Work

A Spatial–Temporal Bayesian Model for a Case-Crossover Design with Application to Extreme Heat and Claims Data

Year: 2024

Type: article

Abstract: Epidemiological approaches for examining human health responses to environmental exposures in observational studies frequently address confounding by employing advanced matching techniques and statistical methods grounded in conditional likelihood. This study incorporates a recently developed Bayesian hierarchical spatiotemporal model within a conditional logistic regression framework to capture the heterogeneous effects of environmental exposures in a case-crossover (CCO) design. Spatial and temporal dependencies are modeled through random effects incorporating multivariate conditional autoregressive priors. Flexible frailty structures are introduced to explore strategies for managing temporal variables. Parameter estimation and inference are conducted using a Monte Carlo Markov chain method within a Bayesian framework. Model fit and optimal model selection are evaluated using the deviance information criterion. Simulations assess and compare model performance across various scenarios. Finally, the approach is illustrated with workers’ compensation claims data from New York and Florida to examine spatiotemporal heterogeneity in hospitalization rates related to heat prostration.

Source: Stats

DOI: 10.3390/stats7040080

Authors: Menglu Liang, Zheng Li, Lijun Zhang, Ming Wang

Institutions: University of Maryland, College Park, Novartis (United States), Case Western Reserve University

Language: English

Topic: Statistical Methods in Epidemiology

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,819