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

2026-01-26

AI Summary

This study addresses the neglect of heterogeneity in the health impacts of flood duration and the lack of effective methods for modeling exposure duration. We propose the Exposure Duration–Varying Coefficient Model (EDVCM), which integrates a regional self-matching design with conditional Poisson regression and, for the first time, incorporates a two-dimensional Gaussian process prior to share information across both exposure days and flood duration. This approach enables high-resolution, stable estimation of heterogeneous effects while effectively controlling for both time-invariant and time-varying confounders within a Bayesian framework. Simulations demonstrate that EDVCM outperforms conventional methods in both effect estimation accuracy and uncertainty quantification. Empirical analysis reveals significant heterogeneity in the association between flood duration and hospitalization risk for musculoskeletal disorders.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

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.

Problem

• flood duration
• health impacts
• effect heterogeneity
• exposure duration
• flood-related health

Innovation

• varying-coefficient model
• exposure duration
• flood health impacts
• Bayesian hierarchical modeling
• Gaussian process prior