A Two-Compartment Mixed-Effects Gamma Regression Model for Quantifying Between-Unit Variability in Length of Stay among Children Admitted to Intensive Care

Lahn Straney, Archie Clements, Jan Alexander, Anthony Slater

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Objective To quantify between-unit variability in mean length of stay (LoS) between intensive care units (ICUs) after adjusting for differences in case mix using a method that does not require arbitrary trimming of data. Setting An analysis of registry data from pediatric ICUs (PICUs) in Australia and New Zealand. Study Design The relationships between patient LoS and associated patient factors were modeled as a log-linear function of the covariates using two gamma distributions. The predicted distribution is estimated as a weighted average of the two distributions where the relative weighting is conditional on the patient's elective status. Data Collection Data for 12,763 admissions submitted to the Australian and New Zealand Paediatric Intensive Care Registry from the eight dedicated PICUs in Australia and New Zealand in 2007 and 2008. Principal Findings The two distributions of the mixture model accurately described the distribution of short- and long-stay patients in ICUs. After adjusting for patient case mix, several sites had a statistically significant effect on patient LoS. Conclusion The two-compartment model characterizes ICU LoS for short- and long-stay patients more effectively than a single-compartment model. There is significant site-level variation in the LoS among children admitted to ICUs in Australia and New Zealand. Differences in the site-level variation between short- and long-stay patients indicate differences in discharge practice.

Original languageEnglish
Pages (from-to)2190-2203
Number of pages14
JournalHealth Services Research
Volume47
Issue number6
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes

Keywords

  • intensive care
  • length of stay
  • pediatrics
  • Quality indicators
  • risk adjustment

Cite this

@article{53298a1ec83c49c4a49b353718f153cd,
title = "A Two-Compartment Mixed-Effects Gamma Regression Model for Quantifying Between-Unit Variability in Length of Stay among Children Admitted to Intensive Care",
abstract = "Objective To quantify between-unit variability in mean length of stay (LoS) between intensive care units (ICUs) after adjusting for differences in case mix using a method that does not require arbitrary trimming of data. Setting An analysis of registry data from pediatric ICUs (PICUs) in Australia and New Zealand. Study Design The relationships between patient LoS and associated patient factors were modeled as a log-linear function of the covariates using two gamma distributions. The predicted distribution is estimated as a weighted average of the two distributions where the relative weighting is conditional on the patient's elective status. Data Collection Data for 12,763 admissions submitted to the Australian and New Zealand Paediatric Intensive Care Registry from the eight dedicated PICUs in Australia and New Zealand in 2007 and 2008. Principal Findings The two distributions of the mixture model accurately described the distribution of short- and long-stay patients in ICUs. After adjusting for patient case mix, several sites had a statistically significant effect on patient LoS. Conclusion The two-compartment model characterizes ICU LoS for short- and long-stay patients more effectively than a single-compartment model. There is significant site-level variation in the LoS among children admitted to ICUs in Australia and New Zealand. Differences in the site-level variation between short- and long-stay patients indicate differences in discharge practice.",
keywords = "intensive care, length of stay, pediatrics, Quality indicators, risk adjustment",
author = "Lahn Straney and Archie Clements and Jan Alexander and Anthony Slater",
year = "2012",
month = "12",
doi = "10.1111/j.1475-6773.2012.01421.x",
language = "English",
volume = "47",
pages = "2190--2203",
journal = "Health Services Research",
issn = "0017-9124",
publisher = "Wiley-Blackwell",
number = "6",

}

A Two-Compartment Mixed-Effects Gamma Regression Model for Quantifying Between-Unit Variability in Length of Stay among Children Admitted to Intensive Care. / Straney, Lahn; Clements, Archie; Alexander, Jan; Slater, Anthony.

In: Health Services Research, Vol. 47, No. 6, 12.2012, p. 2190-2203.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A Two-Compartment Mixed-Effects Gamma Regression Model for Quantifying Between-Unit Variability in Length of Stay among Children Admitted to Intensive Care

AU - Straney, Lahn

AU - Clements, Archie

AU - Alexander, Jan

AU - Slater, Anthony

PY - 2012/12

Y1 - 2012/12

N2 - Objective To quantify between-unit variability in mean length of stay (LoS) between intensive care units (ICUs) after adjusting for differences in case mix using a method that does not require arbitrary trimming of data. Setting An analysis of registry data from pediatric ICUs (PICUs) in Australia and New Zealand. Study Design The relationships between patient LoS and associated patient factors were modeled as a log-linear function of the covariates using two gamma distributions. The predicted distribution is estimated as a weighted average of the two distributions where the relative weighting is conditional on the patient's elective status. Data Collection Data for 12,763 admissions submitted to the Australian and New Zealand Paediatric Intensive Care Registry from the eight dedicated PICUs in Australia and New Zealand in 2007 and 2008. Principal Findings The two distributions of the mixture model accurately described the distribution of short- and long-stay patients in ICUs. After adjusting for patient case mix, several sites had a statistically significant effect on patient LoS. Conclusion The two-compartment model characterizes ICU LoS for short- and long-stay patients more effectively than a single-compartment model. There is significant site-level variation in the LoS among children admitted to ICUs in Australia and New Zealand. Differences in the site-level variation between short- and long-stay patients indicate differences in discharge practice.

AB - Objective To quantify between-unit variability in mean length of stay (LoS) between intensive care units (ICUs) after adjusting for differences in case mix using a method that does not require arbitrary trimming of data. Setting An analysis of registry data from pediatric ICUs (PICUs) in Australia and New Zealand. Study Design The relationships between patient LoS and associated patient factors were modeled as a log-linear function of the covariates using two gamma distributions. The predicted distribution is estimated as a weighted average of the two distributions where the relative weighting is conditional on the patient's elective status. Data Collection Data for 12,763 admissions submitted to the Australian and New Zealand Paediatric Intensive Care Registry from the eight dedicated PICUs in Australia and New Zealand in 2007 and 2008. Principal Findings The two distributions of the mixture model accurately described the distribution of short- and long-stay patients in ICUs. After adjusting for patient case mix, several sites had a statistically significant effect on patient LoS. Conclusion The two-compartment model characterizes ICU LoS for short- and long-stay patients more effectively than a single-compartment model. There is significant site-level variation in the LoS among children admitted to ICUs in Australia and New Zealand. Differences in the site-level variation between short- and long-stay patients indicate differences in discharge practice.

KW - intensive care

KW - length of stay

KW - pediatrics

KW - Quality indicators

KW - risk adjustment

UR - http://www.scopus.com/inward/record.url?scp=84869086932&partnerID=8YFLogxK

U2 - 10.1111/j.1475-6773.2012.01421.x

DO - 10.1111/j.1475-6773.2012.01421.x

M3 - Article

VL - 47

SP - 2190

EP - 2203

JO - Health Services Research

JF - Health Services Research

SN - 0017-9124

IS - 6

ER -