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(Stroke. 2009;40:3336.)
© 2009 American Heart Association, Inc.

Original Contributions

Factors Explaining Excess Stroke Prevalence in the US Stroke Belt

Youlian Liao, MD; Kurt J. Greenlund, PhD; Janet B. Croft, PhD; Nora L. Keenan, PhD Wayne H. Giles, MD, MS

From the Division of Adult and Community Health (Y.L., K.J.G., J.B.C., W.H.G.) and the Division for Heart Disease and Stroke Prevention (N.L.K.), National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Ga.

Correspondence to Youlian Liao, MD, Division of Adult and Community Health, Centers for Disease Control and Prevention, 4770 Buford Highway, NE, Mailstop K-30, Atlanta, GA 30341-3717. E-mail ycl1{at}cdc.gov

Background and Purpose— Higher risk and burden of stroke have been observed within the southeastern states (the Stroke Belt) compared with elsewhere in the United States. We examined reasons for these disparities using a large data set from a nationwide cross-sectional study.

Methods— Self-reported data from the 2005 and 2007 Behavioral Risk Factor Surveillance System were used (n=765 368). The potential contributors for self-reported stroke prevalence (n=27 962) were demographics (age, sex, geography, and race/ethnicity), socioeconomic status (education and income), common risk factors (smoking and obesity), and chronic diseases (hypertension, diabetes, and coronary heart disease). Multivariate logistic regression was used in the analysis.

Results— The age- and sex-adjusted OR comparing self-reported stroke prevalence in the 11-state Stroke Belt versus non-Stroke Belt region was 1.25 (95% CI, 1.19 to 1.31). Unequal black/white distribution by region accounted for 20% of the excess prevalence in the Stroke Belt (OR reduced to 1.20; 1.15 to 1.26). Approximately one third (32%) of the excess prevalence was accounted either by socioeconomic status alone or by risk factors and chronic disease alone (OR, 1.12). The OR was further reduced to 1.07 (1.02 to 1.13) in the fully adjusted logistic model, a 72% reduction.

Conclusions– Differences in socioeconomic status, risk factors, and prevalence of common chronic diseases account for most of the regional differences in stroke prevalence.

Key Words: epidemiology • public policy • risk factors