Version 1
: Received: 10 August 2022 / Approved: 12 August 2022 / Online: 12 August 2022 (11:28:08 CEST)
How to cite:
Emes, E.T. A Quantitative Framework for Evaluating the Societal Impact of Antimicrobial Use Reduction in Agriculture. Preprints2022, 2022080237. https://doi.org/10.20944/preprints202208.0237.v1
Emes, E.T. A Quantitative Framework for Evaluating the Societal Impact of Antimicrobial Use Reduction in Agriculture. Preprints 2022, 2022080237. https://doi.org/10.20944/preprints202208.0237.v1
Emes, E.T. A Quantitative Framework for Evaluating the Societal Impact of Antimicrobial Use Reduction in Agriculture. Preprints2022, 2022080237. https://doi.org/10.20944/preprints202208.0237.v1
APA Style
Emes, E.T. (2022). A Quantitative Framework for Evaluating the Societal Impact of Antimicrobial Use Reduction in Agriculture. Preprints. https://doi.org/10.20944/preprints202208.0237.v1
Chicago/Turabian Style
Emes, E.T. 2022 "A Quantitative Framework for Evaluating the Societal Impact of Antimicrobial Use Reduction in Agriculture" Preprints. https://doi.org/10.20944/preprints202208.0237.v1
Abstract
Antimicrobial resistance (AMR) is an increasingly pressing threat to human, animal, and environmental health. Reducing the use of antibiotics in agriculture has been identified as a key way to curb the spread of AMR. However, the effect of such policies on AMR prevalence, and their broader impacts on agricultural, health, and economic outcomes at the population level have proven very difficult to estimate and compare. This paper draws on and formalises ideas presented at the JPIAMR New Perspectives on Bacterial Drug Resistance workshop in June of 2022. With reference to emerging literature on the topic, it proposes a quantitative framework for estimating the relevant causal relationships needed to quantify the cross-sectoral impacts of AMR policies in agriculture, and for comparing these outcomes in like terms in a way which can feed directly into policy decision-making, notably without prohibitive data requirements. The ability of researchers to apply frameworks such as this will be increasingly necessary in order to holistically capture the impacts of AMR policies and to situate them in the broader policy context; especially where the mechanisms of transmission are opaque or complex, where data availability is limited; and where policymakers must allocate scarce resources among many competing objectives.
Keywords
AMR; agriculture; One Health; health economics; policy; modelling
Subject
Biology and Life Sciences, Agricultural Science and Agronomy
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
