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Identifying and interpreting subgroups in health care utilization data with count mixture regression models.
Stat. Med. 38, 4423-4435 (2019)
Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures such as length of stay and spending include zero inflation, overdispersion, and skewness, all of which complicate statistical modeling. Moreover, latent subgroups of patients may have distinct patterns of utilization and relationships between that utilization and observed covariates. In this work, we apply and compare likelihood-based and parametric Bayesian mixtures of negative binomial and zero-inflated negative binomial regression models. In a simulation, we find that the Bayesian approach finds the true number of mixture components more accurately than using information criteria to select among likelihood-based finite mixture models. When we apply the models to data on hospital lengths of stay for patients with lung cancer, we find distinct subgroups of patients with different means and variances of hospital days, health and treatment covariates, and relationships between covariates and length of stay.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Bayesian Inference ; Cost Data ; Count Data ; Health Economics ; Mixture Model; Bayesian Model; Maximum-likelihood; Finite Mixture; Parameters; Diagnosis; Patterns; Version
ISSN (print) / ISBN
0277-6715
e-ISSN
1097-0258
Journal
Statistics in Medicine
Quellenangaben
Volume: 38,
Issue: 22,
Pages: 4423-4435
Publisher
Wiley
Publishing Place
111 River St, Hoboken 07030-5774, Nj Usa
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Reviewing status
Peer reviewed