A Geo-Stratified Analysis of Associations Between Socio-Economic Factors and Diabetes Risk

Abstract

Introduction. In 2019, diabetes was the seventh leading cause of death in the United States. The association between diabetes risk and socio-economic factors in the United States has been examined primarily at the national level; little is known about this association at the regional level. This study examines and compares the association between diabetes risk and previously established socio-economic factors across four geographic regions (South, Midwest, West, and Northwest).

Methods. We analyzed the 2014 Behavioral Risk Factor Surveillance System (BRFSS) data stratified by four geographic regions of the United States. The risk estimates of diabetes associated with previously established socio-economic factors as well as diabetes prevalence were compared across four geographic regions.

Results. There was marked variation in association between diabetes risk and previously established risk factors across the four geographic regions. In the South, rural residency was associated with increased diabetes risk, whereas in the other geographic regions rural residency had a protective effect. In the South, the diabetes risk for males was 22% higher compared to females in the South, whereas the risk for males was 41% higher than females in the Northeast. Independently, age had the strongest discriminative ability to distinguish between a person with diabetes and a person without diabetes, whereas ethnicity, race and sex had the weakest discriminative abilities.

Conclusions. Our findings suggest a higher prevalence of diabetes by race/ethnicity (Non-Hispanic Black and Hispanic) and income across all four regions. While rural residency is highest in the South, but protective in other regions. Overall, we found age and income provide the highest predictive ability for diabetes risk.  This study highlights differences in diabetes prevalence in association between previously established socio-economic variables and diabetes risk across four geographic regions. These findings could help public health professionals and policy makers in understanding the dynamic relationship between diabetes and risk factors at the regional level.