AHHME: A model for estimating the holistic cost-effectiveness of antimicrobial resistance interventions in food animal production

Abstract

Antimicrobial resistance (AMR) is considered a global priority for human health, and reducing antimicrobial use in food animals has been suggested as a key area for interventions aiming to reduce resistant infections in humans. In addition to the effect on human health, such interventions may have effects across food animal productivity, healthcare sector costs, and the broader macroeconomy, but these effects are rarely captured in the AMR health economic literature. Without being able to estimate these effects, it is difficult to understand the true cost-effectiveness of antimicrobial stewardship interventions in food animal production, or to correctly design and prioritise such interventions.We explore and demonstrate the potential use of a novel compartment-based mathematical model to estimate the holistic cost-effectiveness of AMR-related interventions in food animal production from a One Health perspective. The Agriculture Human Health Micro-Economic model (AHHME) uses Markov state transition models to model the movement of humans and food animals between health states. It assigns values to these health states utilising empiric approaches, from the perspectives of human health, food animal productivity, labour productivity and healthcare sector costs. Providing AHHME open-source code and interactive online modelling tools allow for capacity building in AMR intervention modelling.This model represents a useful framework for capturing the cost-effectiveness of AMR-related interventions in food animal production in a more holistic way: it can allow us to capture the often-overlooked benefits of such interventions in like terms while considering distributional concerns. It also demonstrates that methodological assumptions such as willingness-to-pay thresholds and discount rates can be just as important to health decision models as epidemiological parameters, and allows these assumptions to be altered. We provide example outputs, and encourage researchers and policymakers to use and adapt our code to explore, design, and prioritise AMR-related interventions in their own country contexts.