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

绘制阿根廷牛肉行业抗生素使用和环境抗性的模式和驱动因素。

• 批准号: BB/T00472X/1
• 负责人: Peers Davies
• 金额: $ 115.24万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FT00472X%2F1
• 关键词: Mapping; patterns; drivers; antibiotic; use

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.

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，这些合作伙伴已经确定了牛肉饲养场中存在AMR，并生成了世界上第一个大规模的牛肉养殖场抗生素使用数据集(334个养殖场)。它还将建立在英国官方发展援助种子基金现有的7,700 GB的小额赠款的基础上，以帮助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，用于环境AMR量化，计划于2019年1月至6月在肉牛场进行。这项初步工作为这个项目提供了一个非常有力的起点。该项目将利用兽医和流行病学的专业知识和量化抗生素使用的经验，以及绘制食品价值链的农业健康经济学专业知识，以建立一个理论上的抗生素监测框架。将对照其他LMIC的现有信息来源对这一框架进行评估，以测试其可转移性，特别是在肯尼亚和埃塞俄比亚等欠发达的畜牧系统中，通过合作者参与的现有项目(Horn项目)。该系统将整合关于抗生素处方和抗生素使用的所有现有信息，以及阿根廷整个牛肉养殖系统的农场管理做法。然后，我们将在农场层面对抗生素的使用进行分类，从现有的4500个农场(通过SENASA组织)中招募200-1000个农场，分为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做法与其抗生素使用模式相关联。根据管理系统类型和AMU水平和使用实践，将在(n=50)个农场的分层随机子样本中评估环境AMR种群负荷和多样性。多层次模型、聚类和主成分分析将被用来量化根据AMU监测框架对农场的分类与微生物结果之间的一致性，以验证这种风险评估方法的有效性。将对来自农场的样本进行更深入的分子分析，以证明AMR最显著的多样性和与高度优先的关键重要抗生素有关的负荷。这将包括全基因组元基因组学和基于培养的抗生素表型敏感性，以了解最重要的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养牛场抗生素使用模式的关键控制点。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。该项目的最后阶段将共同制定实用的AMR减少干预措施和监管当局的政策建议。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测试其经济影响，以明确可重复的方式为每个农场管理系统确定最适当的控制措施。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提供决策依据，并为共同制定对牛肉行业的政策建议提供参考。这一合作提供了令人兴奋的机会，可以比较和对比英国和阿根廷的方法，以最大限度地促进农民采用新的做法和建议。该项目将通过大学在线直接雇用两名博士后研究人员(PDRA)。虽然这两个PDRA将由UOL雇用，但他们将由英国和阿根廷合作伙伴高级学者共同监督。