Preventing Chronic Disease | Spatial Analysis and Correlates of County-Level Diabetes Prevalence, 2009–2010 - CDC
Spatial Analysis and Correlates of County-Level Diabetes Prevalence, 2009–2010
J. Aaron Hipp, PhD; Nishesh Chalise, MSW
Suggested citation for this article: Hipp JA, Chalise N. Spatial Analysis and Correlates of County-Level Diabetes Prevalence, 2009–2010. Prev Chronic Dis 2015;12:140404. DOI: http://dx.doi.org/10.5888/pcd12.140404
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Abstract
Introduction
Information on the relationship between diabetes prevalence and built environment attributes could allow public health programs to better target populations at risk for diabetes. This study sought to determine the spatial prevalence of diabetes in the United States and how this distribution is associated with the geography of common diabetes correlates.
Information on the relationship between diabetes prevalence and built environment attributes could allow public health programs to better target populations at risk for diabetes. This study sought to determine the spatial prevalence of diabetes in the United States and how this distribution is associated with the geography of common diabetes correlates.
Methods
Data from the Centers for Disease Control and Prevention and the US Census Bureau were integrated to perform geographically weighted regression at the county level on the following variables: percentage nonwhite population, percentage Hispanic population, education level, percentage unemployed, percentage living below the federal poverty level, population density, percentage obese, percentage physically inactive, percentage population that cycles or walks to work, and percentage neighborhood food deserts.
Data from the Centers for Disease Control and Prevention and the US Census Bureau were integrated to perform geographically weighted regression at the county level on the following variables: percentage nonwhite population, percentage Hispanic population, education level, percentage unemployed, percentage living below the federal poverty level, population density, percentage obese, percentage physically inactive, percentage population that cycles or walks to work, and percentage neighborhood food deserts.
Results
We found significant spatial clustering of county-level diabetes prevalence in the United States; however, diabetes prevalence was inconsistently correlated with significant predictors. Percentage living below the federal poverty level and percentage nonwhite population were associated with diabetes in some regions. The percentage of population cycling or walking to work was the only significant built environment–related variable correlated with diabetes, and this association varied in magnitude across the nation.
We found significant spatial clustering of county-level diabetes prevalence in the United States; however, diabetes prevalence was inconsistently correlated with significant predictors. Percentage living below the federal poverty level and percentage nonwhite population were associated with diabetes in some regions. The percentage of population cycling or walking to work was the only significant built environment–related variable correlated with diabetes, and this association varied in magnitude across the nation.
Conclusion
Sociodemographic and built environment–related variables correlated with diabetes prevalence in some regions of the United States. The variation in magnitude and direction of these relationships highlights the need to understand local context in the prevention and maintenance of diabetes. Geographically weighted regression shows promise for public health research in detecting variations in associations between health behaviors, outcomes, and predictors across geographic space.
Sociodemographic and built environment–related variables correlated with diabetes prevalence in some regions of the United States. The variation in magnitude and direction of these relationships highlights the need to understand local context in the prevention and maintenance of diabetes. Geographically weighted regression shows promise for public health research in detecting variations in associations between health behaviors, outcomes, and predictors across geographic space.
Acknowledgments
This publication was supported by National Institutes of Health’s National Institute of Diabetes and Digestive and Kidney Diseases P30DK092950 and Washington University Center for Diabetes Translation Research (WU-CDTR). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of WU-CDTR, the National Institute of Diabetes and Digestive and Kidney Diseases, or the National Institutes of Health. We acknowledge the support of the Washington University Institute for Public Health for cosponsoring, with WU-CDTR, the Next Steps in Public Health event that led to the development of this study.
Author Information
Corresponding Author: J. Aaron Hipp, PhD, Brown School, Washington University in St Louis, Campus Box 1196, One Brookings Dr, St Louis, MO 63130. Telephone: 314-935-3868. Email: ahipp@wustl.edu.
Author Affiliation: Nishesh Chalise, Brown School, Washington University in St. Louis, St. Louis, Missouri.
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