Abstract 12: Socioeconomic Correlates of Cardiovascular Disease Risk in African Americans

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Abstract

Background: Research has found that socioeconomic status (SES) is associated with cardiovascular disease (CVD) risk. However, few studies have reported the SES correlates of CVD risk in African Americans (AA). Since low SES is over-represented in AA, our objective is to examine the socioeconomic correlates of CVD risk in this population, which may provide new insights on racial/ethnic disparities in CVD risk.

Methods: A cross-sectional analysis of baseline data collected in the Jackson Heart Study (JHS), an entirely AA population, was performed. Multivariable logistic regression analysis estimated the age and sex-adjusted association of SES indicators (education, household income and occupation) with prevalent CVD (myocardial infarction (MI), stroke and hypertension). Stratified analyses by age (<65 and ≥65 years) and sex were also performed. Additional analyses were performed using logistic models with a backward selection procedure that included age, sex, education, household income, occupation and burden of CVD risk factors (measured by the number of Life’s Simple 7® metrics meeting ideal health) as independent variables and prevalent CVD as dependent variables, Due to missing data, sample sizes for these analyses ranged from 3,473 to 5,301.

Results: Baseline prevalence for MI, stroke and hypertension were 5.5%, 4.4% and 60.1%. About one-third (32.5%) of the participants had a bachelor’s degree, 35% held management or professional occupations and 30% were “affluent” (≥3.5 US Census poverty level). We observed an inverse relationship between prevalent CVD and SES indicators. The largest and most consistent SES correlate of prevalent CVD was observed with income on MI (odds ratio (OR) 3.5; 95% CI 2.3, 5.4) and stroke (OR 3.7; 95% CI 2.3, 6.0) comparing the poor (below US Census poverty level) to the affluent income categories. Stratified analyses suggested stronger SES gradients in individuals <65 years and in females for MI. In the backward logistic regression models, the significant SES indicators (p<0.05) remaining in the final model were income for MI and stroke, and occupation for hypertension. The burden of CVD risk factors was statistically significant and remained in the final model for all three CVD outcomes.

Conclusions: Inverse SES gradients on the prevalence of MI, stroke and hypertension were observed among AA in the JHS. The most important SES correlates were income for MI and stroke, and occupation for hypertension. These findings suggest SES, and income in particular, is an important factor contributing to health disparities in CVD among AA and possibly AA-White disparities in health outcomes.

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