Racial and Ethnic Differences in the Health of Older Americans (1997) / Chapter Skim
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4 Race, Socioeconomic Status, and Health in Late Life
4 Race, Socioeconomic Status, and Health in Late Life
Pages 106-162
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From page 106... ...
In response to the growth of other racial and ethnic groups, comparisons have been broadened in recent years to include a growing literature addressing the relative health status of Hispanic, Asian, and Native American populations, but the literature remains dominated by black-white comparisons. In the last 20 years, scientific inquiry has shifted from describing gross health disparities between the races to explaining the underlying factors that account for these differences.
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From page 107... ...
The sixth and major section of the paper summarizes a series of empirical models of selfassessed health status. In particular, these models focus on understanding the reasons underlying the strong correlation between income and health and on the implications of that correlation for racial and ethnic health disparities.
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From page 108... ...
To put it simply, individuals in better health may be more able to translate other inputs into more productive health investments. Therefore, today's investments are influ enced by today's health status and produce tomorrow's health status.
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From page 109... ...
A corollary implication is that additional current economic resources are unlikely to have a quantitatively large impact on the current stock of health, especially in the age groups that are the focus of this research. Additional economic resources may increase health care utilization or induce good health behaviors, but these sorts of behavioral changes may be slow to be adopted.
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From page 110... ...
Another relationship that flows from this approach is a series of derived demand functions for each input into the health production function. These input demand functions have as arguments all the input prices and the underlying determinants of the level of health demand, including household income and tastes.
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From page 111... ...
(6) Equation 6 expresses current health as a function of all input prices and total household income.
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From page 112... ...
To use one illustration, a lower price of health care can directly affect health status through increased utilization of health care. Because health status is altered, there may also be subsequent alterations in household income.
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From page 113... ...
Our research strategy begins with a separation of household income into its important components. We argue a priori that some of these income components largely reflect causation from health to income.
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From page 114... ...
Education Education is an important explanatory variable in both economic and sociological-based empirical models of the socioeconomic status-health relationship. As is demonstrated in the theoretical model outlined above, education may affect health status through a number of channels.
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From page 115... ...
It may be especially important in assessing the comparative health status of currently older blacks versus whites because of the large changes in the relative income of blacks versus whites that have occurred over their lifetimes. Second, income may not be the best measure of economic resources among older individuals, especially those who are retired.
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From page 116... ...
Thus, for studies of racial differences in the relationship between health and occupation in late life, it is especially unclear which occupation is most appropriate. Occupational categories may also present added problems in the context of the socioeconomic statusrace-health analysis because there may be differences between races in exposures and treatment of persons in the same occupational category.
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From page 117... ...
For example, race may affect the health production function per se independently of any racerelated genetic or biological predispositions to certain diseases. Studies have described subtle racial differences in the effectiveness of certain drugs in the treatment of hypertension, possibly related to racial differences in the pathophysiology of hypertension (see review in Kaplan, 1994~.
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From page 118... ...
During the life course of the current population of older African Americans, there have been dramatic changes in the opportunities for and the treatment of African Americans in this society. These changes have produced potentially important cohort effects that may confound other factors especially age important to understanding racial differences in health.
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From page 119... ...
RACE, SOCIOECONOMIC STATUS, AND HEALTH STATUS: EXISTING EMPIRICAL EVIDENCE In this section, we briefly review key studies that have specifically addressed the role of socioeconomic status in explaining racial differences in health, with a focus on health in late life. Any review of this issue is complicated because health status is multifaceted.
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From page 120... ...
person-years at baseline stratified Rogers (1992) Merged Total Age, gender, 100% NHIS 1986; mortality marital status, N= 37,917 1986; family size, and NMFS logistic family income 1986; regression (categories)
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From page 121... ...
hazard at baseline aUnable to determine percentage by race in sample Note: PHSE = Piedmont Health Survey of Elderly; NLSMM = National Longitudinal Survey of Mature Men; CHS = Charleston Heart Study; NHIS = National Health Interview Survey; NMFS = National Mortality Followback Survey; NLMS = National Longitudinal Mortality Study; SSI = Supplemental Security Income; RHS = Retirement History Survey; PSID = Panel Study of Income Dynamics; NHANES I = National Health and Nutrition Examination Survey I; BP = Blood Pressure; BMI = Body Mass Index; DM = diabetes mellitus; HPL = Human Population Laboratory; SES = socioeconomic status. urbanicity (Menchik, 1993~.
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From page 122... ...
However, this diversity in study populations and explanatory variables did not produce great differences in the primary finding across studies confirming the prominent role of socioeconomic status in explaining racial differences in total mortality. General Health Status, Functional Status, and Morbidity Mortality is at best a crude indicator of the health status of a population because it fails to capture the overall burden of poor health.
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From page 123... ...
This research also exhibited diversity along other dimensions, including the measurement of financial resources, the age groups considered, and geographic scope. In general, the literature suggests that a significant amount, but definitely not all, of racial differences in health status are attributable to differences in socioeconomic status.l3 13Unlike the mortality studies, few of these studies were presented in such a way as to allow easy estimation of the amount of the variation that was accounted for by socioeconomic status when a race residual effect remained.
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From page 124... ...
logistic and married) , and less for Tobit regressions net worth general health (logged)
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From page 125... ...
Satariano (1986) , however, found that racial differences in the health status of a sample from Alameda County, California, above age 20 were entirely explained by age, occupation, sex, education, and family income.
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From page 126... ...
To start this comparison, Table 4-3 displays selfreported health status by gender and race. Not surprisingly, people self-rate themselves in better health in the younger HRS sample.
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From page 127... ...
blacks had a higher rate of arthritis, while in HRS, the rate was higher only for black women.~4 There were minimal racial differences in rates of several conditions for both surveys including emotional/psychiatric problems and angina. These relative prevalence rates are influenced by several factors that influence the interpretation of racial differences.
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From page 128... ...
Although biological and genetic factors may have a part, socioeconomic status may also play a role in accounting for racial differences in many of these conditions. For example, stress has long been raised as a potential reason for racial differences in hypertension rates (James et al., 1987~.
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From page 129... ...
Confirming a number of prior studies, this relationship is largely uniform and quantitatively strong. As a gauge of the quantitative importance of this relationship, Table 4-7 arrays median household net worthi6 by self-reported health status in both the HRS and the AHEAD samples.
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From page 130... ...
for 1994. TABLE 4-7 Median Net Worth by Self-Reported Health Status Men Women Self-Reported White Black WhiteBlack Health Status ($)
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From page 131... ...
The relationship is monotonic and quantitatively large each step down in current health status significantly reduces net worth. In addition, current HRS and AHEAD assets are correlated both with current health levels and with changes in health.
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From page 132... ...
data for 1993 and data from Assets and Health Dynamics of the Oldest Old (AHEAD)
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From page 133... ...
SMITH AND RAYNARD S KINGTON TABLE 4-9 Mean and Median HRS Net Worth by Health Status of Spouses Health of Spouse 133 Health of Financial Respondent Excellent ($)
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From page 134... ...
Women in their fifties smoke less than similarly aged men, but there are trivial racial differences in current or past cigarette smoking or in its intensity. Although smoking is much less common among all groups in the AHEAD sample (ages 70 and above)
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From page 135... ...
SMITH AND RAYNARD S KINGTON TABLE 4-10 Risk Behaviors and Factors, by Race and Sex, HRS Sample (Ages 51-61)
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From page 136... ...
In the HRS sample, 56 percent of black women are nondrinkers, 12 percentage points higher than their white counterparts. This racial discrepancy is even larger among older women, where almost 4 in 5 black women are teetotalers compared with 3 in 10 older white women.
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From page 137... ...
While a number of salient health measures are available in these surveys, only a respondent's self-assessed health status is used as an outcome. Since self-assessed health is a ranked categorical response, ordered probit models are used in estimation.
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From page 138... ...
138 RACE, SOCIOECONOMIC STATUS, AND HEALTH TABLE 4-12 Ordered Probit Analysis of Self-Assessed Health Status, HRS Sample (Ages 51-61) Co-variate Parameter z Parameter z Parameter z Parameter z Black -.5278 17.60 -.2977 9.49 -.2402 7.50 -.1763 5.43 Hispanic -.5037 12.90 -.1774 4.11 -.1626 3.94-.0810 1.94 Female .0700 3.67 .0770 4.11 .0489 2.20 .0527 2.39 Marital status Never married .0739 1.18 .0140 0.22 .1577 2.46 Separated -.0736 1.03 -.1478 2.06 .0419 0.57 Divorced -.0043 0.11 .0304 0.75 .1007 2.41 Widowed .0375 0.75 .0483 0.96 .1731 3.37 Education Individual education 12-15 years .4327 17.50 .3240 12.92 .2568 10.04 16 or more years .6639 19.01 .4609 12.83 .3880 10.72 Advanced degree .2106 1.45 .1726 1.18 .1752 1.19 Spousal education 12-15 years .1332 5.02 .0828 3.09 .0378 1.39 16 or more years .1738 5.15 .0983 2.59 .0739 1.93 Advanced degree -.1280 0.78 -.1848 1.11 -.1666 1.00 Income and wealtha Total income .0034 11.30 .0025 8.34 Income 1 st tercile .0153 7.46 Income 2nd tercile .0046 3.41 Income 3rd tercile .0009 2.64 Total wealth .0003 8.83 .0002 5.80 Wealth 1st tercile .0059 7.42 Wealth 2nd tercile .0009 3.07 Wealth 3rd tercile .0001 2.73 Cohort 1935-1937 .1235 4.67 .1217 4.56 .1300 4.86 1938+ .2166 9.63 .2128 9.26 .2289 9.87 Risk factors Smoking Current smoker Cigarettes smoked per day -.0903 2.39-.0770 2.04 -.0034 2.31 -.0027 1.85
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From page 139... ...
The advantage women hold over men is considerably smaller in either survey. The third and fourth columns of Tables 4-12 and 4-13 extend this model by adding a standard list of demographic and economic co-variates; current marital status, education, household income and wealth, birth cohort (or, equivalently, age)
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From page 140... ...
140 RACE, SOCIOECONOMIC STATUS, AND HEALTH TABLE 4-13 Ordered Probit Analysis of Self-Assessed Health Status, AHEAD Sample (Ages 70 and Over) Co-variate Parameter z Parameter z Parameter z Parameter z Black -.4005 9.91 -.1633 3.83 -.1169 2.71 -.0560 1.28 Hispanic -.4038 6.76 -.1642 2.65 -.1313 2.11 -.0304 0.48 Female -.0159 0.64 .0592 2.20 .0772 2.65 .0907 3.10 Marital status Never married .1259 1.67 .1007 1.33 .2288 2.96 Separated or .0524 0.84 .0665 1.06 .2078 3.22 divorced Widowed .1090 3.17 .1004 2.91 .2137 5.78 Education Individual education 12-15 years .3011 10.62 .2627 9.18 .1975 6.70 16 or more years .4876 10.83 .4212 9.26 .3339 7.18 Advanced degree .1081 0.88 .1225 1.00 .1310 1.07 Spousal education 12-15 years .1140 3.07 .0882 2.37 .0654 1.73 16 or more years .1979 3.48 .1406 2.46 .1347 2.34 Advanced degree -.0268 0.18 .0052 0.03 .0027 0.18 Income and wealtha Total income .0008 2.63 .0009 2.85 Income 1st tercile .0126 1.83 Income 2nd tercile .0023 5.74 Income 3rd tercile .0005 1.40 Total wealth .0006 8.32 .0005 7.05 Wealth 1 st tercile .0050 5.12 Wealth 2nd tercile .0005 1.20 Wealth 3rd tercile .0002 2.40 Cohort 1919-1923 .2053 5.11 .2001 4.84 .1402 3.35 1914-1918 .0490 1.18 .0501 1.19 -.0071 0.17 1909-1913 .0098 0.23 .0109 0.25 -.0226 0.52 Living in a standard metropolitan statistical area -.0582 1.97 -.0782 2.64 -.0769 2.58 Region Northeast -.0613 1.51 -.0759 1.86 -.0594 1.46 North Central -.0080 0.22 -.0062 0.17 -.0176 0.48 South -.1274 3.52 -.0852 2.34 -.0776 2.12
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From page 141... ...
At this stage of the analysis, variation in current marital status does not significantly influence current health outcomes.l9 Older residents of standard metropolitan statistical areas and the South have somewhat lower health status than those who live elsewhere. Three dimensions of economic resources are incorporated into the baseline models in Tables 4-12 and 4-13: schooling, total household income, and household net worth.20 Education is commonly thought to affect health status through a number of channels.
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From page 142... ...
Both total household income and wealth are associated with higher health status. In the HRS sample, a dollar of wealth has about one-tenth the effect of a dollar of household income.
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From page 143... ...
Higher regimens of light and heavy physical exercise in HRS are correlated with better current health status in a remarkably well-ordered way. Even though these are self-reported episodes and durations, this analysis provides statistically significant evidence that exposure to workrelated health risks as well as the duration of that exposure is associated with lower self-reported health.
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From page 144... ...
There is no evidence that any of the spousal risk factors have any association with the respondent's self-assessed health status. The last two columns of Tables 4-12 and 4-13 test for nonlinearity in income and wealth effects on probability scores by including linearly splined terciles of total household income and wealth.
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From page 145... ...
It is often the only option available because health surveys typically expend little survey time attempting to measure household resources. If we place a high priority on understanding why socioeconomic status has such a quantitatively strong association with a variety of health outcomes, reliance on such a simple summary statistic as total household income is surely a mistake.
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From page 146... ...
Individual Earnings .0073 4.90 .0002 0.14 Social Security income .0083 1.97 .0096 2.01 Retirement income .0031 1.61 .0024 1.08 Salary = 0 -.3820 8.66 Social Security = 0 .1088 1.62 Retirement income = 0 -.0391 1.28 Spouse Earnings .0037 2.27 .0038 1.95 Social Security income .0046 0.90 .0078 1.29 Retirement income .0044 1.96 .0042 1.34 Salary = 0 .0219 0.40 Social Security = 0 .1121 1.38 Retirement income = 0 -.0306 0.75
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From page 147... ...
The most intriguing results occur with retirement income, which has a negative association with health status in the HRS but a strongly positive one in AHEAD. We interpret the negative coefficient in HRS as capturing a causal mechanism from poor health to early retirement.
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From page 148... ...
Once again, the more plausible causality runs from poor health to nonwork to non-income. Controlling for positive earnings reduces the positive effect of a person' s own earnings on current health status by a third.
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From page 149... ...
The size of this bias stemming from reverse causation may not be trivial. For example, after the receipt and size of the components of household income are controlled for, the estimated effect in HRS of a person's own earnings on current health status is half as large as the estimated effect of total household income on his or her own health.
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From page 150... ...
In Table 4-16, ordered probit models are summarized where the outcomes range from a great improvement in health over the last year to a great deterioration.26 In addition to variables measuring previous year's health status, the other co-variates parallel those included in the cross-sectional models with total household income and total household wealth as our measure of economic resources.27 Table 4-17 contains change models with a decomposition of household income similar to what has been presented in Tables 4-14 and 4-15. One advantage of this specification is that the analytical spotlight focuses exclusively on changes in health, conditional on the stock of health in the last period.
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From page 151... ...
Long-Run Measures of Economic Resources The results thus far demonstrate that regressing current health status on current economic resources hopelessly confuses cause and effect for working-age samples. The short-run reverse causality from health to socioeconomic status is simply too strong.
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From page 152... ...
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From page 153... ...
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From page 154... ...
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From page 155... ...
Individual Earnings .0018 4.06.0008 1.58 Weekly wages .0084 0.86 Retirement income -.0011 0.68.0085 3.84.0079 3.56 Welfare income -.0187 2.49-.0009 0.09.0029 0.27 Earnings = 0 -.1528 5.35 Weekly wages = 0 -.1584 3.94 Retirement income = 0 .1723 4.23.1308 3.20 Welfare income = 0 .1048 1.88.0620 1.11 Spouse Earnings .0021 4.16.0008 1.40 Weekly wages -.0134 1.34 Retirement income -.0007 0.36.0062 2.63.0089 3.77 Welfare income -.0271 3.27-.0022 1.94-.0018 1.57 Earnings = 0 -.1675 5.36 Weekly wages = 0 .1190 2.89 Retirement income = 0 .0704 1.60.0866 1.96 Welfare income = 0 .0224 0.35-.0021 0.03 Asset income -.0001 0.19.0012 1.93.0007 1.07 Asset income = 0 .0502 2.12.0404 1.70 Wealth .0001 1.63.0001 2.42.0001 2.86 Black -.0027 0.07-.0127 -0.35.0111 0.30 Hispanic -.0045 0.10-.0061 -0.13.0043 0.09 Female .0643 2.36.0679 2.47.0917 3.42 AHEAD Sample (Ages 70 and Over) Individual Earnings Social Security Other retirement income Earnings = 0 Social Security = 0 Other retirement income = 0 .0049 3.01 .0001 0.04 .0042 0.89 .0065 1.21 .0020 0.91 .0014 -.2675 .1166 -.0280 0.55 5.37 1.54 0.81 Spouse Earnings -.0001 0.06 -.0009 0.42 Social Security .0042 0.72 .0096 1.43 Retirement income .0043 1.73 .0028 1.00 Earnings = 0 -.0236 0.39 Social Security = 0 .1917 2.09 Retirement income = 0 -.0491 1.07 Continued on following page
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From page 156... ...
The advantage of the second measure is that it predates HRS health measurement by at least 10 years, effectively eliminating short-run reverse causality from health to economic status. The empirical estimates summarized in Table 4-18 do suggest that long-term wealth as measured by Social Security earnings affects health trajectories of mature men and women.
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From page 157... ...
racial and ethnic differences, our results indicate that the relationship among race and ethnicity, socioeconomic status, and health is far more complex than many current analyses recognize. We focus attention on the complexity involved in accounting for economic status as an underlying factor in health status.
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From page 158... ...
In particular, the entire association between current household income and health among households with a member in his or her fifties appears to reflect causation from health to income rather than from income to health. As new longitudinal data sets with more detailed and varied measures of economic status and health status become available, future research should progress toward a more complete understanding of the pathways linking race and ethn~city, socioeconomic status, and health across the lifespan.
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From page 159... ...
Shapiro, and B.K. Edwards 1994 Racial differences in survival from breast cancer: Results of the National Cancer Institute black/white cancer survival study.
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From page 160... ...
Mutchler, J.E., and J.A. Burr 1991 Racial differences in health and care service utilization in later life: The effect of socioeconomic status.
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From page 161... ...
1994 Socioeconomic differences in adult mortality and health status. In Demography of Aging, L.G.
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From page 162... ...
Smith 1994 The effects of occupational class transitions on hypertension: Racial disparities among working-age men. American Journal of Public Health 84:945-950.
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