Estimating excess length of stay due to healthcare-associated infections: a systematic review and meta-analysis of statistical methodology. Issue 2 (October 2018) - Manoukian, S.; Stewart, S.; Dancer, S.; Graves, N.; Mason, H.; McFarland, A.; Robertson, C.; Reilly, J.
Summary: Background: Healthcare-associated infection (HCAI) affects millions of patients worldwide. HCAI 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 HCAI is essential to ensure that 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, HCAI; 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 categorized as time-fixed or time-varying, with the former exhibiting time-dependent bias. Two examples of meta-analysis were used 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 HCAI. 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. Conclusion: 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.