A varying-coefficient model for characterizing duration-driven heterogeneity in flood-related health impacts

TL;DR

This paper introduces a novel Bayesian varying-coefficient model to analyze how flood duration influences health impacts, providing detailed insights into duration-driven heterogeneity using extensive Medicare data.

Contribution

The study develops the first exposure duration varying coefficient model within a self-matched design, incorporating Bayesian Gaussian processes for detailed effect heterogeneity analysis.

Findings

Model outperforms traditional methods in simulations

Reveals significant duration-driven heterogeneity in flood effects

Provides high-resolution insights into flood-related health impacts

Abstract

Previous work revealed associations between flood exposure and adverse health outcomes during and in the aftermath of flood events. Floods are highly heterogeneous events, largely owing to vast differences in flood durations, i.e., flash-floods versus slow-moving floods. However, little to no work has incorporated exposure duration into the modeling of flood-related health impacts or has investigated duration-driven effect heterogeneity. To address this gap, we propose an exposure duration varying coefficient modeling (EDVCM) framework for estimating exposure day-specific health effects of consecutive-day environmental exposures that vary in duration. We develop the EDVCM within an area-level self-matched study design to eliminate time-invariant confounding followed by conditional Poisson regression modeling for exposure effect estimation and adjustment of time-varying confounders. Using a Bayesian framework, we introduce duration- and exposure day-specific exposure coefficients within the conditional Poisson model and assign them a two-dimensional Gaussian process prior to allow for sharing of information across both duration and exposure day. This approach enables highly-resolved insights into duration-driven effect heterogeneity while ensuring model stability through information sharing. Through simulations, we demonstrate that the EDVCM out-performs conventional approaches in terms of both effect estimation and uncertainty quantification. We apply the EDVCM to nationwide, multi-decade Medicare claims data linked with high-resolution flood exposure measures to investigate duration-driven heterogeneity in flood effects on musculoskeletal system disease hospitalizations.