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Impact of Changes in the Food, Built, and Socioeconomic Environment on BMI in US Counties, BRFSS 2003‐2012
Author(s) -
Rummo Pasquale E.,
Feldman Justin M.,
Lopez Priscilla,
Lee David,
Thorpe Lorna E.,
Elbel Brian
Publication year - 2020
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1002/oby.22603
Subject(s) - behavioral risk factor surveillance system , socioeconomic status , demography , environmental health , medicine , unemployment , obesity , random effects model , cross sectional study , gerontology , observational study , body mass index , geography , population , meta analysis , pathology , sociology , economics , economic growth
Objective Researchers have linked geographic disparities in obesity to community‐level characteristics, yet many prior observational studies have ignored temporality and potential for bias. Methods Repeated cross‐sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003‐2012) to examine the influence of county‐level characteristics (active commuting, unemployment, percentage of limited‐service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5‐year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county‐mean‐centered values for each year. Cross‐sectional (between‐county) and longitudinal (within‐county) associations were estimated using a random‐effects within‐between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county‐years within counties within states. Results A negative between‐county association for active commuting (β = −0.19; 95% CI: −0.23 to −0.16) and positive associations for unemployment (β = 0.17; 95% CI: 0.14 to 0.19) and limited‐service restaurants (β = 0.13; 95% CI: 0.11 to 0.14) were observed. An SD increase in active commuting within counties was associated with a 0.51‐kg/m 2 (95% CI: −0.72 to −0.31) decrease in BMI over time. Conclusions These results suggest that community‐level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.