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

  • Ying P. Tabak (a1), Marya D. Zilberberg (a2), Richard S. Johannes (a1) (a3), Xiaowu Sun (a1) and L. Clifford McDonald (a4)...

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.

Copyright

Corresponding author

Division of Healthcare Quality Promotion, Centers for Disease Control and Prevention, 1600 Clifton Road, MS A07, Atlanta, GA 30333 (cmcdonaldl@cdc.gov).

References

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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):160166.
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):10261035.
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.
4.Dubberke, ER, Wertheimer, AI. Review of current literature on the economic burden of Clostridium difficile infection. Infect Control Hosp Epidemiol 2009;30(1):5766.
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):346353.
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):10311038.
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):497504.
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):12191227.
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):194201.
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):123130.
11.Kilgore, ML, Ghosh, K, Beavers, CM, Wong, DY, Hymel, PA JrBrossette, SE. The costs of nosocomial infections. Med Care 2008;46(1):101104.
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):649655.
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):789805.
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;45:18151835.
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.
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.
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):12921301.
18.Rothman, KJ, Greenland, S, Lash, TL. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott-Wolters-Kluwer, 2008.
19.Sato, T. On the variance estimator for the Mantel-Haenszel risk difference. Biometrics 1989;45:13231324.
20.Sullivan, LM, Dukes, KA, Losina, E. Tutorial in biostatistics: an introduction to hierarchical linear modelling. Stat Med 1999;18(7):855888.
21.Efron, B, Tibshirani, R. An Introduction to the Bootstrap. London: Chapman & Hall, 1993.
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):262268.
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):363372.
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):137140.
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):24422449.
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):10371042.
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):3742.
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):431455.
29.Centers for Disease Control and Prevention. Vital signs: preventing Clostridium difficile infections. MMWR Morb Mortal Wkly Rep 2012;61(9):157162.
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):5147651843.

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