Factors explaining excess stroke prevalence in the US Stroke Belt.
OBJECTIVE
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.
Liao
Youlian
Y
Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA 30341-3717, USA. ycl1@cdc.gov
Greenlund
Kurt J
KJ
Croft
Janet B
JB
Keenan
Nora L
NL
Giles
Wayne H
WH
D000293:Adolescent; D000328:Adult; D000368:Aged; D040343:Behavioral Risk Factor Surveillance System; D044383:Black People / Q000706:statistics & numerical data; D002318:Cardiovascular Diseases / Q000453:epidemiology; D015984:Causality; D002908:Chronic Disease / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D005843:Geography; D015438:Health Behavior; D054624:Health Status Disparities; D006306:Health Surveys; D006801:Humans; D008019:Life Style / Q000208:ethnology; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009765:Obesity; D015995:Prevalence; D044469:Racial Groups; D012307:Risk Factors; D012923:Social Class; D012959:Socioeconomic Factors; D020521:Stroke / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology; D044465:White People / Q000706:statistics & numerical data; D055815:Young Adult; D001741:Black or African American