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

Menglu Liang (), Zheng Li, Lijun Zhang and Ming Wang
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Menglu Liang: Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA
Zheng Li: Novartis Pharmaceuticals, New Jersey, NJ 07936, USA
Lijun Zhang: Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
Ming Wang: Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA

Stats, 2024, vol. 7, issue 4, 1-13

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.

Keywords: case-crossover (CCO) design; environmental epidemiology; Bayesian inference; multivariate conditional autoregressive priors; claims data (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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