Research Output

Estimating excess length of stay due to healthcare-associated infections: A systematic review and meta-analysis of statistical methodology

  Background
Healthcare-associated infection (HAI) affects millions of patients worldwide. HAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HAI is essential to ensure we invest in cost-effective infection prevention and control (IPC) measures.
Aim
To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HAI; to highlight and discuss potential biases of all statistical approaches.
Methods
A systematic review from 1997 to April 2017 was conducted in PUBMED, CINAHL, PROQUEST and ECONLIT databases. Studies were quality assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorised into time-fixed or time-varying with the former exhibiting time-dependent bias. We use two examples of meta-analysis to illustrate how estimates of excess LOS differ between different studies.
Findings
Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms and susceptibilities.
Conclusions
Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.

  • Type:

    Review

  • Date:

    11 June 2018

  • Publication Status:

    Published

  • Publisher

    Elsevier BV

  • DOI:

    10.1016/j.jhin.2018.06.003

  • Cross Ref:

    S0195670118303177

  • ISSN:

    0195-6701

  • Library of Congress:

    QR Microbiology

  • Dewey Decimal Classification:

    579 Microorganisms, fungi & algae

  • Funders:

    NHS Health Scotland

Citation

Manoukian, S., Stewart, S., Dancer, S., Graves, N., Mason, H., McFarland, A., …Reilly, J. (2018). Estimating excess length of stay due to healthcare-associated infections: A systematic review and meta-analysis of statistical methodology. Journal of Hospital Infection, https://doi.org/10.1016/j.jhin.2018.06.003

Authors

Keywords

Microbiology (medical); Infectious Diseases; General Medicine

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    Please cite this article as: Manoukian S, Stewart S, Dancer S, Graves N, Mason H, McFarland A, Robertson C, Reilly J, Estimating excess length of stay due to healthcare-associated infections: A systematic review and meta-analysis of statistical methodology This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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