Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan
Published Date:Sep 6 2016
Source:Expert Rev Anti Infect Ther. 14(11):1097-1107.
Pubmed Central ID:PMC5109973
Funding:R01 GM103525/GM/NIGMS NIH HHS/United States
R01 RR025040/RR/NCRR NIH HHS/United States
U54 CK000172/CK/NCEZID CDC HHS/United States
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.
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.
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.
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.
Supporting Files:No Additional Files
You May Also Like: