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

Manoukian, Sarkis; Stewart, Sally; Dancer, Stephanie; Graves, Nicholas; Mason, Helen; McFarland, Agi; Robertson, Chris; Reilly, Jacqui

Abstract

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.

Citation

Manoukian, S., Stewart, S., Dancer, S., Graves, N., Mason, H., McFarland, A., Robertson, C., & 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

• Journal Article Type: Review
• Acceptance Date: Jun 5, 2018
• Online Publication Date: Jun 11, 2018
• Publication Date: Jun 11, 2018
• Deposit Date: Jun 13, 2018
• Publicly Available Date: Jun 12, 2019
• Journal: Journal of Hospital Infection
• Print ISSN: 0195-6701
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1016/j.jhin.2018.06.003
• Keywords: Microbiology (medical); Infectious Diseases; General Medicine
• Public URL: http://researchrepository.napier.ac.uk/Output/1203231
• Contract Date: Jun 14, 2018