Assessing county-level determinants of diabetes in the United States (2003–2012). (May 2020)

• Record Type: Journal Article
• Authors: Feldman, Justin M.; Lee, David C.; Lopez, Priscilla; Rummo, Pasquale E.; Hirsch, Annemarie G.; Carson, April P.; McClure, Leslie A.; Elbel, Brian; Thorpe, Lorna E.
• Is Part Of: Health & place. Volume 63(2020:May)
• Publication Date: 2020-05
• Subjects: Diabetes -- Built environment -- Multilevel modeling; Health -- Social aspects -- Periodicals; Health services accessibility -- Periodicals; Public health -- Periodicals; Political planning -- Periodicals; Social medicine -- Periodicals; Epidemiology -- Periodicals; Health Policy -- Periodicals; Health Services Accessibility -- Periodicals; Public Health -- Periodicals; Public Policy -- Periodicals; Sociology, Medical -- Periodicals; Épidémiologie -- Périodiques; Politique sanitaire -- Périodiques; Santé, Services de -- Accessibilité -- Périodiques; Health services accessibility; Health -- Social aspects; Political planning; Public health; Social medicine; Periodicals; 613
• DOI: 10.1016/j.healthplace.2020.102324
• Languages: English
• ISSNs: 1353-8292

Abstract

Abstract: Using data from the United States Behavioral Risk Factor Surveillance System (2003–2012; N = 3, 397, 124 adults), we estimated associations between prevalent diabetes and four county-level exposures (fast food restaurant density, convenience store density, unemployment, active commuting). All associations confirmed our a priori hypotheses in conventional multilevel analyses that pooled across years. In contrast, using a random-effects within-between model, we found weak, ambiguous evidence that within-county changes in exposures were associated with within-county change in odds of diabetes. Decomposition revealed that the pooled associations were largely driven by time-invariant, between-county factors that may be more susceptible to confounding versus within-county associations. Highlights: Multilevel analyses of repeated cross-sectional surveys depend on model choice. 4 county-level measures predicted diabetes in a model that ignored temporality. There is marginal evidence that increasing walkability led to decreased diabetes. No other findings were supported by a model that considered change over time. Models of repeated-cross sectional survey data should incorporate temporality.