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.
From the aInserm, U707, Research Unit in Epidemiology, Information Systems, and Modeling, Paris, France; bUniversité Pierre et Marie Curie-Paris6, Paris, France; cEHESP School of Public Health, Rennes, France; and dCentre d'Investigations Préventives et Cliniques, Paris, France.
Submitted 20 September 2010; accepted 6 April 2011; posted 27 June 2011.
Supported by the RECORD project received support from the Institute for Public Health Research (IReSP, Institut de Recherche en Santé Publique); the National Institute for Prevention and Health Education (INPES, Institut National de Prévention et d'Education pour la Santé) (Prevention Program 2007 074/07-DAS and 2010–2011 financial support); the National Institute of Public Health Surveillance (InVS, Institut de Veille Sanitaire) (Territory and Health Program); the French Ministries of Research and Health (Epidemiologic Cohorts Grant 2008); the National Health Insurance Office for Salaried Workers (CNAM-TS, Caisse Nationale d'Assurance Maladie des Travailleurs Salariés); the National Research Agency (ANR, Agence Nationale de la Recherche) (Health–Environment Program 2005, 00153 05); the Ile-de-France Regional Health Agency (ARS, Agence Régionale de Santé d'Île-de-France); the City of Paris (Ville de Paris); and the Ile-de-France Youth, Sports, and Social Cohesion Regional Direction (DRJSCS, Direction Régionale de la Jeunesse et des Sports et de la Cohésion Sociale).
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Correspondence: Cinira Leal, Inserm U707, Faculté de Médecine Saint-Antoine, 27 rue Chaligny, 75012, Paris, France. E-mail: firstname.lastname@example.org.