title: The Magnitude of Time-Dependent Bias in the Estimation of Excess Length of Stay Attributable to Healthcare-Associated Infections.
abstract: BACKGROUND Estimates of the excess length of stay (LOS) attributable to healthcare-associated infections (HAIs) in which total LOS of patients with and without HAIs are biased because of failure to account for the timing of infection. Alternate methods that appropriately treat HAI as a time-varying exposure are multistate models and cohort studies, which match regarding the time of infection. We examined the magnitude of this time-dependent bias in published studies that compared different methodological approaches. METHODS We conducted a systematic review of the published literature to identify studies that report attributable LOS estimates using both total LOS (time-fixed) methods and either multistate models or matching patients with and without HAIs using the timing of infection. RESULTS Of the 7 studies that compared time-fixed methods to multistate models, conventional methods resulted in estimates of the LOS to HAIs that were, on average, 9.4 days longer or 238% greater than those generated using multistate models. Of the 5 studies that compared time-fixed methods to matching on timing of infection, conventional methods resulted in estimates of the LOS to HAIs that were, on average, 12.6 days longer or 139% greater than those generated by matching on timing of infection. CONCLUSION Our results suggest that estimates of the attributable LOS due to HAIs depend heavily on the methods used to generate those estimates. Overestimation of this effect can lead to incorrect assumptions of the likely cost savings from HAI prevention measures.
authors.affiliation: 1Veterans Affairs Salt Lake City Health Care System,Salt Lake City,Utah,United States.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Nelson
authors.affiliation: 1Veterans Affairs Salt Lake City Health Care System,Salt Lake City,Utah,United States.
authors.forename: Scott D
authors.initials: SD
authors.lastname: Nelson
authors.affiliation: 1Veterans Affairs Salt Lake City Health Care System,Salt Lake City,Utah,United States.
authors.forename: Karim
authors.initials: K
authors.lastname: Khader
authors.affiliation: 4Iowa City Veterans Affairs Health Care System,Iowa City,Iowa,United States.
authors.forename: Eli L
authors.initials: EL
authors.lastname: Perencevich
authors.affiliation: 4Iowa City Veterans Affairs Health Care System,Iowa City,Iowa,United States.
authors.forename: Marin L
authors.initials: ML
authors.lastname: Schweizer
authors.affiliation: 1Veterans Affairs Salt Lake City Health Care System,Salt Lake City,Utah,United States.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Rubin
authors.affiliation: 6School of Public Health and Social Work,Queensland University of Technology,Brisbane,Australia.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Graves
authors.affiliation: 7Infection Control Program, University of Geneva Hospitals and Faculty of Medicine,Geneva,Switzerland.
authors.forename: Stephan
authors.initials: S
authors.lastname: Harbarth
authors.affiliation: 1Veterans Affairs Salt Lake City Health Care System,Salt Lake City,Utah,United States.
authors.forename: Vanessa W
authors.initials: VW
authors.lastname: Stevens
authors.affiliation: 1Veterans Affairs Salt Lake City Health Care System,Salt Lake City,Utah,United States.
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
mesh_terms: D015982:Bias; D003428:Cross Infection / Q000453:epidemiology*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D013997:Time Factors