Associated organisationsCenter for Global Development · CGD Europe · City St George's, University of London · One Health Trust · St George's, University of London · University of OxfordEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£1.6M
PeriodAug 2024 — Jan 2027
In plain English
AI plain-English summary
Antibiotics are losing their power, and this project will calculate exactly which policies—and which levels of antibiotic use—offer the best economic and health returns for the world. The problem is stark: antimicrobial resistance already kills hundreds of thousands of people each year, yet no one has systematically weighed the costs and benefits of different intervention strategies across human health, animal farming, and the environment. Current decisions are made in the dark. This project will build the first integrated models that combine data from all three sectors—plus crop agriculture—to estimate global antibiotic exposure and its consequences. If successful, the research will tell governments and international bodies whether to act now, act now while collecting more data, or wait for better evidence before spending billions on new regulations, stewardship programmes, or bans on agricultural antibiotic use. The output is a decision-making tool, not a cure. It will prevent costly, poorly evidenced policies that waste public money and fail to slow resistance—keeping common infections treatable and routine surgeries safe for longer.
View original technical description
Antimicrobial resistance is responsible for significant mortality and economic harm. There is an urgent need to guide decision making around implementation of interventions aimed at achieving sustainable global levels of antibiotic use that ensure continued successful treatment of common infections. The overall aim of this project is to evaluate which decisions around antibiotic exposure targets and intervention implementation are optimal given the available data. We will pilot new methods that combine information from multiple sources to estimate antibiotic exposure across the Quadripartite sectors (WS1), develop the model structures needed to capture relevant health- economic consequences of intervention implementation (WS2), synthesise current evidence about parameters required to populate these models (WS3), and evaluate which exposure targets, as well as implementation of interventions aimed at reducing AMR burden, are associated with the largest net economic benefits (WS4). Through supplementing cost-effectiveness with macro-economic and value of information techniques the output will help to assess whether interventions should be i) implemented now, ii) implemented now with the requirement of further data collection and decision re-evaluation, or iii) delayed until further research has been performed to reduce uncertainty around influential parameters, reducing the probability of implementing poorly evidenced and/or cost-ineffective policies.
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