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Automated Surveillance of Clostridium difficile Infections Using BioSense

Published online by Cambridge University Press:  02 January 2015

Stephen R. Benoit ,
L. Clifford McDonald ,
Roseanne English  and
Jerome I. Tokars
Stephen R. Benoit*
Affiliation:
Division of Emergency Preparedness and Response, National Center for Public Health Informatics, Centers for Disease Control and Prevention, Atlanta, Georgia
L. Clifford McDonald
Affiliation:
Division of Healthcare Quality Promotion, National Center for Preparedness, Detection, and Control of Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia
Roseanne English
Affiliation:
Division of Emergency Preparedness and Response, National Center for Public Health Informatics, Centers for Disease Control and Prevention, Atlanta, Georgia
Jerome I. Tokars
Affiliation:
Division of Emergency Preparedness and Response, National Center for Public Health Informatics, Centers for Disease Control and Prevention, Atlanta, Georgia
*
Centers for Disease Control and Prevention, 1600 Clifton Road NE, MS E-90, Atlanta, GA 30333 (bvy8@cdc.gov)
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Abstract

Objective.

To determine the feasibility of using electronic laboratory and admission-discharge-transfer data from BioSense, a national automated surveillance system, to apply new modified Clostridium difficile infection (CDI) surveillance definitions and calculate overall and facility-specific rates of disease.

Design.

Retrospective, multicenter cohort study.

Setting.

Thirty-four hospitals sending inpatient, emergency department, and /or outpatient data to BioSense.

Methods.

Laboratory codes and text-parsing methods were used to extract C. difficile-positive toxin assay results from laboratory data sent to BioSense during the period from January 1, 2007, through June 30, 2008; these were merged with administrative records to determine whether cases were community associated or healthcare onset, as well as patient-day data for rate calculations. A patient was classified as having hospital-onset CDI if he or she had a C. difficile toxin-positive result on a stool sample collected 3 or more days after admission and community-onset CDI if the specimen was collected less than 3 days after admission or the patient was not hospitalized.

Results.

A total of 4,585 patients from 34 hospitals in 12 states had C. difficile-positive assay results. More than half (53.0%) of the cases were community-onset, and 30.8% of these occurred in patients who were recently hospitalized. The overall rate of healthcare-onset CDI was 7.8 cases per 10,000 patient-days, with a range among facilities of 1.5-27.8 cases per 10,000 patient-days.

Conclusions.

Electronic laboratory data sent to the BioSense surveillance system were successfully used to produce disease rates of CDI comparable to those of other studies, which shows the feasibility of using electronic laboratory data to track a disease of public health importance.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 1 , January 2011 , pp. 26 - 33
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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References

1.McDonald, LC, Killgore, GE, Thompson, A, et al. An epidemic, toxin gene-variant strain of Clostridium difficile. N Engl J Med 2005;353(23): 2433–2441.CrossRefGoogle ScholarPubMed
2.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.CrossRefGoogle ScholarPubMed
3.Pépin, J, Valiquette, L, Alary, ME, et al. Clostridium difficile-associated diarrhea in a region of Quebec from 1991 to 2003: a changing pattern of disease severity. CMAJ 2004;171(5):466–472.CrossRefGoogle Scholar
4.Eaton, L. C difficile cases rising, but MRSA rates falling. BMJ 2007;335(7612):177.Google ScholarPubMed
5.Archibald, LK, Banerjee, SN, Jarvis, WR. Secular trends in hospital-acquired Clostridium difficile disease in the United States, 1987-2001. J Infect Dis 2004;189(9):1585–1589.CrossRefGoogle ScholarPubMed
6.Dallal, RM, Harbrecht, BG, Boujoukas, AJ, et al. Fulminant Clostridium difficile: an underappreciated and increasing cause of death and com-plications. Ann Surg 2002;235(3):363–372.CrossRefGoogle Scholar
7.Muto, CA, Pokrywka, M, Shutt, K, et al. A large outbreak of Clostridium difficile-associated disease with an unexpected proportion of deaths and colectomies at a teaching hospital following increased fluoroquinolone use. Infect Control Hosp Epidemiol 2005;26(3):273–280.CrossRefGoogle Scholar
8.McEllistrem, MC, Carman, RJ, Gerding, DN, Genheimer, CW, Zheng, L. A hospital outbreak of Clostridium difficile disease associated with isolates carrying binary toxin genes. Clin Infect Dis 2005;40(2):265–272.CrossRefGoogle ScholarPubMed
9.Centers for Disease Control and Prevention. Severe Clostridium difficile-associated disease in populations previously at low risk—four states, 2005. MMWR Morb Mortal Wkly Rep 2005;54(47):1201–1205.Google Scholar
10.Dial, S, Delaney, JA, Barkun, AN, Suissa, S. Use of gastric acid-suppressive agents and the risk of community-acquired Clostridium difficile-associated disease. JAMA 2005;294(23):2989–2995.CrossRefGoogle ScholarPubMed
11.McDonald, LC, Coignard, B, Dubberke, E, Song, X, Horan, T, Kutty, PK; Ad Hoc Clostridium difficile Surveillance Working Group. Recommendations for surveillance of Clostridium difficile-associated disease. Infect Control Hosp Epidemiol 2007;28(2):140–145.CrossRefGoogle ScholarPubMed
12.Buehler, JW. Review of the 2003 National Syndromic Surveillance Conference—lessons learned and questions to be answered. MMWR Morb Mortal Wkly Rep 2004;53(suppl):18–22.Google ScholarPubMed
13.Mandl, KD, Overhage, JM, Wagner, MM, et al. Implementing syndromic surveillance: a practical guide informed by the early experience. J Am Med Inform Assoc 2004;11(2);141–150.CrossRefGoogle ScholarPubMed
14.Centers for Disease Control and Prevention. Automated detection and reporting of notifiable diseases using electronic medical records versus passive surveillance?—Massachusetts, June 2006-July 2007. MMWR 2008;57(14):373–376.Google Scholar
15.Loonsk, JW. BioSense—a national initiative for early detection and quantification of public health emergencies. MMWR Morb Mortal Wkly Rep 2004;53(suppl):53–55.Google Scholar
16.Bradley, CA, Rolka, H, Walker, D, Loonsk, J. BioSense: implementation of a National Early Event Detection and Situational Awareness System. MMWR Morb Mortal Wkly Rep 2005;26:54(suppl):11–19.Google Scholar
17.Kutty, PK, Benoit, SR, Woods, CW, et al. Assessment of Clostridium difficile-associated disease surveillance definitions, North Carolina, 2005. Infect Control Hosp Epidemiol 2008;29(3): 197–202.CrossRefGoogle ScholarPubMed
18.Dubberke, ER, Butler, AM, Hota, B, et al; Prevention Epicenters Program from the Centers for Disease Control and Prevention. Multicenter study of the impact of community-onset Clostridium difficile infection on surveillance for C. difficile infection. Infect Control Hosp Epidemiol 2009;30(6):518–525.CrossRefGoogle ScholarPubMed
19.Sohn, S, Climo, M, Diekema, D, et al; Prevention Epicenter Hospitals. Varying rates of Clostridium difficile-associated diarrhea at prevention epicenter hospitals. Infect Control Hosp Epidemiol 2005;26(8):676–679.CrossRefGoogle ScholarPubMed
20.Campbell, RJ, Giljahn, L, Machesky, K, et al. Clostridium difficile infection in Ohio hospitals and nursing homes during 2006. Infect Control Hosp Epidemiol 2009;30(6):526–533.CrossRefGoogle ScholarPubMed
21.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(8):926–931.CrossRefGoogle ScholarPubMed
22.Effler, P, Ching-Lee, M, Bogard, A, Ieong, MC, Nekomoto, T, Jernigan, D. Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. JAMA 1999;282(19):1845–1850.CrossRefGoogle ScholarPubMed
23.Panackal, AA, M'ikanatha, NM, Tsui, FC, et al. Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg Infect Dis 2002;8(7):685–691.CrossRefGoogle Scholar
24.Overhage, JM, Grannis, S, McDonald, CJ. A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions. Am J Public Health 2008;98(2):344–350.CrossRefGoogle ScholarPubMed
25.Tabak, YP, Sievert, DM, Zilberberg, MD, et al. The epidemiology of initial and recurrent Clostridium difficile infections in US hospitals, 2007-2008. In: Program and abstracts of the 47th Annual Meeting of the Infectious Diseases Society of America. Philadephia, PA; 2009. Abstract 179.Google Scholar
26. C. difficile infection surveillance and C. difficile LabID event reporting training. National Healthcare Safety Network Website, http://www.cdc.gov/nhsn/wc_MDRO_CDAD_ISlabID.html. Accessed March 10, 2010.Google Scholar
27. Office of the National Coordinator for Health Information Technology Website, http://www.healthit.hhs.gov. Accessed March 10, 2010.Google Scholar
28.Dubberke, ER, Reske, KA, McDonald, LC, Fraser, VJ. ICD-9 codes and surveillance for Clostridium difficile-associated disease. Emerg Infect Dis 2006;12(10):1576–1579.CrossRefGoogle ScholarPubMed
29.Dubberke, ER, Butler, AM, Yokoe, DS, et al; Prevention Epicenters Program of the Centers for Disease Control and Prevention. 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