ArticleExpert review of anti-infective therapy2016
Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan.
Article in Expert review of anti-infective therapy, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Assessing the risk of malaria re-transmission in Anhui Province, China: an integration of spatiotemporal scan statistics and ecological niche modeling.BMC infectious diseases · 2026Article
- Enhanced automated detection of outbreaks of a rare antimicrobial-resistant bacterial species.PloS one · 2024Article
- Characterizing a century of genetic diversity and contemporary antigenic diversity of N1 neuraminidase in influenza A virus from North American swine.Virus evolution · 2023Article
- Spatio-temporal variation on syphilis from 2005 to 2018 in Zhejiang Province, China.Frontiers in public health · 2022Article
- Integrating whole-genome sequencing within the National Antimicrobial Resistance Surveillance Program in the Philippines.Nature communications · 2020Article
- Automated detection of outbreaks of antimicrobial-resistant bacteria in Japan.The Journal of hospital infection · 2019Article
- [Geographic spatial pattern of digestive system cancers in Yiwu city].Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundWhile 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.
methodsEscherichia 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.
resultsGeographic 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.
conclusionSystematic 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.
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