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A hierarchical Poisson mixture regression model to analyse maternity length of hospital stay
Author(s) -
Wang K.,
Yau Kelvin K. W.,
Lee Andy H.
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1307
Subject(s) - poisson regression , poisson distribution , statistics , econometrics , random effects model , generalized linear model , multilevel model , generalized linear mixed model , mixed model , proxy (statistics) , variance (accounting) , mixture model , linear regression , regression analysis , mathematics , computer science , medicine , environmental health , economics , population , meta analysis , accounting
Inpatient length of stay (LOS) is often considered as a proxy of hospital resource consumption. Using statewide obstetrical delivery data, a two‐component Poisson mixture model provides a reasonable fit to the heterogeneous LOS distribution. Adopting the generalized linear mixed model (GLMM) approach, random effects are introduced to the two‐component Poisson mixture regression model to account for the inherent correlation of patients clustered within hospitals. An EM algorithm is developed for the joint estimation of regression coefficients and variance component parameters. Related diagnostic measures for assessing model adequacy are derived. When applying the method to analyse maternity LOS, appropriate risk factors for the short‐stay and long‐stay subgroups can be identified from the respective Poisson components. In addition, predicted random hospital effects enable the comparison of relative efficiencies among hospitals after adjustment for patient case‐mix and health provision characteristics. Copyright © 2002 John Wiley & Sons, Ltd.