title: Reducing Time-dependent Bias in Estimates of the Attributable Cost of Health Care-associated Methicillin-resistant Staphylococcus aureus Infections: A Comparison of Three Estimation Strategies.
abstract: BACKGROUND
  Previous estimates of the excess costs due to health care-associated infection (HAI) have scarcely addressed the issue of time-dependent bias.
  
  
  OBJECTIVE
  We examined time-dependent bias by estimating the health care costs attributable to an HAI due to methicillin-resistant Staphylococcus aureus (MRSA) using a unique dataset in the Department of Veterans Affairs (VA) that makes it possible to distinguish between costs that occurred before and after an HAI. In addition, we compare our results to those from 2 other estimation strategies.
  
  
  METHODS
  Using a historical cohort study design to estimate the excess predischarge costs attributable to MRSA HAIs, we conducted 3 analyses: (1) conventional, in which costs for the entire inpatient stay were compared between patients with and without MRSA HAIs; (2) post-HAI, which included only costs that occurred after an infection; and (3) matched, in which costs for the entire inpatient stay were compared between patients with an MRSA HAI and subset of patients without an MRSA HAI who were matched based on the time to infection.
  
  
  RESULTS
  In our post-HAI analysis, estimates of the increase in inpatient costs due to MRSA HAI were $12,559 (P<0.0001) and $24,015 (P<0.0001) for variable and total costs, respectively. The excess variable and total cost estimates were 33.7% and 31.5% higher, respectively, when using the conventional methods and 14.6% and 11.8% higher, respectively, when using matched methods.
  
  
  CONCLUSIONS
  This is the first study to account for time-dependent bias in the estimation of incremental per-patient health care costs attributable to HAI using a unique dataset in the VA. We found that failure to account for this bias can lead to overestimation of these costs. Matching on the timing of infection can reduce this bias substantially.
authors.affiliation: *Veterans Affairs Salt Lake City Health Care System †Department of Internal Medicine, University of Utah School of Medicine ‡Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, UT §Veterans Affairs Puget Sound Health Care System ∥Department of Health Services, University of Washington, Seattle, WA ¶School of Public Health, Queensland University, Brisbane, Australia #Lexington Veterans Affairs Medical Center **Department of Internal Medicine, University of Kentucky, Lexington, KY.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Nelson
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.forename: Makoto
authors.initials: M
authors.lastname: Jones
authors.forename: Tom
authors.initials: T
authors.lastname: Greene
authors.forename: Vanessa W
authors.initials: VW
authors.lastname: Stevens
authors.forename: Chuan-Fen
authors.initials: CF
authors.lastname: Liu
authors.forename: Nicholas
authors.initials: N
authors.lastname: Graves
authors.forename: Martin F
authors.initials: MF
authors.lastname: Evans
authors.forename: Michael A
authors.initials: MA
authors.lastname: Rubin
mesh_terms: D000368:Aged; D015982:Bias; D003428:Cross Infection / Q000191:economics* / Q000517:prevention & control; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006787:Hospitals, Veterans / Q000191:economics*; D006801:Humans; D008297:Male; D055624:Methicillin-Resistant Staphylococcus aureus; D008875:Middle Aged; D015233:Models, Statistical; D013203:Staphylococcal Infections / Q000191:economics* / Q000517:prevention & control; D013997:Time Factors; D014481:United States