Document Type

Article

Publication Date

2012

Publication Details

This article was originally published as:

Mitchell, B., & Gardner, A. (2012). Prolongation of length of stay and Clostridium difficile infection: A review of the methods used to examine length of stay due to healthcare assoicated infections. Antimicrobial Resistance and Infection Control, 114 doi: 10.1186/2047-2994-1-14

ISSN: 2047-2994

ANZSRC / FoR Code

060501 Bacteriology| 060502 Infectious Agents| 111099 Nursing not elsewhere classified| 111799 Public Health and Health Services not elsewhere classified

Abstract

BACKGROUND: It is believed that Clostridium difficile infection (CDI) contributes to a prolongation of length of stay (LOS). Recent literature suggests that models previously used to determine LOS due to infection have overestimated LOS, compared to newer statistical models. The purpose of this review is to understand the impact that CDI has on LOS and in doing so, describe the methodological approaches used.

AIM: First, to investigate and describe the reported prolongation of LOS in hospitalised patients with CDI. Second, to describe the methodologies used for determining excess LOS.

METHODS: An integrative review method was used. Papers were reviewed and analysed individually and themes were combined using integrative methods.

RESULTS: Findings from all studies suggested that CDI contributes to a longer LOS in hospital. In studies that compared persons with and without CDI, the difference in the LOS between the two groups ranged from 2.8days to 16.1days. Potential limitations with data analysis were identified, given that no study fully addressed the issue of a time-dependent bias when examining the LOS. Recent literature suggests that a multi-state model should be used to manage the issue of time-dependent bias.

CONCLUSION: Studies examining LOS attributed to CDI varied considerably in design and data collected. Future studies examining LOS related to CDI and other healthcare associated infections should consider capturing the timing of infection in order to be able to employ a multi-state model for data analysis.

Comments

At the time of writing Brett Mitchell was affiliated with Australian Catholic University.

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