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Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs

Published online by Cambridge University Press:  10 June 2020

Meghan A. Baker*
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts Brigham and Women’s Hospital, Boston, Massachusetts
Deborah S. Yokoe
Affiliation:
University of California San Francisco, San Francisco, California
John Stelling
Affiliation:
Brigham and Women’s Hospital, Boston, Massachusetts
Ken Kleinman
Affiliation:
University of Massachusetts Amherst, Amherst, Massachusetts
Rebecca E. Kaganov
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Alyssa R. Letourneau
Affiliation:
Brigham and Women’s Hospital, Boston, Massachusetts
Neha Varma
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Thomas O’Brien
Affiliation:
Brigham and Women’s Hospital, Boston, Massachusetts
Martin Kulldorff
Affiliation:
Brigham and Women’s Hospital, Boston, Massachusetts
Damilola Babalola
Affiliation:
Brigham and Women’s Hospital, Boston, Massachusetts
Craig Barrett
Affiliation:
Premier, Inc, Charlotte, NCUSA
Marci Drees
Affiliation:
ChristianaCare, Newark, Delaware
Micaela H. Coady
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Amanda Isaacs
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Richard Platt
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Susan S. Huang
Affiliation:
University of California Irvine School of Medicine, Orange, California
*
Author for correspondence: Meghan A. Baker, E-mail: meghan_baker@harvardpilgrim.org

Abstract

Objective:

To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.

Design:

Multicenter retrospective cohort study.

Setting:

The study included 43 hospitals using a common infection prevention surveillance system.

Methods:

A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.

Results:

We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.

Conclusions:

Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Type
Original Article
Copyright
© 2020 by The Society for Healthcare Epidemiology of America. All rights reserved.

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References

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