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/m2 (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.
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/m2 (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.
Authors: Chika Vera Anekwe; Amber R Jarrell; Matthew J Townsend; Gabriela I Gaudier; Julia M Hiserodt; Fatima Cody Stanford Journal: Curr Obes Rep Date: 2020-09
Authors: Annemarie G Hirsch; April P Carson; Nora L Lee; Tara McAlexander; Carla Mercado; Karen Siegel; Nyesha C Black; Brian Elbel; D Leann Long; Priscilla Lopez; Leslie A McClure; Melissa N Poulsen; Brian S Schwartz; Lorna E Thorpe Journal: JMIR Res Protoc Date: 2020-10-19