ArticleInfection control and hospital epidemiology2020
Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs.
Article in Infection control and hospital epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- A Trial of Automated Outbreak Detection to Reduce Hospital Pathogen Spread.NEJM evidence · 2024Trial
- Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings.Antimicrobial resistance and infection control · 2024Article
- Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.JMIR public health and surveillance · 2024Article
- Enhanced automated detection of outbreaks of a rare antimicrobial-resistant bacterial species.PloS one · 2024Article
- Application of Pathogen Genomics to Outbreak Investigation.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Perspectives on research needs in healthcare epidemiology, infection prevention, and antimicrobial stewardship: what's on the horizon-Part II.Antimicrobial stewardship & healthcare epidemiology : ASHE · 2023Review
- Digital microbiology.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 1 country.
Funding
Abstract
objectiveTo assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.
designMulticenter retrospective cohort study.
settingThe study included 43 hospitals using a common infection prevention surveillance system.
methodsA 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.
resultsWe 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.
conclusionsAutomated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.
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