Bayesian Inference for Spatially-Temporally Misaligned Data Using Predictive Stacking

TL;DR

This paper introduces a Bayesian hierarchical model with predictive stacking to analyze spatially-temporally misaligned air pollution and health data, providing a robust approach to estimate health impacts despite data aggregation challenges.

Contribution

It develops a novel Bayesian predictive stacking method that combines multiple models and bypasses convergence issues of traditional MCMC in spatial-temporal analysis.

Findings

Successfully applied to ozone and asthma data in California

Improves estimation accuracy over existing methods

Handles complex spatial-temporal misalignments

Abstract

Air pollution remains a major environmental risk factor that is often associated with adverse health outcomes. However, quantifying and evaluating its effects on human health is challenging due to the complex nature of exposure data. Recent technological advances have led to the collection of various indicators of air pollution at increasingly high spatial-temporal resolutions (e.g., daily averages of pollutant levels at spatial locations referenced by latitude-longitude). However, health outcomes are typically aggregated over several spatial-temporal coordinates (e.g., annual prevalence for a county) to comply with survey regulations. This article develops a Bayesian hierarchical model to analyze such spatially-temporally misaligned exposure and health outcome data. We introduce Bayesian predictive stacking, which optimally combines multiple predictive spatial-temporal models and avoids iterative estimation algorithms such as Markov chain Monte Carlo that struggle due to convergence issues inflicted by the presence of weakly identified parameters. We apply our proposed method to study the effects of ozone on asthma in the state of California.