OBJECTIVES: The purpose of this study was to identify factors related to pregnancy and childbirth that might be predictive of a patient's length of stay after delivery and to model variations in length of stay. METHODS: California hospital discharge data on maternity patients (n = 499,912) were analyzed. Hierarchical linear modeling was used to adjust for patient case mix and hospital characteristics and to account for the dependence of outcome variables within hospitals. RESULTS: Substantial variation in length of stay among patients was observed. The variation was mainly attributed to delivery type (vaginal or cesarean section), the patient's clinical risk factors, and severity of complications (if any). Furthermore, hospitals differed significantly in maternity lengths of stay even after adjustment for patient case mix. CONCLUSIONS: Developing risk-adjusted models for length of stay is a complex process but is essential for understanding variation. The hierarchical linear model approach described here represents a more efficient and appropriate way of studying interhospital variations than the traditional regression approach.
OBJECTIVES: The purpose of this study was to identify factors related to pregnancy and childbirth that might be predictive of a patient's length of stay after delivery and to model variations in length of stay. METHODS: California hospital discharge data on maternity patients (n = 499,912) were analyzed. Hierarchical linear modeling was used to adjust for patient case mix and hospital characteristics and to account for the dependence of outcome variables within hospitals. RESULTS: Substantial variation in length of stay among patients was observed. The variation was mainly attributed to delivery type (vaginal or cesarean section), the patient's clinical risk factors, and severity of complications (if any). Furthermore, hospitals differed significantly in maternity lengths of stay even after adjustment for patient case mix. CONCLUSIONS: Developing risk-adjusted models for length of stay is a complex process but is essential for understanding variation. The hierarchical linear model approach described here represents a more efficient and appropriate way of studying interhospital variations than the traditional regression approach.
Authors: J M Quintana; S Garcia; U Aguirre; N Gonzalez; E Arteta; A Escobar; M Bare; J A Blasco; J Martínez-Tapias Journal: Eye (Lond) Date: 2013-05-24 Impact factor: 3.775
Authors: Alyssa Troeschel; Tenbroeck Smith; Kathleen Castro; Katherine Treiman; Joseph Lipscomb; Ryan M McCabe; Steven Clauser; Eliot L Friedman; Patricia D Hegedus; Kenneth Portier Journal: Qual Life Res Date: 2016-06-23 Impact factor: 4.147