Selecting Efficient Farm-level Antimicrobial Stewardship Interventions from a One Health perspective

It is often suggested that interventions to prevent the development and transmission of antimicrobial resistance (AMR) should be targeted at the livestock sector. The potential costs to agriculture are often argued as "worth it" for the long-term human (and animal) health benefits. However, a quantification of the relative benefits and costs for each system is lacking.

Through shared learning across partners and secondary use of existing data, we plan on answering the question 'What farm-level antimicrobial usage (AMU) interventions are most efficient at the national-level, given different scenarios of human health, AMU and AMR, for England, Denmark and Senegal?', defining efficiency as the optimisation of resource use, given a set budget and a set of desired outcomes.

We will use statistical, mathematical and economic modelling to construct a compartmental model that can assess (i) the interplay between animal, environment and human transmission of AMR, and (ii) the interplay between costs and benefits across One Health. We will analyse the impact of previously implemented intervention to inform modelling of future interventions under different AMR scenarios. Settings, costs and benefits will be specifically defined via expert elicitation through a stakeholder-led 'knowledge hub'.

This work will not only provide explicit impact estimates and ranking for a range of farm-level AMU interventions, it will also provide insight into uncertainty, highlighting where future research could be most valuable in understanding AMR intervention efficiency from a One Health perspective.

It is often postulated that interventions to prevent the development and transmission of antimicrobial resistance (AMR) should be targeted at the livestock sector. The potential costs to agriculture are often argued as "worth it" for the long-term human (and animal) health benefits. However, a quantification of the relative benefits and costs for each system is lacking.

Through shared learning across partners and secondary use of existing data, we plan on answering the question 'What farm-level antimicrobial usage (AMU) interventions are most efficient at the national-level, given different scenarios of human health, AMU and AMR, for England, Denmark and Senegal?', defining efficiency as the optimisation of resource use, given a set budget and a set of desired outcomes.

We will use statistical data analysis, transmission dynamic mathematical modelling and microeconomic economic modelling to construct a unified model that can assess (i) the interplay between animal, environment and human transmission of AMR, and (ii) the interplay between costs and benefits across One Health. The statistical data analysis will analyse the impact of previously implemented interventions to inform modelling of future interventions under different AMR scenarios. Settings, costs and benefits will be specifically defined via expert elicitation through a stakeholder-led 'knowledge hub'.

This work will not only provide explicit impact estimates and ranking for a range of farm-level AMU interventions, it will also provide insight into uncertainty, highlighting where future research could be most valuable in understanding AMR intervention efficiency from a One Health perspective.