognitive skills. Perhaps by virtue of these connections, there is evidence that education is more closely associated with health behaviors and with cardiovascular risk factors than are the other two variables. Winkleby et al. (1992) show that educational attainment is the only socioeconomic variable having a significant relationship to cigarette smoking, blood pressure (women only), and high-density lipoprotein (HDL) cholesterol in a cross-sectional study of 2,380 participants in the Stanford Five-City Project.2 This may be the only study of their joint effects. RECENT EVIDENCE ON THE EXTENT OF SOCIOECONOMIC DIFFERENCES IN MORTALITY AND HEALTH STATUS After being for many years one of the industrialized countries with the poorest data on socioeconomic differences in mortality, the United States now has two large and high-quality data sources: the National Health and Nutrition Examination Survey (NHANES), which includes the National Health Epidemiologic Follow-up Study (NHEFS), and the National Longitudinal Mortality Study (NLMS). Both are probability samples of the entire U.S. noninstitutionalized population that have been followed forward from initial interviews. Both have overall mortality levels close to, but slightly better than, national vital statistics levels; the lower mortality level is likely to result primarily from their initial restriction to noninstitutionalized persons. NHEFS consists of 14,407 persons aged 25-74 when surveyed in 1971- 2   The income measure used was gross family income, unadjusted for family size or number of earners. The occupational variable was created by imposing an arbitrary cardinal scale on ordinal categories. It is possible that more refined measures of these variables would have performed better.

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Demography of Aging 1975 who were followed to 1982-1984. Feldman et al. (1989) have described educational differentials in mortality in this data set and compared them to the 1960 differentials based on a census-vital statistics matching study (Kitagawa and Hauser, 1973). Figure 8-1 displays the magnitude of educational differentials for older white persons, as well as trends in them, between the two observations. It is clear that except for men aged 75-84 in 1960, those with more education have lower death rates for all ages and both sexes in each year. It is also clear that apart from men aged 65-74 with 0-7 years of schooling, death rates declined over the period of observation for each age-sex-education category. What is perhaps most striking about the figure is the widening of educational differences in mortality for males between these dates.3 Such a tendency was earlier described by Taubman and Rosen (1979) based on a 1973 Current Population Survey matched to Social Security death records through 1976. No widening of differentials is evident for females, whose declines are essentially equiproportionate. After being much smaller, male differentials (as indicated by the educational range in the log of death rates) are roughly as large as female differentials by 1971-1984. A final tendency evident in the figure is a narrowing of the range of educational differences in mortality as age advances, particularly after account is taken of the expansion of education categories for the two oldest age groups. This observation is consistent with much observed human experience; in comparing the typical age pattern of mortality of a high-mortality population to that of a low-mortality population, proportionate differences narrow above age 40 or so as age advances (Coale and Demeny, 1982). Table 8-1 shows that much of the widening of educational differentials for white males aged 65-84 is attributable to a massive change in the educational distribution of heart disease mortality. Heart disease death rates declined by 57 percent for college-educated men and by 5 percent for men with less than 8 years of schooling. With 1960 education differences in heart disease mortality substituted for those in 1971-1984, the ratio of death rates from all causes for the two education groups would have been 1.20 in 1971-1984 instead of its actual value of 1.73. The second major source of data on socioeconomic differences in mortality is the NLMS. The study population consists of 1,281,475 people who were included in various Current Population Surveys of the Census Bureau 3   Some uncertainty about these trends is introduced by the high nonmatch rate in the census-vital statistics matching study: 18 percent of white male decedents aged 65+ were not matched to 1960 census records, and estimates of educational attainment for these individuals were based on a sample with a 25 percent nonresponse rate on the education question (Kitagawa and Hauser, 1973:189,193). These figures were no worse for men than for women, however.

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Demography of Aging Figure 8-1 Estimated annual death rates by age at death, sex, and educational attainment among white persons aged 55-84 years, United States, 1960 and 1971-1984. SOURCE: Feldman et al. (1989). Reprinted with permission.

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Demography of Aging TABLE 8-1 Annual Death Rates per 1,000 Population, by Cause of Death a, Sex, and Educational Attainment, Among White Persons Aged 65-84 Years, United States, 1960 and 1971-1984 Cause of Death and Years of School completed Males Females 1960 1971-1984 1960 1971-1984 Rate Rate (SE) Rate Rate (SE)  All causes    0-7 66.2 66.8 (4.1) 48.5 35.7 (3.0) 8 65.5 53.1 (4.0) 45.4 31.8 (2.8) 9-12 64.9 47.0 (3.2) 41.4 24.4 (1.9) 13+ 64.2 38.7 (4.1) 30.8 23.5 (2.8) Heart disease    0-7 30.6 29.2 (2.7) 22.9 14.0 (1.9) 8 31.5 27.0 (2.8) 20.9 12.2 (1.8) 9-12 31.2 18.6 (2.0) 18.6 9.9 (1.2) 13+ 30.3 13.0 (2.4) 12.6 7.5 (1.6) Other than heart disease    0-7 35.6 35.8 (3.0) 25.6 20.7 (2.3) 8 34.0 25.2 (2.7) 24.5 18.3 (2.2) 9-12 33.6 27.9 (2.5) 22.8 13.2 (1.4) 13+ 33.9 25.2 (3.3) 18.3 15.7 (2.3) a Cause of death is missing for 2 percent of deaths among males and 4 percent of deaths among females in 1971-1984. These deaths were included in calculating death rates for all causes, but excluded for cause-specific death rates. NOTE: SE = standard error. SOURCE: Feldman et al. (1989). Reprinted with permission. from 1973-1985, except for 10 percent of the participants who were drawn from the 1980 U.S. census of population. The cohorts were followed forward for a maximum of 7 years or to January 1, 1986, whichever came first (Rogot et al., 1992b:Table A). Records for these individuals were matched to the National Death Index beginning in 1979, yielding a total of 44,828 deaths. This study thus provides a firm basis for inferring the magnitude of socioeconomic differences in mortality. Table 8-2 presents educational differences in mortality from this study in the form of ratios of actual to expected deaths, where expected deaths are developed by applying the average probability of dying in a particular sex, race, and 5-year age group to each individual's years of exposure. Because the study is much larger than NHEFS, estimates can be made for blacks and the very old (85+). It is clear that educational differences in mortality among blacks are similar to those among whites. However, the lowest

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Demography of Aging Table 8-2 Ratio, Actual to Expected Deaths by Age and Education in the National Longitudinal Mortality Study   NOTE: Categories with 40 or fewer expected deaths have been combined with adjacent categories. SOURCE: Derived from Rogot et al. (1992a:Table 6). educational category (0-4 years of schooling) suffers less disadvantage among blacks, perhaps because it is a less precise marker of physical and mental handicaps in a population where restricted education is as much a product of social forces as of personal attributes. Likewise, blacks who attend college—a highly selective group—have relative mortality ratios lower than their white counterparts. Thus, the black data reveal a similar gradient (slope of the education/mortality relation) to that of whites, but one that

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Demography of Aging begins and ends at a lower level. Unfortunately, the manner in which source data are presented makes it impossible to compare directly the mortality rates of blacks and whites having the same educational attainment. Table 8-2 reveals that educational differences in mortality are virtually absent among the population aged 85 and older. This tendency is consistent with narrowing differentials with age revealed by NHEFS for younger ages. White females aged 85+ do show small but persistent differentials in the expected direction up to the level of college graduates. On the other hand, white males aged 65+ in 1960 also showed no educational differentials in mortality, but those differentials have emerged subsequently in this age span. It is intriguing that the "cohort" aged 65+ in 1960 is much the same as the cohort aged 85+ in the early 1980s, which suggests that cohort approaches to studying socioeconomic differences may have some merit. The most disturbing feature of Table 8-2 is that education differences in mortality are much smaller than in NHEFS. For example, in the NHEFS the ratio of death rates of white males aged 75-84 for those with less than 8 years of schooling to those with some college is 1.95 (Table 8-1), whereas in the NLMS the ratio of actual/expected death ratios for the two groups is only 1.17 (not shown). There is no apparent reason why these ratios, and those for other age-sex groups, would be so different. NHEFS observations are centered around 1978 and NLMS ratios around 1983, but it is surely unlikely that differentials would have widened and then contracted so dramatically. Since NLMS has roughly 70 times the number of person-years of exposure as NHEFS, it seems to provide a firmer foundation for assessing educational differentials. Evidence of the plausibility of NLMS educational differentials is their consistency with international patterns. Valkonen (1987) has provided a masterful review of socioeconomic differences in mortality in Europe. He assembles data from different countries on educational differentials in mortality for men and women age 35-54 during 1976-1980. These are also based on census samples followed forward into death records. We have plotted rates from NLMS for whites in the United States based on Valkonen's figures (Figures 8-2 and 8-3). Although the actual rates are not recoverable from NLMS publications, the ratio of deaths to expected deaths is a multiplicative transformation of the death rates themselves. Since the figures are on a log-linear scale, their slope is invariant to a multiplicative transformation. We have simply chosen a "level" for the U.S. ratios that presents them in a convenient plotting range.4 It is clear that U.S. patterns are congruent with those in Europe for both 4   In particular, we have multiplied the ratios of actual to expected deaths, assembled from Rogot et al. (1992a:Table 6), by a factor of 0.006 for males and 0.003 for females.

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Demography of Aging Figure 8-2 Age-standardized mortality (per 100,000) from all causes of death by years of education and country, males aged 35-54, 1976-1980, log scale. NOTE: U.S. data are for white persons during 1979-1986. SOURCES: Valkonen (1987); Rogot et al. (1992a). males and females. As in Europe, the educational differentials in the prime working ages are much sharper for males than for females (see Valkonen, 1989, for a more rigorous confirmation). An obvious explanation of this sex difference, which may not be correct, is that personal (and family) economic standing is more closely associated with men's than with women's education. It is also possible that health-related behaviors are more closely associated with men's than with women's education.

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Demography of Aging Figure 8-3 Age-standardized mortality (per 100,000) from all causes of death by years of education and country, females aged 35-54, 1976-1980, log scale. SOURCES: Valkonen (1987); Rogot et al. (1992a). Educational differences in mortality have tended to widen in Europe, as they have probably done in the United States as well (Valkonen, 1987, 1992). A widening of socioeconomic differences in England and Wales (measured principally by occupation rather than by income) has evoked a storm of controversy, in part because the National Health Service instituted after World War II was expected by some to mute class differences in

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Demography of Aging ences are unusually narrow in the Alameda County population. In fact, at ages 60+, 17-year survival is not significantly associated with family income (adjusted for family size; Kaplan et al., 1987). The persistent failure of intervening or proximate variables in existing studies to "account for" relations between class and health has led some analysts to postulate that there is some generalized factor that is primarily responsible for the observed differences (Cassel, 1976; Marmot et al., 1984). Marmot et al. suggest that dietary differences may be one such generalized factor. But factors related to diet such as serum cholesterol, blood pressure, and body mass index are clearly not principal channels connecting class and health. Access to, and quality of, medical care may be another such factor; surprisingly little research attempts to understand the contribution to class differences of variation in medical care. But the experimental studies described above, the widening of class differences in Britain after the National Health Service was introduced, and the widening of education differences above age 65 in the United States after Medicare was introduced all suggest that medical care does not hold the key to socioeconomic differences. So does the simple tabulation of the many chronic conditions that are more prevalent among lower-status groups (shown, for example, in Table 8-5). Clearly, the incidence of many diseases varies among groups, and by its nature, medical care has much less to offer in the way of prevention than of treatment. The search for one generalized factor to explain the bulk of class differences is likely to prove fruitless. More and better studies of specific factors would seem a more promising path, within a causal framework that recognizes that individuals are not mere slices of cross-sectional characteristics but have histories and motivations. SOURCES OF CHANGE IN SOCIOECONOMIC DIFFERENCES In most Western countries, differentials have widened in the past several decades, and some attention has been paid to the factors involved. The widening of class differentials in the United States and Britain during this period coincides with a widening of class differentials in cigarette smoking. Between 1974 and 1985, the prevalence of smoking in the United States declined five times faster among college graduates than among people with less than a high school education (Pierce et al., 1989). To our knowledge, no one has attempted an explicit quantitative assessment of the linkage between these trends. However, Marmot and McDowell (1986) demonstrate for Britain that between 1970-1972 and 1979-1983, mortality from smoking-related causes lung cancer and coronary heart disease rose among manual workers and fell among nonmanual workers. For all other causes, mortality rates fell by equal percentages in the two groups.

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Demography of Aging Winkleby et al. (1992) demonstrate that in the Stanford Five-City Project, improvements in levels of smoking, blood pressure, and serum cholesterol (men only) were actually greater among poorly educated people (less than high school) than among college graduates. This unusual pattern of change was thought to be partly a product of a comprehensive program of community organization and health education. However, similar changes were also observed among control cities. The interaction between time trends and education was not statistically significant. To investigate the role of health services in the widening class gap in England and Wales, Mackenbach et al. (1989) divide detailed causes of death into those that are amenable to medical intervention and those that are not. Although larger percentage declines for the amenable causes were observed for higher classes between 1931 and 1961, no such trend was evident between 1961 and 1981. Thus, it does not appear that differential access to medical care was a significant factor in widening class differentials during the later period. Income distributions have become more unequal both in the United States and in England and Wales during the past 15 years. In the United States, these disparities have resulted in higher "returns to education" (i.e., greater disparities in income between educational groups). Obviously, these changes could also be related to changes in educational differentials in health. No one has examined this issue in the United States, to our knowledge. In Britain, Wilkinson (1989) argues that, although trends in mean earnings by class cannot account for widening mortality differentials by occupational group, trends in class differences in poverty are plausibly implicated. RACE, CLASS, AND HEALTH Race is a dimension of social stratification in the United States that interacts with, but is separable from, education, income, and occupational class. For example, residential segregation between the races is markedly greater than segregation between even the most extreme income or occupational groups (White, 1987). Preston and Haines (1991) note that at the turn of the century, social class differences in American child mortality were very small, especially in comparison to England and Wales, but racial differentials were enormous. Black life expectancy has trailed white life expectancy throughout the twentieth century. Racial differences have narrowed since 1970 (Manton et al., 1987); they were 7.4 years in 1960 and 5.8 years in 1985. If white age and cause-specific death rates were substituted for the equivalent black rates, the largest reduction in black mortality would be produced by changes in heart disease for females and homicide for males (Manton et al., 1987).

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Demography of Aging Blacks have a higher prevalence of most chronic conditions and of disability in all age groups (Manton et al., 1987). Recorded black-white mortality differences contract sharply above age 65 and are eliminated or reversed above age 80. More studies have aimed at "explaining" black-white differences than at explaining other socioeconomic differences, and they have been far more successful. The principal reason is that the factors admitted into the explanation have included the other socioeconomic variables. In short, black-white differences in mortality and health status appear to be primarily a manifestation of racial inequality in education and income. Otten et al. (1990) use NHANES follow-up data on persons aged 35-54 at baseline. An initial black-white mortality ratio of 2.3 with no controls is reduced to 1.4 after controls are instituted for six risk factors (e.g., blood pressure, smoking) and family income. By far the largest reduction in relative risk occurs when family income is introduced into the hazard model. At ages 55-77, a slight black mortality disadvantage is converted into a slight black advantage when these same factors are controlled. Behrman et al. (1991) use longitudinal data from the Retirement History Survey to examine the degree to which black-white differences in male mortality are attributable to differences in income. This data source is especially useful in having measures of lifetime earnings, but is limited to persons who were heads of households at survey in 1969, when they were aged 58-63. In a later version of this analysis, Behrman et al. (1993) find that both Social Security and pension benefits have a highly significant effect on the mortality hazard rate. Using a regression-decomposition procedure, they find that differences in characteristics between blacks and whites account for 60-80 percent of the racial difference in hazard rates. Most of the contraction is attributable to differences in pension income, education, and marital status (Behrman et al., 1993:174) In a study that draws on the 1986 National Mortality Followback Survey for numerators and the 1986 National Health Interview Survey for denominators of adult death rates, Rogers (1992) finds an odds ratio of 1.48 between black and white mortality when only age and sex are controlled. Controlling marital status and family size alone reduces the odds ratio to 1.29. Controlling family income class alone reduces it to 1.17. Controlling both variables together reduces the odds ratio to 1.01. In a study using NLMS and confined to comparisons of age-adjusted death rates, Sorlie et al. (1992) show that controlling family income levels reduces the relative risk of death for black males aged 45-64 from 1.67 to 1.30 and for black females from 1.82 to 1.48. Racial differences were smaller above age 65 and not substantially modified by income controls. Keil et al. (1992a) examine black-white differences in mortality from all causes and from coronary heart disease among men recruited into the

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Demography of Aging Charleston Heart Survey in 1960. Educational level and occupational status at baseline served as socioeconomic control variables in a 28-year follow-up. Initially large black-white differences in all-cause and coronary heart disease mortality were reduced to insignificance when socioeconomic status was controlled. In a subsequent analysis of these data, Keil et al. (1992b) find that skin color within the black population is insignificantly related to mortality, although there is some tendency for lighter-skinned blacks to have lower survivorship. Mutchler and Burr (1991) examine racial differences in disability and other indices of health status using the 1984 Survey of Income and Program Participation. They find no significant racial differences once social and economic variables are controlled. The most influential of these variables are education and net worth. However, racial differences in self-assessed health status (proportion rating their own health as fair or poor) remained significant. Other studies have examined cancer incidence and survival differences between blacks and whites. Devesa and Diamond (1983) find lung cancer incidence rates to be higher for black men than for white men in the Third National Cancer Survey. However, when the median income and median educational level of one's census tract are introduced into a regression equation, racial differences become insignificant. Bassett and Krieger (1986) note that blacks have poorer survival rates from breast cancer than whites, controlling stage and histology. Racial differences in survival are also common at other sites. Using data from the Western Washington Cancer Surveillance System, the authors find that an initial racial difference of 35 percent is reduced to 10 percent when socioeconomic characteristics of one's census tract are controlled. Economic characteristics may affect cancer survival largely through the quality of care received; Page and Kuntz (1980) show that racial differences in survival are insignificant among patients treated in Veterans Administration hospitals for seven of the eight cancer sites investigated. Blendon et al. (1989) show that blacks are less likely to use physician services at a particular level of self-assessed morbidity as reported in a national telephone survey in 1986. They are also less likely to express satisfaction with the quality of care received. Use of a single dichotomous income variable, plus health insurance availability and demographic controls, reduces an initial difference of 26 percent in the mean number of annual ambulatory visits to physicians to a difference of 10 percent, which remains significant. Four factors are likely to be significant in the narrowing of adult racial differences in mortality during the past two decades, although there has been no explicit examination of this phenomenon. First, racial differences in cigarette smoking have contracted (Manton et al., 1987). Second, there has been a huge reduction in the prevalence of hypertension among blacks,

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Demography of Aging especially black males. Nevertheless, blacks have an approximately 30 percent higher incidence of hypertension even when obesity and diabetes are controlled statistically (Svetkey et al., 1993). Third, the introduction of Medicare and Medicaid has been associated with a change in patterns of physician visitation and hospitalization, with black increases exceeding white (Manton et al., 1987). Finally, racial differences in income and poverty rates have narrowed, although there is some ambiguity about trends in racial income disparities (National Research Council, 1990). These four sets of changes are clearly interrelated rather than independent. Navarro (1990) notes that the United States has an explicit policy goal of narrowing racial differences in mortality and health status, but, unlike most other industrialized countries, has no explicit goals regarding socioeconomic differences. In light of careful research that demonstrates income, education, and occupational disparities to be the principal source of racial differences in mortality and health status, the policy emphasis would appear to be misplaced. Now that reliable national data on socioeconomic differences have become available, there is less reason to continue using race as a proxy for class. SUMMARY Mortality rates and the prevalence of ill health are higher among groups of lower social standing in all contemporary Western countries, including the United States. In most countries where evidence is available, social disparities in mortality have widened during the past two decades, although inconsistencies among data sources in the United States make this conclusion uncertain. Heart disease is the principal cause of death responsible for social class differences in mortality from all causes combined. The principal approaches used to identify the sources of these differences are economic and social-psychological. Economic approaches have the virtue of conceptual clarity; social-psychological approaches have the advantage of focusing on variables that have substantial predictive ability. The former focuses on choice under constraints; the latter, on predispositions of unknown origin, stressors, and coping mechanisms. In neither case are individuals' personal histories well integrated into the analytic apparatus, which seems essential for a full appreciation of the sources of health differentials at any moment in time. A fruitful blending of the approaches seems possible in which the economist's focus on goods and services as the principal source of satisfaction is supplemented by attention to personal relationships as additional desiderata. Poverty and low status exact a health toll not only through absolute deprivation of material resources but also through interpersonal stresses and impaired relationships, some of which may reflect relative deprivation as much as absolute deprivation. These

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Demography of Aging influences cumulate over a lifetime. Research designs need to be expanded to capture the broad array of class-related phenomena affecting the health of older persons. Efforts to ascribe class differences in mortality or health status to various intervening biomedical variables such as smoking or elevated blood pressure have not been entirely successful. Although some reduction in class differences typically results from controlling these variables, the bulk of the differences remains. Whether this result reflects a deficiency in the array of variables considered, the activity of hitherto unidentified factors, or the futility of a strictly biomedical approach to studying a process with important cognitive, affective, and motivational elements, is not clear. In contrast, the bulk of black-white differences in mortality and health status are explicable in terms of the unequal distribution of the groups on variables such as education and income. REFERENCES Bassett, M.T., and N. Krieger 1986 Social class and black-white differences in breast cancer survival. American Journal of Public Health 76(12):1400-1403. Becker, G.S., M. Grossman, and K.M. Murphy 1990 An Empirical Analysis of Cigarette Addiction. National Bureau of Economic Research, NBER Working Paper No. 3322. Cambridge, Mass.: NBER. Behrman, J.R., Z. Hrubec, P. Taubman, and T.J. Wales 1980 Socioeconomic Success: A Study of the Effects of Genetic Endowments, Family Environment and Schooling. Amsterdam: North-Holland Publishing Company. Behrman, J., R. Sickles, P. Taubman, and A. Yazbeck 1991 Black-white mortality inequalities. Journal of Econometrics 50:183-204. Behrman, J.R., M.R. Rosenzweig, and P. Taubman 1992 Endowments and the Allocation of Schooling in the Family and in the Marriage Market. Unpublished manuscript, University of Pennsylvania, Philadelphia. Behrman, J.R., R. Sickles, and P. Taubman 1993 Some Causes and Consequences of Death. Unpublished manuscript, University of Pennsylvania, Philadelphia. Benzeval, M., K. Judge, and C. Smaje 1993 Beyond Class and Race: Measuring the Impact of Deprivation on Health and Health Care in Britain. Unpublished manuscript, King's Fund Institute, London . Berkman, S.F., and L. Breslow 1983 Health and Ways of Living. New York: Oxford University Press. Blaxter, M. 1989 A comparison of measures of inequality in morbidity. In J. Fox, ed., Health Inequalities in European Countries. Aldershot, England: Gower. Blendon, R.J., L. Aiken, H. Freeman, and C. Corey 1989 Access to medical care for black and white Americans. Journal of the American Medical Association 261(2):278-281. Brook, R.H., J.E. Ware, Jr., W.H. Rogers, E.B. Keeler, A.R. Davies, C.A. Donald, G.A. Goldberg, K.N. Lohr, P.C. Masthay, and J.P. Newhouse 1983 Does free care improve adults' health? Results from a randomized controlled trail. New England Journal of Medicine 309(23): 1426-1434.

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