Mapping the patterns and drivers of antibiotic use and environmental resistance in the Argentine beef industry.

该项目将建立在阿根廷和英国合作伙伴开展的初步工作的基础上，该合作伙伴已经确定了牛肉饲养场中抗生素耐药性的存在，并生成了世界上任何地方牛场抗生素使用数据（334个农场）的第一个大规模数据集。它还将以英国官方发展援助种子基金现有的7700英镑小额赠款为基础，帮助UoL学者开发“英国-阿根廷牛肉抗生素研究网络”，该网络将用于收集和分析样本，以优化qPCR基因选择，用于牛肉农场的环境AMR量化，计划于2019年1月至6月进行。这项初步工作为本项目提供了一个非常坚实的起点。该项目将利用兽医和流行病学方面的专业知识和量化抗生素使用的经验，以及农业卫生经济学方面绘制食品价值链的专业知识，建立一个理论上的抗生素监测框架。将根据其他低收入和中等收入国家的现有信息来源对该框架进行评估，以检验其可转移性，特别是在包括肯尼亚和埃塞俄比亚在内的欠发达牲畜系统，通过英国和合作者参与的现有项目（HORN项目）。该系统将整合所有关于抗生素处方和抗生素使用的现有信息，以及阿根廷整个牛肉养殖系统的农场管理实践。然后，我们将对农场层面的抗生素使用进行分类，从现有的4500个农场（通过SENASA组织）的代表性网络中招募200 - 1000个农场，将其纳入不同的管理系统类型(例如：通过使用监测框架，将这些农场使用的管理实践与抗生素使用模式联系起来。环境抗菌素耐药性种群负荷和多样性将根据管理系统类型、抗菌素水平和使用做法，在农场的分层随机子样本（n = 50）中进行评估。多层次建模、聚类和主成分分析将用于量化基于微生物监测框架的养殖场分类与微生物学结果之间的一致性，以验证这种风险评估手段的有效性。将对来自农场的样本进行更深入的分子分析，这些样本有证据表明与高优先级至关重要的抗生素有关的最显著的抗菌素耐药性多样性和负荷。这将包括全基因组宏基因组学和基于培养的抗生素表型敏感性，以了解最显著的AMR环境的耐药谱的群体结构。该项目的这一阶段将使我们能够使用风险因素分析来确定牛肉养殖场抗生素使用模式中的关键控制点。这将通过将分子流行病学数据与对农民的详细定量和定性访谈联系起来，以了解抗生素使用的驱动因素来实现。该项目的最后阶段将共同制定切实可行的减少抗微生物药物耐药性干预措施，并为监管当局提供政策建议。所有建议的干预措施都将以风险因素分析为基础，并通过成本效益分析对其经济影响进行测试，以明确和可重复的方式确定每个农场管理系统的最适当控制措施。在研究中对每个农民进行的定性访谈将为采取个别措施的障碍的决策提供信息，并为牛肉行业的政策建议的共同制定提供信息。这次合作为比较和对比英国和阿根廷的方法提供了令人兴奋的机会，以最大限度地提高农民对新做法和建议的采用。该项目将直接通过UoL聘用两名博士后研究员（PDRAs）。虽然这两名PDRA将受雇于UoL，但他们将由英国和阿根廷合作伙伴高级学者共同监督。

This project will build upon preliminary work conducted by Argentine and UK partners which has already identified the presence of AMR in beef feedlots and generated the first large-scale dataset of beef farm antibiotic usage data (334 farms) anywhere in the world. It will also build upon a small existing grant of £7,700 from UK ODA seed fund to help UoL academics develop a 'UK-Argentine Beef Antibiotic Research Network' which will be used to collect and analysis samples to optimize qPCR gene selection for environmental AMR quantification in beef farms, planned for Jan-June 2019. This preliminary work provides a very robust starting point for this project.The project will use veterinary and epidemiology expertise and experience in quantifying antibiotic usage and agri-health economics expertise in mapping food value chains to build a theoretical antibiotic surveillance framework. This framework will be assessed against the available sources of information in other LMIC's to test its transferability especially in less well developed livestock systems including Kenya and Ethiopia, through existing projects which the UK the collaborators are engaged in (HORN project). The system would integrate all the available information on antibiotic prescribing and antibiotic usage along with the farm management practices across the whole range of beef farming systems in Argentina. We will then categorize antibiotic use at farm level recruiting between 200 - 1000 farms from an existing representative network of 4500 farms (organized via SENASA) into different management system types (eg. breeding, growing, feedlot finishing) and correlate the management practices used in these farms 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 sub-sample of (n = 50) of the farms according to management system type and AMU level & usage practices. Multi-level modelling, 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.Deeper molecular analysis will be conducted on samples from farms with evidence of the most significant AMR diversity and load relating to High Priority Critically important antibiotics. This will include whole genome metagenomics and culture based antibiotic phenotype sensitivity to understand the population structure of the resistance profile of the most significant AMR environments. This phase of the project will allow us to use risk factor analysis to identify critical control points in the mode of antibiotic use in beef farms. This will be achieved by linking the molecular epidemiology data to detailed quantitative and qualitative interviews with farmers to understand the drivers of antibiotic use.The final phase of the project will co-develop practical AMR reduction interventions and policy advice of regulatory authorities. All the interventions recommended will be based up the risk factor analysis and their economic impact tested by cost - effectiveness analysis to identify in a clear a reproducible manner the most appropriate control measures for each farm management system. The qualitative interviews conducted with each farmer in the study will inform decision making on barriers to adoption of individual measures and feed into the co-development of policy advice to the beef industry. The collaboration provides the exciting opportunity to compare and contrast approaches in the UK and Argentina to maximizing farmer adoption of new practices and advice. The project would employ two post-doctoral researchers (PDRAs) directly through the UoL. Whilst these two PDRA's will be employed by UoL they will be supervised jointly by UK and Argentine partner senior academics.