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Detection of Rare Antimicrobial Resistance Profiles by Active and Passive Surveillance Approaches.

Published version
Peer-reviewed

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Authors

Mather, Alison E 
Reeve, Richard 
Mellor, Dominic J 
Matthews, Louise 
Reid-Smith, Richard J 

Abstract

Antimicrobial resistance (AMR) surveillance systems are generally not specifically designed to detect emerging resistances and usually focus primarily on resistance to individual drugs. Evaluating the diversity of resistance, using ecological metrics, allows the assessment of sampling protocols with regard to the detection of rare phenotypes, comprising combinations of resistances. Surveillance data of phenotypic AMR of Canadian poultry Salmonella Heidelberg and swine Salmonella Typhimurium var. 5- were used to contrast active (representative isolates derived from healthy animals) and passive (diagnostic isolates) surveillance and assess their suitability for detecting emerging resistance patterns. Although in both datasets the prevalences of resistance to individual antimicrobials were not significantly different between the two surveillance systems, analysis of the diversity of entire resistance phenotypes demonstrated that passive surveillance of diagnostic isolates detected more unique phenotypes. Whilst the most appropriate surveillance method will depend on the relevant objectives, under the conditions of this study, passive surveillance of diagnostic isolates was more effective for the detection of rare and therefore potentially emerging resistance phenotypes.

Description

Keywords

Animals, Anti-Bacterial Agents, Drug Resistance, Bacterial, Microbial Sensitivity Tests, Models, Theoretical, Population Surveillance, Salmonella, Salmonella Infections, Animal, Salmonella enterica, Swine

Journal Title

PLoS One

Conference Name

Journal ISSN

1932-6203
1932-6203

Volume Title

11

Publisher

Public Library of Science (PLoS)
Sponsorship
Biotechnology and Biological Sciences Research Council (BB/M014088/1)
AEM was supported by the William Stewart Fellowship whilst at the University of Glasgow, and is currently supported by Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/ M014088/1; RR is supported by BBSRC grant BB/ E010326/1 and BB/L004070/1; LM is supported by BB/K01126X/1, BB/L004070/1, BB/F015313/1, National Science Foundation DEB1216040 and an EU-funded Marie Curie Initial Training Network (MCITN) program (NEMATODE SYSTEM HEALTH project (FP7-PEOPLE-2010-ITN- ID:264639)).