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

抗生素耐药性的进化和传播不断挑战着抗生素治疗的进步，使得医生在经济有效的治疗方法上的选择越来越少。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人群规模--必须计划和实施干预措施的规模--持续存在的理解很薄弱。制定有效的公共卫生和管理工具的关键是对人类和动物群体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一个统一的认识。这个项目将收集数据并建立理解，明确解决这一知识差距。我们的长期目标是确定有助于维持和传播抗生素耐药性的生态和社会经济驱动因素。我们将开发一个社区规模的抗生素耐药性流行病学模型，将分子和表型数据与生态建模相结合，并使用该模型来调查抗生素耐药性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策略将被应用于研究坦桑尼亚三个宿主群体(人、牲畜和野生动物)和三个不同生态区的抗生素耐药性特征。我们选择较大的塞伦盖蒂生态系统进行研究的部分原因是：(I)潜在水库种群之间的近距离和接触为开发模型提供了一个易于处理的系统，以检验与工业化国家和资源受限国家相关的假设；(Ii)在面临药物选择压力的情况下，当地不受管制地获得抗生素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)社会经济状况在空间和时间上有所不同，该地区正在发生的变化包括采用新的牲畜生产系统、更多地依赖旅游业和不断增长的人口，以及农村社区的抗生素使用模式和人与动物的相互作用；(4)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的城市化，我们的发现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耐药持久性的相对贡献，并整合社区知识、态度和实践的贡献，以模拟社会经济对抗生素耐药性的贡献。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)，并预测人口流动性、社会经济状况和牲畜生产类型变化对抵抗力的潜在影响。对行为驱动因素(例如知识、教育和社会归属)的了解将指导与利益攸关方进行最适当的沟通。生物、流行病学和社会经济分析结合起来，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、经济和社会成果结合在一起。该框架将允许确定社区使用抗生素的积极、中性和消极方面，以指导更广泛的社会背景下的政策制定，使安全和稳定的食品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.