Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-14T04:29:26.716Z Has data issue: false hasContentIssue false
  • English
  • Français

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).
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

1. Brown, J, Doloresco Iii, F, Mylotte, JM. “Never events”: not every hospital-acquired infection is preventable. Clin Infect Dis 2009;49:743–746.Google Scholar
2. Selected Medicare hospital quality provisions under the ACA. American Association of Medical Colleges website. https://www.aamc.org/advocacy/medicare/153882/selected_medicare_hospital_quality_provisions_under_the_aca.html. Published June 8, 2010. Accessed November 6, 2014.Google Scholar
3. Barnett, AG, Beyersmann, J, Allignol, A, Rosenthal, VD, Graves, N, Wolkewitz, M. The time-dependent bias and its effect on extra length of stay due to nosocomial infection. Value Health 2011;14:381–386.Google Scholar
4. Samore, M, Harbarth, S. A Methodologically focused review of the literature in hospital epidemiology and infection control. In: Mayhill CG, ed. Hospital Epidemiology and Infection Control. Philadelphia: Lippincott Williams & Wilkins, 2004.Google Scholar
5. Beyersmann, J, Gastmeier, P, Wolkewitz, M, Schumacher, M. An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation. J Clin Epidemiol 2008;61:1216–1221.CrossRefGoogle ScholarPubMed
6. van Walraven, C, Davis, D, Forster, AJ, Wells, GA. Time-dependent bias was common in survival analyses published in leading clinical journals. J Clin Epidemiol 2004;57:672–682.Google Scholar
7. De Angelis, G, Murthy, A, Beyersmann, J, Harbarth, S. Estimating the impact of healthcare-associated infections on length of stay and costs. Clin Microbiol Infect 2010;16:1729–1735.CrossRefGoogle ScholarPubMed
8. Cosgrove, SE, Kaye, KS, Eliopoulous, GM, Carmeli, Y. Health and economic outcomes of the emergence of third-generation cephalosporin resistance in Enterobacter species. Arch Intern Med 2002;162:185–190.CrossRefGoogle ScholarPubMed
9. Jiang, Y, Viner-Brown, S, Baier, R. Burden of hospital-onset Clostridium difficile infection in patients discharged from Rhode Island hospitals, 2010–2011: application of present on admission indicators. Infect Control Hosp Epidemiol 2013;34:700–708.CrossRefGoogle ScholarPubMed
10. Sammons, JS, Localio, R, Xiao, R, Coffin, SE, Zaoutis, T. Clostridium difficile infection is associated with increased risk of death and prolonged hospitalization in children. Clin Infect Dis 2013;57:1–8.CrossRefGoogle ScholarPubMed
11. Stewart, DB, Hollenbeak, CS. Clostridium difficile colitis: factors associated with outcome and assessment of mortality at a national level. J Gastrointest Surg 2011;15:1548–1555.Google Scholar
12. Wolkewitz, M, Beyersmann, J, Gastmeier, P, Schumacher, M. Efficient risk set sampling when a time-dependent exposure is present: matching for time to exposure versus exposure density sampling. Methods Inf Med 2009;48:438–443.Google Scholar
13. Schulgen, G, Kropec, A, Kappstein, I, Daschner, F, Schumacher, M. Estimation of extra hospital stay attributable to nosocomial infections: heterogeneity and timing of events. J Clin Epidemiol 2000;53:409–417.Google Scholar
14. Roberts, RR, Scott, RD 2nd, Hota, B, et al. Costs attributable to healthcare-acquired infection in hospitalized adults and a comparison of economic methods. Med Care 2010;48:1026–1035.CrossRefGoogle Scholar
15. De Angelis, G, Allignol, A, Murthy, A, et al. Multistate modelling to estimate the excess length of stay associated with meticillin-resistant Staphylococcus aureus colonisation and infection in surgical patients. J Hosp Infect 2011;78:86–91.Google Scholar
16. Wolkewitz, M, Allignol, A, Harbarth, S, de Angelis, G, Schumacher, M, Beyersmann, J. Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias. J Clin Epidemiol 2012;65:1171–1180.Google Scholar
17. Macedo-Vinas, M, De Angelis, G, Rohner, P, et al. Burden of meticillin-resistant Staphylococcus aureus infections at a Swiss University hospital: excess length of stay and costs. J Hosp Infect 2013;84:132–137.CrossRefGoogle Scholar
18. Schumacher, M, Allignol, A, Beyersmann, J, Binder, N, Wolkewitz, M. Hospital-acquired infections—appropriate statistical treatment is urgently needed! Int J Epidemiol 2013;42:1502–1508.Google Scholar
19. Vrijens, F, Hulstaert, F, Van de Sande, S, Devriese, S, Morales, I, Parmentier, Y. Hospital-acquired, laboratory-confirmed bloodstream infections: linking national surveillance data to clinical and financial hospital data to estimate increased length of stay and healthcare costs. J Hosp Infect 2010;75:158–162.CrossRefGoogle ScholarPubMed
20. Vrijens, F, Hulstaert, F, Devriesse, S, van de Sande, S. Hospital-acquired infections in Belgian acute-care hospital: an estimation of their global impact on mortality, length of stay and healthcare costs. Epidemiol Infect 2012;140:126–136.Google Scholar
21. Suissa, S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol 2008;167:492–499.CrossRefGoogle ScholarPubMed
22. Samore, MH, Shen, S, Greene, T, et al. A simulation-based evaluation of methods to estimate the impact of an adverse event on hospital length of stay. Med Care 2007;45:S108–S115.CrossRefGoogle ScholarPubMed
23. Fact sheets: CMS final rule to improve quality of care during hospital inpatient stays. Centers for Medicare and Medicaid Services website. http://www.cms.gov/newsroom/mediareleasedatabase/fact-sheets/2013-fact-sheets-items/2013-08-02-3.html. Published 2013. Accessed November 7, 2014.Google Scholar
24. McIntyre, LK, Warner, KJ, Nester, TA, Nathens, AB. The incidence of post-discharge surgical site infection in the injured patient. J Trauma 2009;66:407–410.Google Scholar
25. Sands, K, Vineyard, G, Platt, R. Surgical site infections occurring after hospital discharge. J Infect Dis 1996;173:963–970.Google Scholar
26. Chang, HT, Krezolek, D, Johnson, S, Parada, JP, Evans, CT, Gerding, DN. Onset of symptoms and time to diagnosis of Clostridium difficile-associated disease following discharge from an acute care hospital. Infect Control Hosp Epidemiol 2007;28:926–931.Google Scholar
27. Dial, S, Kezouh, A, Dascal, A, Barkun, A, Suissa, S. Patterns of antibiotic use and risk of hospital admission because of Clostridium difficile infection. CMAJ 2008;179:767–772.Google Scholar
28. Dubberke, ER, McMullen, KM, Mayfield, JL, et al. Hospital-associated Clostridium difficile infection: is it necessary to track community-onset disease? Infect Control Hosp Epidemiol 2009;30:332–337.Google Scholar
29. Palmore, TN, Sohn, S, Malak, SF, Eagan, J, Sepkowitz, KA. Risk factors for acquisition of Clostridium difficile-associated diarrhea among outpatients at a cancer hospital. Infect Control Hosp Epidemiol 2005;26:680–684.CrossRefGoogle Scholar