Automated detection of hospital outbreaks: A systematic review of methods

Brice Leclère (1, 2) , David L. Buckeridge (3) , Pierre-Yves Boëlle (4) , Pascal Astagneau (5, 6, 7) , Didier Lepelletier (2, 8)

1 Service d'Epidémiologie et Biostatistique [Nantes]
2 MiHAR - Microbiotes, Hôtes, Antibiotiques et Résistances bactériennes (MiHAR)
3 McGill University = Université McGill [Montréal, Canada]
4 iPLESP - Institut Pierre Louis d'Epidémiologie et de Santé Publique
5 CCLIN PARIS NORD - Centre de Coordination de Lutte contre les Infections Nosocomiales PARIS NORD
6 UPMC - Université Pierre et Marie Curie - Paris 6
7 EHESP - École des Hautes Études en Santé Publique [EHESP]
8 Dpt Microbiologie Infectiologie [CHU Nantes]

Objectives 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. Methods 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. Results 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%. Conclusion 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.

Domaines: Médecine humaine et pathologie

Brice Leclère, David L. Buckeridge, Pierre-Yves Boëlle, Pascal Astagneau, Didier Lepelletier. Automated detection of hospital outbreaks: A systematic review of methods. PLoS ONE, 2017, 12 (4), pp.e0176438. ⟨10.1371/journal.pone.0176438⟩. ⟨hal-01528556⟩