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(Stroke. 1995;26:1759-1763.)
© 1995 American Heart Association, Inc.

Articles

Role of Social Class in Excess Black Stroke Mortality

George Howard, DrPH; Gregory B. Russell, MS; Roger Anderson, PhD; Gregory W. Evans, MA; Timothy Morgan, PhD; Virginia J. Howard, MSPH Gregory L. Burke, MD, MS

From the Departments of Public Health Sciences (G.H., G.B.R., R.A., G.W.E., T.M., G.L.B.) and Neurology (G.H., V.J.H., G.L.B.), Bowman Gray School of Medicine of Wake Forest University, Winston-Salem, NC.

Correspondence to George Howard, DrPH, Departments of Public Health Sciences and Neurology, Bowman Gray School of Medicine of Wake Forest University, Winston-Salem, NC 27157-1063. E-mail howard@phs.bgsm.wfu.edu.

	Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Background and Purpose It has been suggested that a substantial proportion of the excess stroke mortality among black Americans may be attributable to relatively lower socioeconomic status (SES) in this group. In this report we provide the first quantitative estimates of the proportion of excess black stroke mortality attributable to SES for a large population-based cohort.

Methods We used data from the National Longitudinal Mortality Study for persons 45 years and older (73 400 white men, 87 528 white women, 6522 black men, and 8816 black women). Sex-specific proportional hazards model were used to estimate excess black stroke mortality with and without adjustment for education and income (measures of SES). The contribution of SES to the excess black stroke risk was estimated from the difference in regression coefficients for race in these models.

Results In men, low SES was associated with increased stroke mortality (P.0001) and accounted for 14% to 46% of the excess black stroke risk (P<.05). However, we could find no association between SES and stroke mortality in women, and SES did not account for a significant proportion of the excess stroke mortality in black women.

Conclusions Although SES proved to account for a statistically significant proportion of excess male black stroke mortality, overall SES explained less than one quarter of the observed excess between ages 45 and 65. In women, SES did not significantly reduce the estimated excess black stroke mortality. Although SES may be playing a role in excess black stroke mortality, a substantial proportion of the excess appears attributable to other sources, including cerebrovascular risk factors that are unrelated to SES, unmeasured lifestyle influences, social resources, and genetic factors.

Key Words: blacks • lifestyle • mortality • racial differences • risk factors

	Introduction

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Stroke mortality in the US black population is generally estimated to be approximately 1.5 times that observed in the US white population.^{1 2 3 4 5 6 7} This marked racial difference in stroke deaths has led to a "call to action" by the past president of the American Heart Association.⁸ A more thorough understanding of the factors underlying this excess black stroke mortality will play a central role in guiding future research efforts to reduce the stroke burden borne among black Americans.

Observed ethnic differences in mortality have sometimes been largely attributed to differences in SES.⁹ Stroke mortality has been related to income, educational level, and other measures of SES.^{10 11} Because of clear SES differences between US racial groups, SES may be a confounder in the assessment of racial differences in stroke.

Black excess stroke mortality is not consistent across age. Black/white excess stroke mortality decreases from approximately 4:1 at age 45 to roughly 1:1 at age 85.^{1 7} The extent to which SES contributes to racial differences in stroke mortality may differ across the age spectrum (perhaps as a result of survivorship bias).

Stroke mortality is too low to allow for a statistically powerful assessment of risk factors (such as SES) in most prospective cohort studies. The availability of data from the NLMS, based on the CPS, offers a unique opportunity to investigate the impact of SES on stroke mortality in a sample of more than 175 000 black and white adults aged 45 years and older.

	Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Details of the public use version of the NLMS data set and its use in analysis of stroke mortality are provided elsewhere.¹ Briefly, this NLMS version contains records for 630 000 persons from 10 CPS¹² from the Census Bureau whose data were linked with the National Death Index¹³ from 1979 through 1985. No personal identifiers (respondent's name, geographic residence, or CPS cohort) were included in the public use data to fully ensure the confidentiality of census data. The underlying causes of all cohort deaths during a 5-year follow-up period were centrally coded (ICD-9). Race was categorized by response to the CPS question, "What is the race of each person in this household?" Only data from blacks and whites were used in this analysis. To better represent the broad nature and influences of social class, we included the respondent's education and household income in each analysis. Education was evaluated in three categories: less than high school, high school graduate, and some college or more. Household income was categorized as less than $10 000, $10 000 to $19 999, and $20 000 or more. Deaths with ICD-9 codes of 430 to 438 (cerebrovascular disease) were considered stroke deaths, while deaths from other causes were considered censored. These analyses are restricted to the 73 400 white men, 87 528 white women, 6522 black men, and 8816 black women who were aged 45 years or older at the time of the baseline interview. In this sample, there were 578 stroke deaths among white men, 744 stroke deaths among white women, 77 stroke deaths among black men, and 77 stroke deaths among black women.

Sex-specific proportional hazards analyses were used to estimate the excess black stroke mortality and the impact of age on this excess. Two alternative models were considered:

and

where h(t) is the hazard (conditioned on the levels of the covariates), h₀(t) the underlying hazard, and ß the proportional hazard regression coefficients, A the age, R the indicator variable for race (0=white, 1=black), AR the age-by-race interaction, I the indicator variables (2) for income, E the indicator variables (2) for education, and IE the indicator variables (4) for income-by-education interaction. The HR is used as a measure of the stroke mortality excess risk among blacks, and at any age x is equal to:

Of prime interest in this report is the difference in the age-specific estimate of excess black risk in these two models, (ß₁+ß₃x)-(ß*₁+ß*₃x), which is a measure of the decrease in the excess stroke risk associated with adjustment for SES. Note that if this difference is zero, the HR for race is the same unadjusted for SES as after adjustment for SES. The proportionate reduction in the difference, times 100, may be interpreted as the percentage of the excess stroke mortality attributed to race that is due to SES (as measured by the categories of income and education). The statistical significance of the reduction in stroke mortality risk due to adjusting for SES was estimated by bootstrapping¹⁴ the estimated difference with 500 repetitions. Estimates of excess risk were provided at 10-year intervals from age 45 to 75 years.

	Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
The percentage of the race-sex strata (white men, white women, black men, black women) in each of the nine income-education strata is shown in Table 1. Differences by race and sex were dramatic; for example, the lowest SES category (less than high school graduate with a household income below $10 000) contained 49% of black women but only 18% of white men. Blacks and women had lower income than their white or male counterparts. Whites and men also tended to have higher educational levels, although sex differences in educational levels were smaller (particularly for blacks).

View this table: [in this window] [in a new window]   Table 1. Percentage of Subjects by Income and Education Strata (and Marginals) for Each Race-Sex Group

As anticipated, there was a highly significant (P.0001 for all) impact of age, race, and age-by-race interaction on the risk of stroke for both men and women. The race and race-by-age interaction coefficients were used to estimate the "unadjusted" values for SES estimates of the impact of race on stroke risk as a function of age shown in Table 2. The decline in the HR across age shows the greater black/white excess in stroke mortality observed in younger age groups in both men and women.

View this table: [in this window] [in a new window]   Table 2. Estimated Impact of Adjustment for SES on the Estimated Excess Stroke Risk

The estimated impact of income and education on risk of stroke death differed greatly for men and women. In the left panel of the Figure, the estimated impact of the SES variables is shown for men (after adjustment for age, race, and the age-by-race interaction). There was a clear difference in the estimated HR (P.0001), with those individuals with household incomes greater than or equal to $20 000 and a high school or greater education having lower stroke risk than those with either income less than $20 000 or less than a high school education. Lower income or education was associated with at least a doubling of the estimated HR. For women, however, SES had little impact on the hazard of death from stroke (P=.67) (Figure, right panel).

View larger version (32K): [in this window] [in a new window]   Figure 1. Bar graphs show estimated HR for income and education strata after adjustment for age, race, and the age-by-race interaction. Estimates are provided for men (left) and women (right) in separate models. Lt and LT indicate less than; HS, high school.

The estimated impact of race as a function of age after adjustment for the SES variables is also shown in Table 2. For men, there was a significant reduction in the risk of stroke (P.05) in those older than the group aged 45 to 75 years. While the absolute magnitude of this decrease was smaller at older ages, when expressed as a percentage the proportion of the excess black stroke risk explained by SES adjustment increased marginally with age. For women, the SES adjustment played a minor role in reducing the estimated black stroke risk (P>.05 for all ages).

	Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
SES adjustment reduced excess black stroke mortality for men but not for women. Unadjusted for SES, the risk in black men was 4.76 times greater than whites at age 45 and 1.97 times greater at age 65, a decreasing pattern of excess black stroke risk that has been previously described.^{1 7} After SES adjustment, the black HR was reduced to 3.84 (a 14% decrease) at age 45 years and to 1.70 (a 22% reduction in the excess risk) at age 65 years. While the absolute magnitude of black excess risk decreases with age, for men the proportion of the excess due to SES is greater at older ages.

The extent that SES factors would be expected to have an impact on stroke mortality is speculative. Racial differences have been reported in cerebrovascular atherosclerosis that are at least partially explained by traditional cerebrovascular risk factors.^{15 16} These risk factors are inversely associated with income in the general population.^{17 18 19 20 21 22 23} Thus, the lower SES of blacks compared with whites is likely associated with an increase in the prevalence of major cerebrovascular risk factors and likely associated with increased stroke incidence and severity. SES may also be associated with stroke mortality through early detection and treatment of cerebrovascular disease.

Understanding the role of SES in the racial excess of stroke mortality by the use of proportional hazards modeling is complicated by a known statistical bias in the estimation of risk when an "important" covariate (such as SES) is omitted from models.^{24 25} Omitting SES while just including race underestimates the overall effect of race in nonlinear models, such as the proportional hazards model frequently (and here) used in survival analysis. Using methods described by Morgan,²⁴ we directly estimated this effect in these data and adjusted for the deflation that occurs in the proportional hazards model with omitted covariates (the SES factors). This analysis (not shown) showed that this statistical bias does not have a sizable effect on the estimates or their interpretation in these data. For example, for men at age 65 this adjustment to correct the bias in the model without SES factors would change the estimated 21% deflation in risk attributable to SES shown in Table 2 to an estimated 26% deflation in risk, adjusting for the known statistical bias. Thus, this bias does not substantially affect the interpretation of these analyses.

The results indicate that for subjects aged 45 years there is a threefold to fourfold excess stroke mortality for blacks, and no more than 14% of this excess risk can be attributed to SES effects. This indicates that there are one or more very strong risk factors associated with race other than SES. In considering the strength of risk factors and their relative importance in older populations, it is important to consider the "harvest" effect or the "living well" effect. As older populations are observed, subjects with very abnormal risk profiles are more likely to have died and therefore to not be included in the population at older ages. Given that the non-SES risk factors have such a strong predicted effect at age 45 years, it is likely that there is a survival selection effect that omits those at highest risk by age 75 years. This may account for the fact that the "relative" effect on the excess risk of SES increases from 14% at age 45 years to 46% at age 75 years for men. While a similar trend is observed in women, the degree of survival selection is much less for women younger than 75 years.

We hypothesized that SES would play a large role in excess black stroke mortality by its association with obesity, diabetes, inadequately controlled hypertension, dyslipidemia, and stress through lifestyle and economic influences. The relatively small estimated magnitude of the SES effect may be the result of insensitivity of income and education to disparity in lifestyles between either SES groups or ethnic groups.²⁶ Stroke is a chronic disease, reflecting long-term exposures to cerebrovascular disease risk factors. Traditional indicators of SES assessed at baseline may not be sensitive to the individual's past financial prosperity. On the other hand, educational attainment is generally stable in adults aged 45 years and older. Also, availability of health insurance may modify or buffer the effects of low income or education on health risk behaviors by facilitating primary and secondary prevention. This could be especially relevant among persons with lower SES, who may have access to government-sponsored healthcare options. Unfortunately, there are no measures of health insurance in the public use NLMS data set. Finally, it is possible that the cut points used for income and education to identify the lowest category were unable to adequately distinguish potential differences between indigent and low-income wage earners or between middle-income and upper-income wage earners. We can only hypothesize that an improved measure of SES would show stronger associations of SES and stroke mortality in men and women.

The relationship of SES and stroke mortality may be different depending on sex. Risks associated with a certain level of income or educational attainment in the general population may be modified by unmeasured factors among blacks and women. For example, among women in our sample aged 45 to 65 years, SES adjustment was associated with only a 3% to 5% reduction in the black excess stroke risk and was not statistically significant (P>.05). A weakened or attenuated "dose-response" relationship with SES has been previously reported for women compared with their male counterparts for all-cause mortality.^{27 28 29} Franks et al³⁰ investigated stroke mortality in 32 London boroughs. He found a significant relationship between SES and stroke mortality in men but no relationship in women. Casper et al³¹ related stroke mortality to the proportion of white-collar workers in state economic areas (collections of a few counties). They found that while for men black excess stroke mortality decreased with the proportion of the population employed in white-collar occupations, for women an increased proportion employed in white-collar jobs had a negligible effect on black excess mortality. However, Araki et al³² found a consistent relationship between income measures and stroke mortality for women but not men in the 46 prefectures of Japan. Wong and Donnan³³ failed to find a significant relationship in either men or women in the 24 districts of Hong Kong. Other authors did not provide sex-specific estimates but did find overall relationships between SES and stroke mortality.^{10 34 35}

A number of explanations have been postulated for the lack of an effect of SES on mortality risk for women. It has been speculated that the more extensive social integration that exists among women,³⁶ differences in response to stress, and a heightened illness prevention orientation³⁷ may protect women from disease and its complications compared with men. However, the literature on social support and mortality in women as reviewed by Shumaker and Hill³⁸ in 1992 is equivocal, with some studies finding greater mortality risk among women with increased social integration and some showing increased mortality with fewer contacts. It is also possible that the association of SES with risk factor status may also differ by sex. For example, the association of SES with coronary heart disease risk factors such as smoking³⁹ and negative life events⁴⁰ is weaker in women than in men. Women may also have other biological factors (such as endogenous estrogen production before menopause) that may afford protection against deleterious effects from cerebrovascular disease risk factors associated with low SES.

In conclusion, SES explained less than 25% of excess stroke mortality among those participants aged 45 to 65 years (in whom the biggest racial stroke mortality excess exists) in men and had a very small impact in women. Therefore, SES factors, as reflected by income and education, explain little of the black excess stroke mortality for women and only 20% of the stroke mortality in men. This seems to argue that while SES is important in stroke mortality risk, it is but one component of the many factors that likely explain the huge excess black stroke mortality. Cerebrovascular risk factors that are unrelated to SES, such as other lifestyle factors, social resources, and genetic factors, are the most likely culprits responsible for the SES-adjusted excess black stroke mortality.

	Selected Abbreviations and Acronyms

 

CPS = Current Population Survey HR = hazard ratio ICD-9 = International Classification of Diseases, 9th Revision NLMS = National Longitudinal Mortality Study SES = socioeconomic status

	Acknowledgments

 
This study was supported by National Institutes of Health, National Heart, Lung, and Blood Institute grant 53339.

Received May 18, 1995; revision received July 10, 1995; accepted July 12, 1995.

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