Year: 2024
Type: article
Study design: Clinical Trial
Abstract: BACKGROUND: Detection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown. METHODS: We conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods. RESULTS: Real-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92). CONCLUSIONS: Automated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).
Source: NEJM Evidence
Authors: Meghan A. Baker, Edward Septimus, Ken Kleinman, Julia Moody, Kenneth E. Sands, Neha Varma, Amanda Isaacs, Laura E. McLean, Micaela H. Coady, Eunice J. Blanchard, Russell E. Poland, Deborah S. Yokoe, John Stelling, Katherine Haffenreffer, Adam Clark, Taliser R. Avery, Selsebil Sljivo, Robert A. Weinstein, Kimberly N. Smith, Brandon Carver, Brittany Meador, Michael Y. Lin, Sarah S. Lewis, Chamaine Washington, Megha Bhattarai, Lauren Shimelman, Martin Kulldorff, Sujan C. Reddy, John A. Jernigan, Jonathan B. Perlin, Richard Platt, Susan S. Huang
Institutions: Brigham and Women's Hospital, Harvard University, Harvard Pilgrim Health Care, Memorial Hermann, Texas A&M University, University of Massachusetts Amherst, HCA Healthcare, University of California, San Francisco, Rush University Medical Center, John H. Stroger, Jr. Hospital of Cook County, Duke University Hospital, Centers for Disease Control and Prevention, Joint Commission, University of California, Irvine
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4395036984