Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-28T16:42:22.248Z Has data issue: false hasContentIssue false
  • English
  • Français

Attributable Burden of Hospital-Onset Clostridium difficile Infection: A Propensity Score Matching Study

Published online by Cambridge University Press:  02 January 2015

Ying P. Tabak ,
Marya D. Zilberberg ,
Richard S. Johannes ,
Xiaowu Sun  and
L. Clifford McDonald
Ying P. Tabak
Affiliation:
Clinical Research, CareFusion, San Diego, California
Marya D. Zilberberg
Affiliation:
University of Massachusetts, Amherst, Massachusetts; and EviMed Research Group, LLC, Williamsburg, Massachusetts
Richard S. Johannes
Affiliation:
Clinical Research, CareFusion, San Diego, California Gastrointestinal Division, Brigham and Women's Hospitaland Harvard Medical School, Boston, Massachusetts
Xiaowu Sun
Affiliation:
Clinical Research, CareFusion, San Diego, California
L. Clifford McDonald*
Affiliation:
Centers for Disease Control and Prevention, Atlanta, Georgia
*
Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A07, Atlanta, GA 30333 ([email protected]).
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

To determine the attributable in-hospital mortality, length of stay (LOS), and cost of hospital-onset Clostridium difficile infection (HO-CDI).

Design.

Propensity score matching.

Setting.

Six Pennsylvania hospitals (2 academic centers, 1 community teaching facility, and 3 community nonteaching facilities) contributing data to a clinical research database.

Patients.

Adult inpatients between 2007 and 2008.

Methods.

We defined HO-CDI in adult inpatients as a positive C. difficile toxin assay result from a specimen collected more than 48 hours after admission and more than 8 weeks following any previous positive result. We developed an HO-CDI propensity model and matched cases with noncases by propensity score at a 1 : 3 ratio. We further restricted matching within the same hospital, within the same principal disease group, and within a similar length of lead time from admission to onset of HO-CDI.

Results.

Among 77,257 discharges, 282 HO-CDI cases were identified. The propensity score-matched rate was 90%. Compared with matched noncases, HO-CDI patients had higher mortality (11.8% vs 7.3%; P<.05), longer LOS (median [interquartile range (IQR)], 12 [9–21] vs 11 [8–17] days; P< .01), and higher cost (median [IQR], $20,804 [$ll,059-$38,429] vs $16,634 [$9,413–$30,319]; P< .01). The attributable effect of HO-CDI was 4.5% (95% confidence interval [CI], 0.2%–8.7%; P<.05) for mortality, 2.3 days (95% CI, 0.9–3.8; P<.01) for LOS, and $6,117 (95% CI, $1,659–$10,574; P<.01) for cost.

Conclusions.

Patients with HO-CDI incur additional attributable mortality, LOS, and cost burden compared with patients with similar primary clinical condition, exposure risk, lead time of hospitalization, and baseline characteristics.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 6 , June 2013 , pp. 588 - 596
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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.)

References

1.Klevens, RM, Edwards, IR, Richards, CL Jret al.Estimating health care–associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122(2):160–166.Google Scholar
2.Roberts, RR, Scott, RD IIHota, B, et al.Costs attributable to healthcare-acquired infection in hospitalized adults and a comparison of economic methods. Med Care 2010;48(11):1026–1035.Google Scholar
3.Lucado, J, Gould, C, Elixhauser, A. Clostridium difficile infections (CDI) in hospital stays, 2009. Statistical brief 124. Rockville, MD: Healthcare Cost and Utilization Project Statistical Briefs, 2012.Google ScholarPubMed
4.Dubberke, ER, Wertheimer, AI. Review of current literature on the economic burden of Clostridium difficile infection. Infect Control Hosp Epidemiol 2009;30(1):57–66.CrossRefGoogle ScholarPubMed
5.Kyne, L, Hamel, MB, Polavaram, R, Kelly, CP. Health care costs and mortality associated with nosocomial diarrhea due to Clostridium difficile. Clin Infect Dis 2002;34(3):346–353.Google Scholar
6.Dubberke, ER, Butler, AM, Reske, KA, et al.Attributable outcomes of endemic Clostridium difficile–associated disease in nonsurgical patients. Emerg Infect Dis 2008;14(7):1031–1038.CrossRefGoogle ScholarPubMed
7.Dubberke, ER, Reske, KA, Olsen, MA, McDonald, LC, Fraser, VJ. Short- and long-term attributable costs of Clostridium difficile–associated disease in nonsurgical inpatients. Clin Infect Dis 2008;46(4):497–504.CrossRefGoogle Scholar
8.O'Brien, IA, Lahue, BJ, Caro, JJ, Davidson, DM. The emerging infectious challenge of Clostridium difficile–associated disease in Massachusetts hospitals: clinical and economic consequences. Infect Control Hosp Epidemiol 2007;28(11):1219–1227.Google Scholar
9.Miller, M, Gravel, D, Mulvey, M, et al.Health care–associated Clostridium difficile infection in Canada: patient age and infecting strain type are highly predictive of severe outcome and mortality. Clin Infect Dis 2010;50(2):194–201.CrossRefGoogle ScholarPubMed
10.Lawrence, SJ, Puzniak, LA, Shadel, BN, Gillespie, KN, Kollef, MH, Mundy, LM. Clostridium difficile in the intensive care unit: epidemiology, costs, and colonization pressure. Infect Control Hosp Epidemiol 2007;28(2):123–130.Google Scholar
11.Kilgore, ML, Ghosh, K, Beavers, CM, Wong, DY, Hymel, PA JrBrossette, SE. The costs of nosocomial infections. Med Care 2008;46(1):101–104.CrossRefGoogle ScholarPubMed
12.Zilberberg, MD, Tabak, YP, Sievert, DM, et al.Using electronic health information to risk-stratify rates of Clostridium difficile infection in US hospitals. Infect Control Hosp Epidemiol 2011;32(7):649–655.CrossRefGoogle ScholarPubMed
13.Tabak, YP, Johannes, RS, Silber, JH. Using automated clinical data for risk adjustment: development and validation of six disease-specific mortality predictive models for pay-for-performance. Med Care 2007;45(8):789–805.Google Scholar
14.Tabak, YP, Sun, X, Derby, KG, Kurtz, SG, Johannes, RS. Development and validation of a disease-specific risk adjustment system using automated clinical data. Health Serv Res 2010;45:1815–1835.Google Scholar
15. Acute inpatient PPS. Centers for Medicare and Medicaid Services website. http://www.cms.hhs.gov/AcuteInpatientPPS/HIF/list.asp#TopOfPage. Accessed November 7, 2011.Google Scholar
16.Parsons, LS, ed. Reducing bias in a propensity score matched-pair sample using greedy matching techniques. In: Proceedings of the 26th Annual SAS Users Group International Conference. Long Beach, CA: SAS Institute, 2001.Google Scholar
17.Austin, PC. Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. Stat Med 2011;30(11):1292–1301.CrossRefGoogle ScholarPubMed
18.Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott-Wolters-Kluwer, 2008.Google Scholar
19.Sato, T. On the variance estimator for the Mantel-Haenszel risk difference. Biometrics 1989;45:1323–1324.Google Scholar
20.Sullivan, LM, Dukes, KA, Losina, E. Tutorial in biostatistics: an introduction to hierarchical linear modelling. Stat Med 1999;18(7):855–888.3.0.CO;2-7>CrossRefGoogle ScholarPubMed
21.Efron, B, Tibshirani, R. An Introduction to the Bootstrap. London: Chapman & Hall, 1993.Google Scholar
22.Dubberke, ER, Butler, AM, Yokoe, DS, et al.Multicenter study of surveillance for hospital-onset Clostridium difficile infection by the use of ICD-9-CM diagnosis codes. Infect Control Hosp Epidemiol 2010;31(3):262–268.CrossRefGoogle ScholarPubMed
23.Dallal, RM, Harbrecht, BG, Boujoukas, AJ, et al.Fulminant Clostridium difficile: an underappreciated and increasing cause of death and complications. Ann Surg 2002;235(3):363–372.CrossRefGoogle ScholarPubMed
24.Miller, MA, Hyland, M, Ofner-Agostini, M, Gourdeau, M, Ishak, M. Morbidity, mortality, and healthcare burden of nosocomial Clostridium difficile–associated diarrhea in Canadian hospitals. Infect Control Hosp Epidemiol 2002;23(3):137–140.CrossRefGoogle ScholarPubMed
25.Loo, VG, Poirier, L, Miller, MA, et al.A predominantly clonal multi-institutional outbreak of Clostridium difficile–associated diarrhea with high morbidity and mortality. N Engl J Med 2005;353(23):2442–2449.Google Scholar
26.Pepin, J, Valiquette, L, Cossette, B. Mortality attributable to nosocomial Clostridium difficile–associated disease during an epidemic caused by a hypervirulent strain in Quebec. CMAJ 2005;173(9):1037–1042.Google Scholar
27.Forster, AJ, Taljaard, M, Oake, N, Wilson, K, Roth, V, van Walraven, C. The effect of hospital-acquired infection with Clostridium difficile on length of stay in hospital. CMAJ 2012;184(1):37–42.Google Scholar
28.Cohen, SH, Gerding, DN, Johnson, S, et al.Clinical practice guide-lines for Clostridium difficile infection in adults: 2010 update by the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA). Infect Control Hosp Epidemiol 2010;31(5):431–455.CrossRefGoogle Scholar
29.Centers for Disease Control and Prevention. Vital signs: preventing Clostridium difficile infections. MMWR Morb Mortal Wkly Rep 2012;61(9):157–162.Google Scholar
30.Centers for Medicare and Medicaid Services. Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and FY 2012 rates; hospitals' FTE resident caps for graduate medical education payment. Final rules. Fed Regist 2011;76(160):51476–51843.Google Scholar