Welcome to CDC stacks | Statistical detection of geographic clusters of resistant Escherichia coli in a regional network with WHONET and SaTScan - 42729 | CDC Public Access
Stacks Logo
Advanced Search
Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.
 
 
Help
Clear All Simple Search
Advanced Search
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.


Public Access Version Available on: November 01, 2017 information icon
Please check back on the date listed above.
Details:
  • Pubmed ID:
    27530311
  • Pubmed Central ID:
    PMC5109973
  • Description:
    Objectives

    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.

  • Document Type:
  • Collection(s):
  • 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
  • Supporting Files:
    No Additional Files
No Related Documents.
You May Also Like: