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

Justin M. Feldman; David C. Lee; Priscilla Lopez; Pasquale E. Rummo; Annemarie G. Hirsch; April P. Carson; Leslie A. McClure; Brian Elbel; Lorna E. Thorpe

doi:10.1016/j.healthplace.2020.102324

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

Justin M. Feldman, David C. Lee, Priscilla Lopez, Pasquale E. Rummo, Annemarie G. Hirsch, April P. Carson, Leslie A. McClure, Brian Elbel, Lorna E. Thorpe

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Article number	102324
Journal	Health and Place
Volume	63
DOIs	https://doi.org/10.1016/j.healthplace.2020.102324
State	Published - May 2020

Keywords

Built environment
Diabetes
Multilevel modeling

ASJC Scopus subject areas

Health(social science)
Sociology and Political Science
Life-span and Life-course Studies

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10.1016/j.healthplace.2020.102324

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title = "Assessing county-level determinants of diabetes in the United States (2003–2012)",

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.",

keywords = "Built environment, Diabetes, Multilevel modeling",

author = "Feldman, {Justin M.} and Lee, {David C.} and Priscilla Lopez and Rummo, {Pasquale E.} and Hirsch, {Annemarie G.} and Carson, {April P.} and McClure, {Leslie A.} and Brian Elbel and Thorpe, {Lorna E.}",

note = "Funding Information: This work was supported by the Centers for Disease Control and Prevention [Grant number 1U01DP006299-01 ]. Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2020",

month = may,

doi = "10.1016/j.healthplace.2020.102324",

language = "English (US)",

volume = "63",

journal = "Health and Place",

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T1 - Assessing county-level determinants of diabetes in the United States (2003–2012)

AU - Feldman, Justin M.

AU - Lee, David C.

AU - Lopez, Priscilla

AU - Rummo, Pasquale E.

AU - Hirsch, Annemarie G.

AU - Carson, April P.

AU - McClure, Leslie A.

AU - Elbel, Brian

AU - Thorpe, Lorna E.

PY - 2020/5

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AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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