Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan. Issue 11 (1st November 2016)

• Record Type: Journal Article
• Authors: Park, Rachel; O'Brien, Thomas F.; Huang, Susan S.; Baker, Meghan A.; Yokoe, Deborah S.; Kulldorff, Martin; Barrett, Craig; Swift, Jamie; Stelling, John
• Is Part Of: Expert review of anti-infective therapy. Volume 14:Issue 11(2016)
• Page Start: 1097
• Page End: 1107
• Publication Date: 2016-11-01
• Subjects: Geographic clustering -- outbreak detection -- antimicrobial resistance -- Escherichia coli -- Escherichia coli ST131 -- WHONET -- SaTScan; Anti-infective agents -- Research -- Periodicals; 616.90461
• DOI: 10.1080/14787210.2016.1220303
• Languages: English
• ISSNs: 1478-7210

Abstract

ABSTRACT: Background: While antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time. Methods: Escherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time. Results: Geographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others. Conclusion: Systematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.