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A proposed analytic framework for determining the impact of an antimicrobial resistance intervention

Published online by Cambridge University Press:  16 May 2017

Yrjo T. Grohn Open the ORCID record for Yrjo T. Grohn [Opens in a new window] ,
Carolee Carson ,
Cristina Lanzas ,
Laura Pullum ,
Michael Stanhope  and
Victoriya Volkova
Yrjo T. Grohn*
Affiliation:
Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
Carolee Carson
Affiliation:
Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Ottawa, Ontario, Canada
Cristina Lanzas
Affiliation:
Population Health and Pathobiology, North Carolina State University College of Veterinary Medicine, Raleigh, North Carolina, USA
Laura Pullum
Affiliation:
Oak Ridge National Laboratory, Health Data Sciences Institute, Oak Ridge, Tennessee, USA
Michael Stanhope
Affiliation:
Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
Victoriya Volkova
Affiliation:
Diagnostic Medicine/Pathobiology, Institute of Computational Comparative Medicine, College of Veterinary Medicine, Kansas State University, Manhattan, Kansas, USA
*
*Corresponding author. E-mail: ytg1@cornell.edu
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Abstract

Antimicrobial use (AMU) is increasingly threatened by antimicrobial resistance (AMR). The FDA is implementing risk mitigation measures promoting prudent AMU in food animals. Their evaluation is crucial: the AMU/AMR relationship is complex; a suitable framework to analyze interventions is unavailable. Systems science analysis, depicting variables and their associations, would help integrate mathematics/epidemiology to evaluate the relationship. This would identify informative data and models to evaluate interventions. This National Institute for Mathematical and Biological Synthesis AMR Working Group's report proposes a system framework to address the methodological gap linking livestock AMU and AMR in foodborne bacteria. It could evaluate how AMU (and interventions) impact AMR. We will evaluate pharmacokinetic/dynamic modeling techniques for projecting AMR selection pressure on enteric bacteria. We study two methods to model phenotypic AMR changes in bacteria in the food supply and evolutionary genotypic analyses determining molecular changes in phenotypic AMR. Systems science analysis integrates the methods, showing how resistance in the food supply is explained by AMU and concurrent factors influencing the whole system. This process is updated with data and techniques to improve prediction and inform improvements for AMU/AMR surveillance. Our proposed framework reflects both the AMR system's complexity, and desire for simple, reliable conclusions.

Keywords

antimicrobial useantimicrobial resistanceintervention strategyanalytic framework
Type
Review Article
Information
Animal Health Research Reviews , Volume 18 , Issue 1 , June 2017 , pp. 1 - 25
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Copyright
Copyright © Cambridge University Press 2017 

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