Racial Disparities in Stroke Risk Factors
The Impact of Socioeconomic Status
Background and Purpose— In the US, blacks have a higher incidence of stroke and more severe strokes than whites. Our objective was to determine if differences in income, education, and insurance, as well as differences in the prevalence of stroke risk factors, accounted for the association between ethnicity and stroke.
Methods— We used data from the Third National Health and Nutrition Survey (NHANES III), a cross-sectional sample of the noninstitutionalized US population (1988–1994), and included blacks and whites aged 40 years or older with a self-reported stroke history. Income was assessed using a ratio of income to US Census Bureau annual poverty threshold.
Results— Among 11 163 participants, 2752 (25%) were black and 619 (6%) had a stroke history (blacks: 160/2752 [6%]; whites: 459/8411 [6%]; P=0.48). Blacks had a higher prevalence of 5 risk factors independently associated with stroke: hypertension, treated diabetes, claudication, higher C-reactive protein, and inactivity; whites had a higher prevalence of 3 risk factors: older age, myocardial infarction, and lower high-density lipoprotein cholesterol. Ethnicity was independently associated with stroke after adjusting for the 8 risk factors (adjusted odds ratio, 1.32; 95% CI, 1.04 to 1.67). Ethnicity was not independently associated with stroke after adjustment for income and income was independently associated with stroke (adjusted odds ratios for: ethnicity, 1.15; 95% CI, 0.88 to 1.49; income, 0.89; 95% CI, 0.82 to 0.95). Adjustment for neither education nor insurance altered the ethnicity–stroke association.
Conclusions— In this study of community-dwelling stroke survivors, ethnic differences exist in the prevalence of stroke risk factors and income may explain the association between ethnicity and stroke.
In the US, blacks have a higher incidence of stroke, more severe strokes, and higher stroke mortality than whites.1 These ethnic disparities have been attributed to higher prevalence or severity of stroke risk factors in blacks, biological differences between blacks and whites, and lower socioeconomic status in blacks compared with whites.2–4⇓⇓
Socioeconomic status may contribute to ethnic disparities in stroke incidence and outcomes. Specifically, decreasing socioeconomic status is associated with increasing stroke incidence and stroke mortality.5 In the US, blacks have lower average socioeconomic status compared with whites. For example, the median 1999 family income for whites was $53 356 versus $33 255 for blacks.6 Similarly, 9.1% of white families lived below the poverty level in 1999, compared with 24.9% of black families.6
Previous studies have compared ethnic differences in stroke incidence or stroke mortality and have found that income explained a portion of the observed ethnic differences.7–9⇓⇓ However, these studies were limited by not accounting for differences for known stroke risk factors.
The objective of the current study was to determine if differences in income, education, and insurance, as well as differences in the prevalence of stroke risk factors, accounted for the association between ethnicity and stroke.
Materials and Methods
We used data from the Third National Health and Nutrition Survey (NHANES III), conducted by the Centers for Disease Control and Prevention (1988 to 1994).10 This survey obtained historical, physical examination, and laboratory data on a nationally representative sample of the noninstitutionalized US civilian population with over-sampling of minorities.10 We included white and black participants aged 40 years and older.
NHANES participants were asked, “Has a doctor ever told you that you had a stroke?” This self-reported stroke history was the dependent variable for the current study. Self-reported ethnicity was categorized as white, black, and other.
Income was assessed using the income to poverty threshold ratio (IPTR). The numerator of the IPTR is the midpoint of the self-reported family income category and the denominator is the US Census Bureau poverty threshold for the year in which the participant was enrolled in NHANES.11 Participants were asked to sum the income of all family members (eg, earnings, unemployment compensation, Social Security).11 Each participant was assigned a poverty threshold, which varies with the number and ages of family members.11 Poverty thresholds are revised yearly to account for inflation; therefore, poverty threshold values adjust for changes in inflation between calendar years.12 Higher ratios indicate greater income.
Educational attainment was examined in years of schooling. Insurance status was examined using type of insurance and any insurance. Because of missing values in the type of insurance variable, any insurance was used in the multivariable analysis. Employment was assessed by self-reported occupation and current employment. Because of missing values in occupational category, current employment was used in the multivariable modeling.
All analyses were performed using SAS 8.2 (SAS Institute). Student 2-sided t tests were used to compare differences between dimensional variables, χ2 tests were used to compare differences between categorical variables, and the Cochran–Armitage test was used to assess trends across ordinal variables.
Multivariable Analysis Strategy
Our multivariable analysis strategy was developed to evaluate the impact of income, education, and insurance on the stroke–ethnicity association and involved the following 6 steps. In step 1, we used forward-stepping multivariable logistic regression analysis to identify clinical factors that were associated with stroke, excluding ethnicity, education, insurance status, and income to poverty threshold ratio. In step 2, we used logistic regression to model stroke and included the variables that were independently associated with stroke (from step 1) and added ethnicity. In step 3, we used the model from step 2 (including ethnicity) and added the income to poverty threshold ratio. In step 4, we used the model from step 2 and added education. In step 5, we used the model from step 2 and added insurance status. In step 6, we used the model from step 3 and added insurance status. We did not include education in step 6 because education was not independently associated with stroke in step 4.
As part of step 1, the following stroke risk factors were evaluated: age, sex, history of hypertension, systolic and diastolic blood pressures, history of myocardial infarction, claudication, congestive heart failure, atrial fibrillation, treated diabetes, untreated diabetes, glycosylated hemoglobin (HbA1c), insulin sensitivity, current smoking, tobacco use (pack-years), alcohol use, activity, body mass index, waist circumference, C-reactive protein, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. These clinical factors were identified on the basis of clinical judgment and previous research. Clinical significance was accepted at an adjusted odds ratio (OR) of 1.3 (for categorical variables) and statistical significance was accepted at P<0.05. Variables that were associated with stroke, using both clinical and statistical significance, were retained in the multivariable analyses. An events-per-variable ratio of >10:1 was maintained in all models.13
Ethnicity, insurance status, and educational attainment were known for all participants. Among the 11 163 subjects, the IPTR was unknown in 1314 (12%). Subjects with unknown IPTR versus subjects with known IPTR did not differ with regard to ethnicity (black: 339/1314, 26% versus 2413/9849, 24%; P=0.30), had a greater prevalence of stroke (93/1314, 7% versus 526/9849, 5%; P=0.0098), were older (mean±standard deviation: 67.4±14.0 versus 62.1±14.0 years; P<0.0001), were more frequently female (784/1314, 60% versus 5130/9849, 52%; P<0.0001), had higher systolic blood pressure (mean±standard deviation: 136.3±20.3 versus 134.1±20.1 mm Hg; P=0.0020), had greater prevalence of claudication (425/1314, 32% versus 2918/9849, 30%; P=0.043), had greater prevalence of untreated diabetes (152/1314, 12% versus 963/9849, 10%; P<0.0001), had higher HbA1c values (mean±standard deviation: 5.9±1.2 versus 5.8±1.3; P=0.034), were less likely to smoke (234/1314, 18% versus 2178/9849, 22%; P=0.0004), and were less likely to be active (825/1314, 63% versus 7198/9849, 73%; P<0.0001). No imputations were made for missing values in the main analyses. When the mean value of the IPTR was imputed for missing values, the results were essentially identical (data not shown).
Among 11 163 participants, 2752 (25%) were black, and 619 (6%) were post-stroke. There was no ethnic difference in the stroke prevalence (blacks: 160/2752 [6%]; whites: 459/8411 [6%]); P=0.48). The proportion of patients with stroke decreased with increasing income (IPTR <1: 239/3279, 7.3%; IPTR 1 to 2: 193/2789, 6.9%; IPTR 2 to 3: 88/1811, 4.9%; IPTR ≥3: 99/3284, 3.0%; P<0.0001). Participants with stroke had lower educational attainment compared with participants without stroke (mean±standard deviation: 9.2±4.1 versus 10.3±4.2 years; P<0.0001). Participants without insurance were less likely to have been told by a doctor that they had a stroke than participants with insurance (24/1307, 1.8% versus 595/9856, 6.0%; P<0.0001). Currently employed participants were less likely to have a stroke history (57/4629, 1% versus 562/6534, 9%; P<0.0001).
Ethnic Differences in Stroke Risk Factors
Eight clinical factors were independently associated with stroke: older age, history of hypertension, treated diabetes, claudication, myocardial infarction, higher C-reactive protein, lower high-density lipoprotein cholesterol, and inactivity. Blacks had a higher prevalence of 5 factors: history of hypertension, treated diabetes, claudication, higher C-reactive protein, and inactivity. Whites had a higher prevalence of 3 factors: age, myocardial infarction, and high-density lipoprotein cholesterol (Table 1).
Influence of Income, Education, and Insurance on the Ethnicity–Stroke Association
Table 2 provides the adjusted odds ratios (ORs) for stroke (adjusted for the 8 factors independently associated with stroke) from the multivariable analyses evaluating the role of income, education, and insurance on the ethnicity–stroke association. Ethnicity was independently associated with stroke when adjusting for the 8 factors that were independently associated with stroke (OR, 1.32; 95% CI, 1.04 to 1.67). However, ethnicity was not independently associated with stroke when income was added to the model (OR, 1.15; 95% CI, 0.88 to 1.49); income was independently associated with stroke (OR, 0.89; 95% CI, 0.82 to 0.95). Adjustment for educational attainment did not alter the ethnicity–stroke association and education was not independently associated with stroke. Ethnicity was independently associated with stroke after adjustment for having no insurance (OR, 1.30; 95% CI, 1.02 to 1.65) as was having no insurance (OR, 0.54; 95% CI, 0.33 to 0.90). Ethnicity was not associated with stroke after adjustment for both income and insurance status.
In a secondary analysis, income and current employment were forced into a logistic regression model with the clinical factors associated with stroke: ethnicity was not associated with stroke, but both income (OR, 0.93; 95% CI, 0.88 to 0.99) and current employment were (OR, 0.38; 95% CI, 0.27 to 0.52).
We found, in this sample of community-dwelling stroke survivors, that ethnic disparities exist in the prevalence of vascular risk factors and that income may explain the association between black ethnicity and stroke.
Three explanations have been proposed to account for the excess stroke incidence and mortality in blacks: (1) blacks have a higher prevalence or greater severity of stroke risk factors; (2) biological differences; or (3) blacks have a lower socioeconomic status (eg, inadequate access to care).14,15⇓ The current study does not specifically examine differences in biology (eg, stroke subtype) between blacks and whites; however, our data provide support for the other 2 hypotheses.
Previous research has shown that blacks have increased prevalence of some stroke risk factors.16,17⇓ Our finding of ethnic differences in vascular risk factors, along with the previous research, provides support for the hypothesis that ethnic differences in stroke are caused in part by differences in vascular risk factors.
In support of the hypothesis that ethnic disparities in stroke are caused by lower socioeconomic status, previous studies have demonstrated that lower socioeconomic status is associated with increasing stroke risk.5,7,18–20⇓⇓⇓⇓ Similarly, we found that low income was independently associated with stroke.
We were surprised by the finding that having insurance was associated with a higher risk of stroke in this data set; we offer 4 hypotheses to explain this finding. First, given the cross-sectional design of the NHANES, no temporal relationship can be established for the insurance–stroke relationship. Some patients without insurance might have had a stroke and then become eligible for disability insurance (thereby increasing the association between insurance and stroke). Second, in the US, there is a strong correlation between increasing age and having Medicare insurance. Given the relationship between increasing age and stroke risk, the insurance–stroke association may be confounded by age. However, because the multivariable analysis included adjustment for age, this hypothesis seems less plausible. Third, the insurance variable we used was a crude measure (any versus no insurance) and might not have adequately described a person’s coverage for or access to health care. Fourth, patients with insurance may be more likely to have a physician and consequently more likely to have stroke diagnosed.
One additional hypothesis requires examination, specifically that prejudice might contribute to ethnic disparities.21 Although numerous studies have demonstrated ethnic disparities in stroke incidence, outcomes, and care, these studies have not attributed the disparities to prejudice. Our study was not designed to examine prejudice, but future studies of ethnic disparities should explore this hypothesis.
We used NHANES data because it provided a large sample and included a large subsample of blacks. Our results should be evaluated within the context of 2 important limitations: the NHANES sample included noninstitutionalized participants and the NHANES data are cross-sectional.
Because NHANES includes only noninstitutionalized participants, subjects with the most severe strokes are not likely to be included. Specifically those patients with stroke who died from their stroke or who required skilled nursing home care would not be included. Similarly, stroke survivors with impaired communication skills are also not likely to have been included. Given that blacks have greater stroke severity and stroke mortality than whites, it is probable that NHANES under-represents blacks with stroke.22,23⇓ Our finding that stroke rates were similar for blacks and whites may be caused by this sampling method. Future research should include participants living in a variety of settings and should examine incident stroke to avoid excluding patients who have died.
Another important limitation is that because of the cross-sectional methodology used in NHANES, we cannot ascertain the temporal sequence of the vascular risk factors or the socioeconomic status variables and stroke. For example, some of the clinical factors (eg, hypertension) that were associated with stroke may have been identified after the stroke. However, the vascular risk factors that were associated with stroke in the current study have all been associated with stroke in other studies. The cross-sectional nature of the data should be considered when evaluating the results that describe the association between socioeconomic status and stroke, because we do not know the participants’ income, insurance, or employment status before their stroke.
Because the NHANES did not include an assessment of wealth (eg, owning property), our evaluation of socioeconomic status was confined to income, education, insurance status, employment, and occupation.
The primary outcome measure in the current study was self-reported stroke history. Previous authors have demonstrated strong agreement between self-reported stroke history and medical record review.24,25⇓ For example, the question, “Have you ever had a stroke?” had a sensitivity of 95% and specificity of 96% compared with a home assessment, clinician interview, and medial record review.24 However, the self-report stroke question used in NHANES does not discern stroke subtype. Ethnic differences exist in stroke subtypes incidence;17,26,27⇓⇓ however, our study is unable to examine the role of socioeconomic status by stroke subtype.
We have described the subjects for whom the income to poverty threshold value was unknown. Although missing data might have biased our findings, it is unlikely to have had a large effect given that the influence of the IPTR had a very small or no effect on the ORs of the 8 factors other than ethnicity in the multivariable models. Also, our results did not change after imputing the mean IPTR for missing data.
We included white and black NHANES participants aged 40 years and older. Because cause and epidemiology differ for stroke in young versus older patients, research that focuses on stroke in adults often uses an age cutoff of 45 years and older.23,28,29⇓⇓ Given that blacks have an increased risk of stroke at earlier ages than whites,30 we chose to use ≥40 years age demarcation to ensure that our sample included adequate numbers of blacks with stroke. The ≥40 years age cutoff has been used by other studies about ethnic differences in stroke risk.2,31⇓
Given our findings that in a group of community-dwelling stroke survivors, blacks had a higher prevalence of several modifiable vascular risk factors, clinicians and policy makers should attempt to ameliorate ethnic differences in stroke risk by addressing differences in these modifiable risk factors.
D.M.B. and J.L. are supported by Career Development Awards from the Department of Veteran Affairs Health Services Research & Development Service. C.K.W. was supported by supplemental funds from the Department of Veteran Affairs Health Services Research & Development Service.
D.M.B. conceived this study. D.M.B., J.C., J.L., L.M.B and C.K.W. participated in the design of the study. D.M.B. and C.K.W. conducted the data analysis with input from all of the authors. D.M.B. prepared the manuscript, and all of the authors edited and approved the final manuscript.
- Received January 4, 2005.
- Revision received February 21, 2005.
- Accepted April 21, 2005.
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