08: A Hierarchical Bayesian Model for Mixture Exposure and Count Data in Environmental Health

Music City Center 

Modeling count-based health outcomes in environmental research presents challenges like correlated exposures, non-linear interactions, and spatiotemporal dependencies. We propose a hierarchical Bayesian model that incorporates the negative binomial distribution via data augmentation to address these complexities. This framework integrates variable selection, effect estimation, and hotspot detection to improve inference in exposure-outcome relationships.
We evaluated the model through simulations across eight scenarios, varying exposure correlation, interaction effects, and dependence. Each scenario included 100 datasets of 504 observations across 21 spatial units and 24 time points. The model's utility was further demonstrated using real-world air pollution data.
The proposed approach consistently identified influential exposures, estimated effects, and detected hotspot areas, particularly with appropriate spatiotemporal dependencies. By leveraging the negative binomial distribution, it accounted for data dispersion without additional adjustments. This model provides a robust, unified framework for analyzing count outcomes in environmental health research and policy-making.

Count Data

Mixture Exposure

Bayesian Kernel Machine Regression

Air Pollution

Environmental Health 

Section on Statistics in Epidemiology