ArticleAntimicrobial resistance and infection control2024
Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings.
Article in Antimicrobial resistance and infection control, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- How to utilize routine antimicrobial resistance surveillance data for local and national actions in an LMIC.JAC-antimicrobial resistance · 2025Review
- Not forgetting the humanitarian contexts in the fight against antimicrobial resistance: Operational-driven reflection on knowledge and research gaps by Médecins Sans Frontières.PLOS global public health · 2025Review
- Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review.Frontiers in microbiology · 2024Review
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Authors and funding
3 authors.
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Abstract
backgroundDetection of pathogen-related clusters within a hospital is key to early intervention to prevent onward transmission. Various automated surveillance methods for outbreak detection have been implemented in hospital settings. However, direct comparison is difficult due to heterogenicity of data sources and methodologies. In the hospital setting, we assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns.
methodsIn this retrospective cohort study we use WHONET-SaTScan, CLAR (CLuster AleRt system) and our currently used percentile-based system (P75) for the means of cluster detection. The three methods are applied to the same data curated from 1st January 2014 to 31st December 2021 from a tertiary care hospital. We show the results for the following case studies: the introduction of a new pathogen with subsequent endemicity, an endemic species, rising levels of an endemic organism, and a sporadically occurring species.
resultsAll three cluster detection methods showed congruence only in endemic organisms. However, there was a paucity of alerts from WHONET-SaTScan (n = 9) compared to CLAR (n = 319) and the P75 system (n = 472). WHONET-SaTScan did not pick up smaller variations in baseline numbers of endemic organisms as well as sporadic organisms as compared to CLAR and the P75 system. CLAR and the P75 system revealed congruence in alerts for both endemic and sporadic organisms.
conclusionsUse of statistically based automated cluster alert systems (such as CLAR and WHONET-Satscan) are comparable to rule-based alert systems only for endemic pathogens. For sporadic pathogens WHONET-SaTScan returned fewer alerts compared to rule-based alert systems. Further work is required regarding clinical relevance, timelines of cluster alerts and implementation.
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