A proposed analytic framework for determining the impact of an antimicrobial resistance intervention. (16th May 2017)

• Record Type: Journal Article
• Authors: Grohn, Yrjo T.; Carson, Carolee; Lanzas, Cristina; Pullum, Laura; Stanhope, Michael; Volkova, Victoriya
• Is Part Of: Animal health research reviews. Volume 18:Number 1(2017:Jun.)
• Page Start: 1
• Page End: 25
• Publication Date: 2017-05-16
• Subjects: antimicrobial use, -- antimicrobial resistance, -- intervention strategy, -- analytic framework; Animal health -- Periodicals; Veterinary medicine -- Periodicals; 636.089
• DOI: 10.1017/S1466252317000019
• Languages: English
• ISSNs: 1466-2523

Abstract

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.