Hierarchical Bayesian Spatial Methods for Exposure Buffer-Size Selection in Place-Based Studies

Oregon Convention Center 

Place-based epidemiology studies often rely on circular buffers to define exposure at spatial locations. Buffers are a popular choice due to their simplicity and alignment with public health policies. However, the buffer radius is often chosen relatively arbitrarily and assumed constant across space, which may result in biased effect estimates if these assumptions are violated. To address these limitations, we propose a novel method to inform buffer size selection and allow for spatial heterogeneity in radii across outcome units. Our model uses a spatially structured Gaussian process to model buffer radii as a function of covariates and spatial random effects, and a modified Bayesian variable selection framework to select the most appropriate radius distance. We perform a simulation study to understand the properties of our new method and apply our proposed method to a study of health care access and health outcomes in Madagascar. We find that our method outperforms existing approaches in terms of estimation and inference for key model parameters. By relaxing rigid assumptions about buffer characteristics, our method offers a flexible, data-driven approach to exposure definition.

Bayesian methods

exposure buffers

geographic and spatial uncertainty

place-based epidemiology

health studies 

Section on Statistics in Epidemiology