The framework will describe the correlation between usage levels and management practices with excretion of antimicrobial resistance (AMR) and how this contributes to environmental AMR loading.
This will serve as a robust evidence base for the development of policy in Argentina, as well as a model for other low and middle income countries (LMICs) in South America and Africa.
The beef cattle management systems in Argentina are diverse. They range from:
- Extensive to intensive
- Large to small scale
- Traditional to vertically integrated
This variety provides the ideal sampling frame to identify AMR selection risk factors in management systems that can be found in many livestock dependent LMICs in South America and beyond.
The system will integrate all the available information on human behaviour and economics of antibiotic prescribing and antibiotic usage, along with farm management practices across the whole range of beef farming systems in Argentina.
Farms will then be categorised into different management system types (eg. breeding, growing, feedlot finishing) and management practices used in these farms will be correlated with their patterns of antibiotic usage by using the surveillance framework.
The environmental AMR population load and diversity will be assessed in a stratified random sample of the farms according to management system type and antimicrobial use (AMU) level and usage practices.
Multi level modelling with cluster and principal component analysis will be used to quantify the agreement between the classification of farms based upon the AMU surveillance framework and the microbiological results to validate the efficacy of this means of risk assessment.
In addition to the validation of the surveillance framework, the project will identify farm management and cattle husbandry risk factors for environmental AMR contamination.
This risk factor analysis will form the evidence base upon which targeted interventions will be co-developed with all parties.
Beyond conventional academic impact, the project also aims to create a toolbox including:
- An antibiotic surveillance framework that identifies the repositories of data on beef cattle production and antibiotic usage/sales/prescriptions, along with the barriers and opportunities for data integration from these diverse sources.
- A between farm antibiotic usage benchmarking system based upon the surveillance framework and validated by this project through targeted molecular microbiology by qPCR and whole genome metagenomics. This benchmarking system would be flexible enough to be operated at local or national levels, and organised by farmer co-operatives, breeding companies, processors, veterinary practices or regulatory authorities.
- A comprehensive set of best practice recommendations for specific ways to reduce risk of AMR contamination of the environment that would be drawn from the risk factor analysis conducted as part of this project. The highest risk areas of beef production would be identified through comparison between high and low AMR farms in Argentina and refined using evidence from the in-depth qualitative interviews conducted with farmer participants.
This research is funded by the UK Department of Health and Social Care as part of the Global AMR Innovation Fund (GAMRIF). This is a UK aid programme that supports early stage innovative research in underfunded areas of AMR research and development for the benefit of those in LMICs, who bear the greatest burden of AMR.
- Principal Investigator: Dr Peers Davies, University of Liverpool
- Funded value: £864,388
- Funded period: Aug 2019 – May 2022
- Funder: Biotechnology and Biological Sciences Research Council (BBSRC)
The University of Liverpool is also involved in the following AMR and livestock-related projects:
- Vietnamese Platform for Antimicrobial Reductions in Chicken production (ViParc) – funded by the Wellcome Trust.
- Drivers for AMR in Poultry in India (DARPI) – funded by UK Research and Innovation (UKRI).
- Rethinking of Antimicrobial Decision-systems in the Management of Animal Production (ROADMAP) – funded by Horizon 2020.