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Genomics for antimicrobial resistance surveillance to support infection prevention and control in health-care facilities

  • SEDRIC Genomics Surveillance Working Group
  • , Elita Jauneikaite
  • , Kate S. Baker
  • , Jamie G. Nunn
  • , Janet T. Midega
  • , Li Yang Hsu
  • , Shweta R. Singh
  • , Alison L. Halpin
  • , Katie L. Hopkins
  • , James R. Price
  • , Padmini Srikantiah
  • , Beverly Egyir
  • , Iruka N. Okeke
  • , Kathryn E. Holt
  • , Sharon J. Peacock
  • , Nick Feasey
  • Imperial College London
  • Imperial College Healthcare NHS Trust
  • University of Liverpool
  • University of Cambridge
  • Wellcome Trust
  • National University of Singapore
  • Centers for Disease Control and Prevention
  • UK Health Security Agency
  • University of Sussex
  • Gates Foundation
  • University of Ghana
  • University of Ibadan
  • London School of Hygiene and Tropical Medicine
  • Monash University
  • Malawi-Liverpool-Wellcome Trust Clinical Research Programme

Research output: Contribution to journalReview articlepeer-review

39 Citations (Scopus)

Abstract

Integration of genomic technologies into routine antimicrobial resistance (AMR) surveillance in health-care facilities has the potential to generate rapid, actionable information for patient management and inform infection prevention and control measures in near real time. However, substantial challenges limit the implementation of genomics for AMR surveillance in clinical settings. Through a workshop series and online consultation, international experts from across the AMR and pathogen genomics fields convened to review the evidence base underpinning the use of genomics for AMR surveillance in a range of settings. Here, we summarise the identified challenges and potential benefits of genomic AMR surveillance in health-care settings, and outline the recommendations of the working group to realise this potential. These recommendations include the definition of viable and cost-effective use cases for genomic AMR surveillance, strengthening training competencies (particularly in bioinformatics), and building capacity at local, national, and regional levels using hub and spoke models.

Original languageEnglish
Pages (from-to)e1040-e1046
JournalThe Lancet Microbe
Volume4
Issue number12
DOIs
Publication statusPublished - 14 Nov 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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