HOW CLOSE AND HOW MUCH? LINKING HEALTH OUTCOMES TO BUILT ENVIRONMENT SPATIAL DISTRIBUTIONS.

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 paper 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 Inhomogenous Poisson processes (IPP). With the goal of identifying representative spatial patterns of exposure to FFRs, we model the intensity functions of the IPPs 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; they differ in how they accommodate uncertainty in the exposure patterns. In the first approach the odds of obesity at the school level is regressed on cluster indicators, each representing a major pattern of exposure to FFRs. In the second, we employ Bayesian Kernel Machine regression to relate the odds of obesity to the multivariate vector reporting the degree of similarity of a given school to all other schools. Our analysis on the influence of patterns of FFR occurrence on obesity among Californian schoolchildren has indicated that, in 2010, among schools that are consistently assigned to a cluster, there is a lower odds of obesity amongst 9th graders who attend schools with most distant FFR occurrences in a 1-mile radius as compared to others.