Inpatient length of stay: a finite mixture modeling analysis

Abstract

Length of stay (LOS) in hospital for inpatient treatment is a measure of crucial recovery time. Using nationwide data on inpatient healthcare in India, a three-component finite mixture negative binomial model was found to provide a reasonable fit to the heterogeneous LOS distribution. Associated risk factors for short-stay, medium-stay and long-stay subgroups were identified from the respective negative binomial components. In addition, significant heterogeneities within each group were also found.

Factors affecting length of stay in teaching hospitals of a middle-income country

“…This could be explained by the fact that one of the hospitals is only active in gynecology and obstetrics, and most of deliveries and female surgeries are performed there. However, the Singh study in India showed that gender has an influence on LOS (16). Among different age groups, patients older than 40 years had the longest stay, and age groups between 20 to 30 years old had the shortest stay, so patient age influences LOS.…”

On the performance ofL2Eestimation in modelling heterogeneous count responses with extreme values

“…Furthermore, for the LOS data, we have made Downloaded by [North Dakota State University] at 10:44 13 October 2014 a compelling argument that the L 2 E-based two-component Poisson mixture regression model provides a better overall fit than those based on the MLE and MHDE. Since the L 2 E methodology is not distribution-specific, it can be used in conjunction with a negative binomial mixture regression to analyse other count data sets with extreme values arising in healthcare studies, for example, [4,5,7,8] and in other contexts, for example, the automobile crash data [22].…”

“…Despite attempts to manage the LOS for frequently performed medical procedures, many service providers cannot achieve the target range allowed by the managed care system. Consequently, as an important component in the consumption of hospital resources, LOS in hospital has drawn considerable attention in the healthcare literature; see, for instance, [6][7][8]17]. The LOS also provides important information for health planning and formation of payment policy [18][19][20]; hence, it is a key performance indicator for hospital management and a key measure of hospital efficiency.…”

“…For example, the count data on number of office-based physician visits [4] and the LOS data [6][7][8] contain a fraction of extreme values. These extreme values cannot be immediately judged as outliers because one plausibly can have a large number of doctor visits in a year, for example, if one is on dialysis.…”

Factors affecting the length of stay after total hip replacement; a retrospective analysis

“…All other age groups do not have significant difference. The reason for increased LOS along with age could be the presence of chronic disease in older ages, which required a longer hospital stay [21] . This study also shows that gender has effect on LOS which is consistent with the findings of our study.…”