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Kurz, C.F. ; Hatfield, L.A.*

Identifying and interpreting subgroups in health care utilization data with count mixture regression models.

Stat. Med. 38, 4423-4435 (2019)
Postprint DOI PMC
Open Access Green
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter 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
Zeitschrift Statistics in Medicine
Quellenangaben Band: 38, Heft: 22, Seiten: 4423-4435 Artikelnummer: , Supplement: ,
Verlag Wiley
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Health Economics and Health Care Management (IGM)