Automated detection of hospital outbreaks: A systematic review of methods

• Published in: PloS one Vol. 12; no. 4; p. e0176438
• Main Authors: Leclère, Brice, Buckeridge, David L., Boëlle, Pierre-Yves, Astagneau, Pascal, Lepelletier, Didier
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.04.2017; Public Library of Science (PLoS)
• Subjects: Accessibility; Algorithms; Analysis; Antibiotic resistance; Antibiotics; Appendix; Automation; Bibliographic data bases; Bibliographies; Classification; Communities; Complications; Confidence intervals; Cross infection; Cross Infection - diagnosis; Data mining; Data processing; Deoxyribonucleic acid; Diagnosis; Disease control; Disease Outbreaks; DNA; Drug resistance; Early warning systems; Epidemics; Epidemiology; Health care; Heterogeneity; Hospitals; Human health and pathology; Humans; Identification methods; Informatics; Information systems; Intelligence; Internet; Intervals; Learning algorithms; Life Sciences; Literature reviews; Machine learning; Mathematical models; Medical laboratories; Medicine and Health Sciences; Methicillin; Methods; Microbiology; Morbidity; Natural language processing; Nosocomial infection; Nosocomial infections; Nucleotide sequence; Outbreaks; Pathogens; Pediatrics; Physical Sciences; Population Surveillance - methods; Predictions; Process control; Protocol (computers); Public health; Radiology; Regression analysis; Research and Analysis Methods; Risk factors; Search engines; Security; Sensitivity; Software; Staphylococcus infections; Statistical analysis; Statistical models; Studies; Technology application; Working groups; France; Automated detection; Hospital outbreaks; Systematic review; Methods
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0176438

Several automated algorithms for epidemiological surveillance in hospitals have been proposed. However, the usefulness of these methods to detect nosocomial outbreaks remains unclear. The goal of this review was to describe outbreak detection algorithms that have been tested within hospitals, consider how they were evaluated, and synthesize their results. We developed a search query using keywords associated with hospital outbreak detection and searched the MEDLINE database. To ensure the highest sensitivity, no limitations were initially imposed on publication languages and dates, although we subsequently excluded studies published before 2000. Every study that described a method to detect outbreaks within hospitals was included, without any exclusion based on study design. Additional studies were identified through citations in retrieved studies. Twenty-nine studies were included. The detection algorithms were grouped into 5 categories: simple thresholds (n = 6), statistical process control (n = 12), scan statistics (n = 6), traditional statistical models (n = 6), and data mining methods (n = 4). The evaluation of the algorithms was often solely descriptive (n = 15), but more complex epidemiological criteria were also investigated (n = 10). The performance measures varied widely between studies: e.g., the sensitivity of an algorithm in a real world setting could vary between 17 and 100%. Even if outbreak detection algorithms are useful complementary tools for traditional surveillance, the heterogeneity in results among published studies does not support quantitative synthesis of their performance. A standardized framework should be followed when evaluating outbreak detection methods to allow comparison of algorithms across studies and synthesis of results.