Richard E Nelson1, Matthew H Samore, Makoto Jones, Tom Greene, Vanessa W Stevens, Chuan-Fen Liu, Nicholas Graves, Martin F Evans, Michael A Rubin. 1. *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.
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.
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: Richard E Nelson; Vanessa W Stevens; Karim Khader; Makoto Jones; Matthew H Samore; Martin E Evans; R Douglas Scott; Rachel B Slayton; Marin L Schweizer; Eli L Perencevich; Michael A Rubin Journal: Am J Prev Med Date: 2016-05 Impact factor: 5.043
Authors: Richard E Nelson; Vanessa W Stevens; Makoto Jones; Karim Khader; Marin L Schweizer; Eli N Perencevich; Michael A Rubin; Matthew H Samore Journal: Antimicrob Agents Chemother Date: 2018-10-24 Impact factor: 5.191
Authors: Jenine R Leal; John Conly; Elizabeth Ann Henderson; Braden J Manns Journal: Antimicrob Resist Infect Control Date: 2017-06-02 Impact factor: 4.887
Authors: Marta Riu; Pietro Chiarello; Roser Terradas; Maria Sala; Enric Garcia-Alzorriz; Xavier Castells; Santiago Grau; Francesc Cots Journal: Medicine (Baltimore) Date: 2017-04 Impact factor: 1.889
Authors: Thomas Heister; Martin Wolkewitz; Philip Hehn; Jan Wolff; Markus Dettenkofer; Hajo Grundmann; Klaus Kaier Journal: Cost Eff Resour Alloc Date: 2019-08-01