Inserm, U707, Research Unit in Epidemiology, Information Systems, and Modeling, Paris, France.
Epidemiology. 2011 Sep;22(5):694-703. doi: 10.1097/EDE.0b013e3182257784.
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.
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).
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.
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.
我们研究了以个体为中心的区域（即个体居住地为中心）内测量的邻里社会经济特征是否与体重指数（BMI[kg/m²]）和腰围有关。我们使用倾向评分匹配作为一种诊断和验证工具，以检查社会空间隔离（和相关的结构性混杂）是否允许我们在调整个体社会经济特征后估计邻里社会经济效应，而不会过度模型外推。
我们使用 RECORD（居住环境与冠心病）队列研究，对来自巴黎地区的 7230 名成年人进行了横断面分析。我们首先使用传统的多层次回归模型，根据个体协变量调整了 3 个邻里社会经济指标（教育、收入、房地产价格）与 BMI 和腰围的关系。其次，我们检查了这些关联在基于居住在低社会经济地位社区的可能性进行参与者交换（倾向评分匹配样本）时是否仍然存在。
在调整了协变量后，BMI/腰围随着邻里社会经济地位的降低而增加，尤其是在以居住地周围 500 米半径缓冲区为基础的邻里教育方面；这种关联在女性中更强。通过倾向评分匹配技术，暴露人群和未暴露人群之间的暴露可能性存在一些重叠。作为社会空间隔离的一个函数，以及是否支持数据推断的一个指标，样本量从初始样本到倾向评分匹配样本减少了 17%-59%。倾向评分匹配模型证实了在整个样本模型中获得的关系。
总的来说，在法国背景下，尽管社会空间隔离程度很高，但通过调整邻里社会经济变量与 BMI/腰围之间的关联，在没有过度模型外推的情况下，可以进行实证估计。
