A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia.

Autor: Mutiso F; Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA., Pearce JL; Division of Environmental Health, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA., Benjamin-Neelon SE; Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Mueller NT; Department of Pediatrics Section of Nutrition, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA., Li H; Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, California, USA., Neelon B; Division of Biostatistics, Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA.; Charleston Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson VA Medical Center, Charleston, South Carolina, USA.
Jazyk: angličtina
Zdroj: Biometrical journal. Biometrische Zeitschrift [Biom J] 2024 Jul; Vol. 66 (5), pp. e202300182.
DOI: 10.1002/bimj.202300182
Abstrakt: Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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