Abstract

Built environment features (BEFs) refer to aspects of the human constructed environment, which may in turn support or restrict health related behaviors and thus impact health. In this work, we are interested in understanding whether the spatial distribution and quantity of fast food restaurants (FFRs) influence the risk of obesity in schoolchildren. To achieve this goal, we propose a two-stage Bayesian hierarchical modeling framework. In the first stage, examining the position of FFRs relative to that of some reference locations - in our case, schools - we model the distances of FFRs from these reference locations as realizations of a spatial point process. With the goal of identifying representative spatial patterns of exposure to FFRs, we model the intensity functions of the spatial point process using a Bayesian non-parametric model, specifying a Nested Dirichlet Process prior. The second stage model relates exposure patterns to obesity. We offer two different approaches to carry out the second stage, which differ in the way they accommodate uncertainty in the exposure patterns. Our analysis of the influence of patterns of FFR occurrence on obesity among Californian schoolchildren has indicated that, in 2010, there is a lower odds of obesity among 9th graders who attend school with most distant FFR occurrences in a 1-mile radius as compared to others.