Automated detection of hospital outbreaks of multi-drug resistant pathogens in one Italian region. Issue 9 (2nd September 2022)

• Record Type: Journal Article
• Authors: Binello, Nicolò; D'Ancona, Fortunato; Forni, Silvia; D'Arienzo, Sara; Gemmi, Fabrizio; Clark, Adam; Stelling, John
• Is Part Of: Expert review of anti-infective therapy. Volume 20:Issue 9(2022)
• Page Start: 1233
• Page End: 1241
• Publication Date: 2022-09-02
• Subjects: Antimicrobial resistance -- automated surveillance -- outbreak detection -- antibacterial agents -- drug-resistant bacteria; Anti-infective agents -- Research -- Periodicals; 616.90461
• DOI: 10.1080/14787210.2022.2098115
• Languages: English
• ISSNs: 1478-7210

Abstract

ABSTRACT: Background: Automated tools for antimicrobial resistance surveillance are critical for improving detection of drug-resistant organisms and informing prevention and control interventions. In this study, the WHONET-SaTScan software was used at a multihospital level in Tuscany, Italy, to identify case clusters consistent with hospital outbreaks caused by drug-resistant pathogens. Methods: Antimicrobial resistance surveillance data from all Tuscany hospitals between January 2018 and December 2020 were analyzed using WHONET. The SaTScan package was used to detect case clusters applying a simulated prospective approach and the space–time permutation algorithm. Clusters were identified using resistance profiles and two distinct spatial variables: single medical services ('service') or groups of related services ('metaservice'). Results: Data from eight bacterial pathogens were provided from 49 hospitals for 312, 779 isolates from 158, 809 patients. Single service-based analysis detected 693 hospital clusters, while metaservice-based analysis identified 635. There was no evidence for a difference between the two methods in terms of cluster length, cluster size, recurrence intervals, number of alerts, distribution across years or hospitals. Among clusters involving multiple services identified by both analyses, metaservice-detected clusters were usually larger and more statistically significant. Conclusions: WHONET-SaTScan proved to be a valuable multi-facility cluster detection tool that can be implemented for real-time surveillance.