Solveig A Cunningham1, Shivani A Patel2, Gloria L Beckles3, Linda S Geiss3, Neil Mehta2, Hui Xie3, Giuseppina Imperatore3. 1. Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA. Electronic address: sargese@emory.edu. 2. Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA. 3. Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, GA.
Abstract
PURPOSE: Health and administrative systems are facing spatial clustering in chronic diseases such as diabetes. This study explores how geographic distribution of diabetes in the United States is associated with socioeconomic and built environment characteristics and health-relevant policies. METHODS: We compiled nationally representative county-level data from multiple data sources. We standardized characteristics to a mean = 0 and a SD = 1 and modeled county-level age-adjusted diagnosed diabetes incidence in 2013 using 2-level hierarchical linear regression. RESULTS: Incidence of age-standardized diagnosed diabetes in 2013 varied across U.S. counties (n = 3109), ranging from 310 to 2190 new cases/100,000, with an average of 856.4/100,000. Socioeconomic and health-related characteristics explained ∼42% of the variation in diabetes incidence across counties. After accounting for other characteristics, counties with higher unemployment, higher poverty, and longer commutes had higher incidence rates than counties with lower levels. Counties with more exercise opportunities, access to healthy food, and primary care physicians had fewer diabetes cases. CONCLUSIONS: Features of the socioeconomic and built environment were associated with diabetes incidence; identifying the salient modifiable features of counties can inform targeted policies to reduce diabetes incidence.
PURPOSE: Health and administrative systems are facing spatial clustering in chronic diseases such as diabetes. This study explores how geographic distribution of diabetes in the United States is associated with socioeconomic and built environment characteristics and health-relevant policies. METHODS: We compiled nationally representative county-level data from multiple data sources. We standardized characteristics to a mean = 0 and a SD = 1 and modeled county-level age-adjusted diagnosed diabetes incidence in 2013 using 2-level hierarchical linear regression. RESULTS: Incidence of age-standardized diagnosed diabetes in 2013 varied across U.S. counties (n = 3109), ranging from 310 to 2190 new cases/100,000, with an average of 856.4/100,000. Socioeconomic and health-related characteristics explained ∼42% of the variation in diabetes incidence across counties. After accounting for other characteristics, counties with higher unemployment, higher poverty, and longer commutes had higher incidence rates than counties with lower levels. Counties with more exercise opportunities, access to healthy food, and primary care physicians had fewer diabetes cases. CONCLUSIONS: Features of the socioeconomic and built environment were associated with diabetes incidence; identifying the salient modifiable features of counties can inform targeted policies to reduce diabetes incidence.
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