You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
Automated Surveillance of Clostridium difficile Infections Using BioSense
Stephen R. Benoit , MD, MPH, L. Clifford McDonald , MD, Roseanne English , BS and Jerome I. Tokars , MD, MPH
Infection Control and Hospital Epidemiology
Vol. 32, No. 1 (January 2011), pp. 26-33
Published by: Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America
Stable URL: http://www.jstor.org/stable/10.1086/657633
Page Count: 8
Preview not available
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.
This article is in the public domain, and no copyright is claimed.