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Prolongation of length of stay and Clostridium difficile infection: a review of the methods used to examine length of stay due to healthcare associated infections

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Author(s): Mitchell Brett G | Gardner Anne

Journal: Antimicrobial Resistance and Infection Control
ISSN 2047-2994

Volume: 1;
Issue: 1;
Start page: 14;
Date: 2012;
Original page

Keywords: Clostridium difficile infection | Clostridium difficile associated diarrhoea | Cost | Healthcare associated infection | Length of stay | Time dependent bias

ABSTRACT
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.

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