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Built environment features (BEFs) refer to aspects of the human constructedenvironment, which may in turn support or restrict health related behaviors andthus impact health. In this paper we are interested in understanding whetherthe spatial distribution and quantity of fast food restaurants (FFRs) influencethe risk of obesity in schoolchildren. To achieve this goal, we propose atwo-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 referencelocations as realizations of Inhomogenous Poisson processes (IPP). With thegoal of identifying representative spatial patterns of exposure to FFRs, wemodel the intensity functions of the IPPs using a Bayesian non-parametricviewpoint and specifying a Nested Dirichlet Process prior. The second stagemodel relates exposure patterns to obesity, offering two different approachesto accommodate uncertainty in the exposure patterns estimated in the firststage: in the first approach the odds of obesity at the school level isregressed on cluster indicators, each representing a major pattern of exposureto FFRs. In the second, we employ Bayesian Kernel Machine regression to relatethe odds of obesity to the multivariate vector reporting the degree ofsimilarity of a given school to all other schools. Our analysis on theinfluence of patterns of FFR occurrence on obesity among Californianschoolchildren has indicated that, in 2010, among schools that are consistentlyassigned to a cluster, there is a lower odds of obesity amongst 9th graders whoattend schools with most distant FFR occurrences in a 1-mile radius as comparedto others.

How Close and How Much? Linking Health Outcomes to Built Environment Spatial Distributions