title: Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits.
abstract: Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components-one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient- and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data.
authors.affiliation: Center for Health Services Research in Primary Care, Durham VAMC, USA brian.neelon@duke.edu.|Department of Biostatistics and Bioinformatics, Duke University, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Neelon
authors.affiliation: Department of Biostatistics, Emory University, USA.
authors.forename: Howard H
authors.initials: HH
authors.lastname: Chang
authors.affiliation: Department of Biostatistics, Emory University, USA.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Ling
authors.affiliation: Center for Health Services Research in Primary Care, Durham VAMC, USA.|Department of Medicine, Duke University School of Medicine, USA.|Geriatrics Research, Education and Clinical Center, Durham VAMC, USA.
authors.forename: Nicole S
authors.initials: NS
authors.lastname: Hastings
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D016010:Binomial Distribution; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D015999:Multivariate Analysis; D016012:Poisson Distribution; D012984:Software; D062211:Spatio-Temporal Analysis; D014481:United States
keywords: emergency department use; generalized Poisson distribution; hurdle model; overdispersion; spatiotemporal model; zero inflation