Ecological and socio-economic factors impacting maintenance and dissemination of antibiotic resistance in the Greater Serengeti Ecosystem

抗生素治疗的进展不断受到抗生素耐药性的演变和传播的挑战，使医疗从业者的成本效益治疗选择越来越少。虽然人们普遍了解支持抗生素耐药性的分子尺度机制，但目前对耐药性如何在群体尺度（必须计划和实施干预措施的尺度）上持续存在的理解还很薄弱。对于开发有效的公共卫生和管理工具至关重要的是，对人类和动物种群中抗生素耐药性的总体生态以及影响抗生素耐药性演变和传播的社会经济因素有统一的认识。该项目将收集数据并建立明确解决这一知识差距的理解。我们的长期目标是确定促进抗生素耐药性维持和传播的生态和社会经济驱动因素。我们将开发一个社区尺度的抗生素耐药性流行病学模型，将分子和表型数据与生态模型相结合，并利用该模型研究抗生素耐药性的生态模式与社会经济驱动因素之间的关系。这一结合生态和社会经济驱动因素的战略将用于研究坦桑尼亚三个宿主种群（人类、牲畜和野生动物）以及三个不同生态区的抗生素耐药性特征。我们选择了更大的塞伦盖蒂生态系统进行研究，部分原因是：(1)潜在水库种群之间的接近和接触为开发模型提供了一个易于处理的系统，以测试与工业化和资源受限国家相关的假设；（ii）在存在药物选择压力的情况下，当地不受管制的抗生素获取为检验我们的中心假设（见下文）提供了一个强有力的机会；（iii）社会经济条件因时空而异，该区域正在发生变化，包括采用新的畜牧生产系统、更加依赖旅游业和人口增长，以及农村社区抗生素使用模式和人与动物的相互作用；（四）抗生素耐药性在坦桑尼亚的传播与当地社区直接相关。由于坦桑尼亚正在经历快速城市化，我们的研究结果将对其他经历类似社会经济变化的国家产生影响。利用统计和生态模型，我们将确定传播途径和生态水库对人类和动物种群中细菌持续存在抗生素耐药性的相对贡献，并整合社区知识、态度和实践的贡献，以模拟对抗生素耐药性的社会经济贡献。通过将生态动态与社会经济调查数据联系起来，我们将能够确定可改变的风险（例如抗生素使用模式、废物管理、牲畜管理和接触模式），并预测人口流动、社会经济地位和牲畜生产类型变化对耐药性的潜在影响。对行为驱动因素（例如，知识、教育和社会关系）的理解将指导与利益相关者的最适当的沟通模式。生物、流行病学和社会经济分析将有助于制定一个框架，将促进和维持抗生素耐药性的驱动因素的技术、经济和社会结果纳入其中。该框架将能够确定社区抗生素使用的积极、中性和消极方面，以指导在更广泛的社会背景下制定政策，使安全稳定的粮食供应和可持续畜牧业与人类健康相匹配。

Advances in antibiotic treatment are continually challenged by the evolution and dissemination of antibiotic resistance leaving medical practitioners with dwindling options for cost-effective therapies. Though the molecular scale mechanisms underpinning antibiotic resistance are generally understood, current understanding of how resistance persists at the population scale - the scale at which interventions must be planned and implemented - is weak. Critical to the development of effective public health and management tools is a unified understanding of the overall ecology of antibiotic resistance in both human and animal populations and the socio-economic factors that influence evolution and dissemination of antibiotic resistance. This project will gather data and build understanding that explicitly addresses this knowledge gap.Our long-term goal is to identify the ecological and socio-economic drivers that contribute to maintenance and dissemination of antibiotic resistance. We will develop a community-scale model of antibiotic resistance epidemiology that integrates molecular and phenotypic data with ecological modeling, and use this model to investigate the relationship between ecological patterns of antibiotic resistance and socio-economic drivers.This strategy of combining both ecological and socio-economic drivers will be applied to study antibiotic resistance traits amongst three host populations (human, livestock, and wildlife) and across three distinct ecological zones in Tanzania. We selected the greater Serengeti ecosystem for our study in part because (i) the close proximity and contact between potential reservoir populations provides a tractable system for developing models to test hypotheses that are relevant to both industrialized and resource-constrained countries; (ii) the local unregulated access to antibiotics provides a robust opportunity to test our central hypothesis (see below) in the presence of drug selection pressure; (iii) socio-economic conditions vary across space and time with on-going changes occurring in the region regarding adoption of new livestock production systems, greater reliance on tourism and growing human populations alongside antibiotic use patterns and human-animal interactions in rural communities; and (iv) the spread of antibiotic resistance in Tanzania is directly relevant to local communities. Because Tanzania is undergoing rapid urbanization, our findings will have implications for other countries experiencing similar socio-economic changes. Using statistical and ecological modelling, we will determine the relative contribution of transmission pathways and ecological reservoirs to the persistence of antibiotic resistance in bacteria from humans and animal populations and integrate the contribution of community knowledge, attitudes and practices to model the socio-economic contribution to antibiotic resistance. By linking the ecological dynamics with the socio-economic survey data, we will be able to identify modifiable risks (e.g., antibiotic usage patterns, waste management, livestock management and contact patterns) and also predict the potential impact on resistance of changes in population mobility, socio-economic status and livestock production type. The understanding of behavioural drivers (e.g., knowledge, education and social affiliation) will guide the most appropriate modes of communication with stakeholders. Together, the biological, epidemiological and socio-economic analyses will allow development of a framework that incorporates technical, economic and social outcomes of drivers that promote and maintain antibiotic resistance. The framework will allow identification of positive, neutral and negative aspects of antibiotic use in communities to guide policy development in broader societal context of matching safe and stable food supply and sustainable livestock farming with human health.