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The Magnitude of Time-Dependent Bias in the Estimation of Excess Length of Stay Attributable to Healthcare-Associated Infections

Published online by Cambridge University Press:  04 June 2015

Richard E. Nelson ,
Scott D. Nelson ,
Karim Khader ,
Eli L. Perencevich ,
Marin L. Schweizer ,
Michael A. Rubin ,
Nicholas Graves ,
Stephan Harbarth ,
Vanessa W. Stevens  and
Matthew H. Samore
Richard E. Nelson*
Affiliation:
Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States
Scott D. Nelson
Affiliation:
Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, Utah, United States
Karim Khader
Affiliation:
Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States
Eli L. Perencevich
Affiliation:
Iowa City Veterans Affairs Health Care System, Iowa City, Iowa, United States Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
Marin L. Schweizer
Affiliation:
Iowa City Veterans Affairs Health Care System, Iowa City, Iowa, United States Department of Internal Medicine, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States
Michael A. Rubin
Affiliation:
Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States
Nicholas Graves
Affiliation:
School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
Stephan Harbarth
Affiliation:
Infection Control Program, University of Geneva Hospitals and Faculty of Medicine, Geneva, Switzerland
Vanessa W. Stevens
Affiliation:
Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States Department of Pharmacotherapy, University of Utah College of Pharmacy, Salt Lake City, Utah, United States
Matthew H. Samore
Affiliation:
Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, United States Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, United States
*
Address correspondence to Richard E. Nelson, PhD, 500 Foothill Blvd, Salt Lake City, UT 84148 (richard.nelson@utah.edu).
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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.

Infect. Control Hosp. Epidemiol. 2015;36(9):1089–1094

Type
Review Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 9 , September 2015 , pp. 1089 - 1094
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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Footnotes

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

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