BACKGROUND: We investigated whether neighborhood socioeconomic characteristics, measured within person-centered areas (ie, centered on individuals' residences) are associated with body mass index (BMI [kg/m²]) and waist circumference. We used propensity-score matching as a diagnostic and validation tool to examine whether socio-spatial segregation (and related structural confounding) allowed us to estimate neighborhood socioeconomic effects adjusted for individual socioeconomic characteristics without excessive model extrapolations. METHODS: Using the RECORD (Residential Environment and CORonary heart Disease) Cohort Study, we conducted cross-sectional analyses of 7230 adults from the Paris region. We first estimated the relationships of 3 neighborhood socioeconomic indicators (education, income, real estate prices) with BMI and waist circumference using traditional multilevel regression models adjusted for individual covariates. Second, we examined whether these associations persisted when estimated among participants exchangeable based on their probability of living in low-socioeconomic-status neighborhoods (propensity-score matched samples). RESULTS: After adjustment for covariates, BMI/waist circumference increased with decreasing neighborhood socioeconomic status, especially with neighborhood education measured within 500-m radius buffers around residences; associations were stronger for women. With propensity-score matching techniques, there was some overlap in the odds of exposure between exposed and unexposed populations. As a function of socio-spatial segregation and an indicator of whether the data support inferences, sample size decreased by 17%-59% from the initial to the propensity-score matched samples. Propensity-score matched models confirmed relationships obtained from models in the entire sample. CONCLUSIONS: Overall, adjusted associations between neighborhood socioeconomic variables and BMI/waist circumference were empirically estimable in the French context, without excessive model extrapolations, despite the extent of socio-spatial segregation.
BACKGROUND: We investigated whether neighborhood socioeconomic characteristics, measured within person-centered areas (ie, centered on individuals' residences) are associated with body mass index (BMI [kg/m²]) and waist circumference. We used propensity-score matching as a diagnostic and validation tool to examine whether socio-spatial segregation (and related structural confounding) allowed us to estimate neighborhood socioeconomic effects adjusted for individual socioeconomic characteristics without excessive model extrapolations. METHODS: Using the RECORD (Residential Environment and CORonary heart Disease) Cohort Study, we conducted cross-sectional analyses of 7230 adults from the Paris region. We first estimated the relationships of 3 neighborhood socioeconomic indicators (education, income, real estate prices) with BMI and waist circumference using traditional multilevel regression models adjusted for individual covariates. Second, we examined whether these associations persisted when estimated among participants exchangeable based on their probability of living in low-socioeconomic-status neighborhoods (propensity-score matched samples). RESULTS: After adjustment for covariates, BMI/waist circumference increased with decreasing neighborhood socioeconomic status, especially with neighborhood education measured within 500-m radius buffers around residences; associations were stronger for women. With propensity-score matching techniques, there was some overlap in the odds of exposure between exposed and unexposed populations. As a function of socio-spatial segregation and an indicator of whether the data support inferences, sample size decreased by 17%-59% from the initial to the propensity-score matched samples. Propensity-score matched models confirmed relationships obtained from models in the entire sample. CONCLUSIONS: Overall, adjusted associations between neighborhood socioeconomic variables and BMI/waist circumference were empirically estimable in the French context, without excessive model extrapolations, despite the extent of socio-spatial segregation.
Authors: Jonggyu Baek; Jana A Hirsch; Kari Moore; Loni Philip Tabb; Tonatiuh Barrientos-Gutierrez; Lynda D Lisabeth; Ana V Diez-Roux; Brisa N Sánchez Journal: Epidemiology Date: 2017-05 Impact factor: 4.822
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Authors: Jennifer Ahern; Magdalena Cerdá; Sheri A Lippman; Kenneth J Tardiff; David Vlahov; Sandro Galea Journal: J Epidemiol Community Health Date: 2012-08-22 Impact factor: 3.710
Authors: Anh D Ngo; Catherine Paquet; Natasha J Howard; Neil T Coffee; Robert Adams; Anne Taylor; Mark Daniel Journal: BMC Public Health Date: 2013-07-25 Impact factor: 3.295