Case-based surveillance of antimicrobial resistance with full susceptibility profiles.


Journal

JAC-antimicrobial resistance
ISSN: 2632-1823
Titre abrégé: JAC Antimicrob Resist
Pays: England
ID NLM: 101765283

Informations de publication

Date de publication:
Dec 2019
Historique:
entrez: 14 4 2020
pubmed: 14 4 2020
medline: 14 4 2020
Statut: ppublish

Résumé

Surveillance of antimicrobial resistance (AMR) is essential for clinical decision-making and for public health authorities to monitor patterns in resistance and evaluate the effectiveness of interventions and control measures. Existing AMR surveillance is typically based on reports from hospital laboratories and public health laboratories, comprising reports of pathogen frequencies and resistance frequencies among each species detected. Here we propose an improved framework for AMR surveillance, in which the unit of surveillance is patients with specific conditions, rather than biological samples of a particular type. In this 'case-based' surveillance, denominators as well as numerators will be clearly defined with clinical relevance and more comparable at the local, national and international level. In locations with sufficient resources, individual-based data on patient characteristics and full antibiotic susceptibility profiles would provide high-quality evidence for monitoring resistant pathogens of clinical importance, clinical treatment of infections and public health responses to outbreaks of infections with resistant bacteria.

Identifiants

pubmed: 32280945
doi: 10.1093/jacamr/dlz070
pii: dlz070
pmc: PMC7134534
doi:

Types de publication

Journal Article

Langues

eng

Pagination

dlz070

Subventions

Organisme : NIGMS NIH HHS
ID : U54 GM088558
Pays : United States

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy.

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Auteurs

Sukhyun Ryu (S)

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
Department of Preventive Medicine, College of Medicine, Konyang University, Daejeon, Republic of Korea.

Benjamin J Cowling (BJ)

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.

Peng Wu (P)

WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.

Scott Olesen (S)

Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Christophe Fraser (C)

Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

Daphne S Sun (DS)

Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Marc Lipsitch (M)

Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Yonatan H Grad (YH)

Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Classifications MeSH