Detection 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, Sim, et al. (2024) assess the performance of three different methods for identifying microbiological clusters when applied to various pathogens with distinct occurrence patterns.
In this retrospective cohort study the researchers used WHONET-SaTScan, CLAR (CLuster AleRt system) and their currently used percentile-based system (P75) for the means of cluster detection. The three methods were applied to the same data curated from January 1, 2014 to December 31, 2021 from a tertiary-care hospital. The researchers 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.
All 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.
Use 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.
Source: Sim JXY, et al. Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings. Antimicrobial Resistance & Infection Control. Volume 13, article number 69 (2024).