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

• Activity Number: 220 - Frontiers of Spatio-Temporal Statistical Learning in Health Care and Environmental Science
• Type: Invited
• Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Veronica J Berrocal*
• Companies: University of California
• Keywords: Built environment; Bayesian non-parameetrics; Inhomogeneous Poisson process; Bayesian Kernel Machine Regresison

Abstract

In this talk, 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 schools, we cluster them using a Nested Dirichlet Process prior. The second stage model relates exposure patterns to obesity, offering two different approaches to accommodate uncertainty in the exposure patterns estimated in the first stage. In the first approach, the odds of obesity at the school level are 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 using data for California 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.