Faculty Collaboration Database

Prediction of Hospitalization Length. Quantile Regression Predicts Hospitalization Length and its Related Factors better than Available Methods. Ann Ig 2021;33(2):177-188

Date

02/12/2021

Pubmed ID

33570089

DOI

10.7416/ai.2021.2423

Scopus ID

2-s2.0-85101437144 (requires institutional sign-in at Scopus site)   2 Citations

Abstract

BACKGROUND: Length of hospitalization is one of the most important indices in evaluating the efficiency and effectiveness of hospitals and the optimal use of resources. Identifying these indices' associated factors could be useful. This study aimed to investigate effective factors of the length of hospitalization in Zanjan teaching hospitals in 2018 using the Quantile regression model.

METHODS: This cross-sectional study was conducted on 1,031 patients. The study population consisted of patients in orthopaedic, pediatric, internal, surgical and intensive care units. The samples were selected by multistage random sampling. The information was collected by a pre-designed checklist. The Quantile regression model and ordinary regression model were performed on the data.

RESULTS: Of the 1,031 patients admitted to different units, 624 (60.52%) were male. Mean and standard deviation of length of hospitalization for men, women and all patients were 7.25±5.48, 8.09±6.35 and 7.58±5.83 respectively. For 90 percent of patients the length of hospitalization was less than 14 days. Twenty-five percent of patients in pediatric and orthopedic units and ten percent of patients in internal and surgery units were hospitalized less than three days. In all quantiles, patients' length of hospitalization in surgery and orthopedic units, compared to the intensive care unit, and patients hospitalized for injuries and poisonings compared to other causes, had a statistically significant difference. (p<0.05).

CONCLUSION: Due to the heterogeneity (skewness) of the length of hospital stay in different units of the hospital, the quantile regression model predicts the length of hospital stay more precisely than the ordinary regression models.

Author List

Kazemi M, Nazari S, Motamed N, Arsang-Jang S, Fallah R

Author

Shahram Arsang-Jang Postdoctoral Fellow in the Medicine department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Child
Cross-Sectional Studies
Female
Hospitalization
Hospitals, Teaching
Humans
Intensive Care Units
Length of Stay
Male