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

影响大塞伦盖蒂生态系统抗生素耐药性维持和传播的生态和社会经济因素

• 批准号: BB/K01126X/1
• 负责人: Louise Matthews
• 金额: $ 61.37万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2012
• 资助国家: 英国
• 起止时间: 2012 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FK01126X%2F1
• 关键词: Ecological; socio; economic; factors; impacting

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.

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疗法的选择越来越少。尽管人们普遍了解抗生素耐药性的分子机制，但目前对耐药性如何在人口规模上持续存在的理解-干预措施必须计划和实施的规模-很薄弱。开发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整体生态学以及影响抗生素耐药性演变和传播的社会经济因素的统一理解。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。我们将建立一个社区规模的抗生素耐药流行病学模型，将分子和表型数据与生态学建模相结合，并利用该模型研究抗生素耐药的生态模式与社会经济驱动因素之间的关系。这种将生态和社会经济驱动因素相结合的策略将用于研究三个宿主种群的抗生素耐药性特征（人类，牲畜和野生动物）和坦桑尼亚三个不同的生态区。我们选择较大的塞伦盖蒂生态系统进行研究，部分原因是（i）潜在水库种群之间的紧密接近和接触为开发模型提供了一个易于处理的系统，以测试与工业化国家和资源受限国家相关的假设;（ii）当地不受管制地获得抗生素提供了一个强有力的机会来检验我们的中心假设（见下文）在存在药物选择压力的情况下; ㈢社会经济条件因空间和时间而异，该区域在采用新的畜牧生产系统方面正在发生变化，农村社区更加依赖旅游业和不断增长的人口以及抗生素使用模式和人畜互动;以及（iv）坦桑尼亚抗生素耐药性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，我们的研究结果将对经历类似社会经济变化的其他国家产生影响。利用统计和生态建模，我们将确定传播途径和生态水库对人类和动物种群细菌抗生素耐药性持续存在的相对贡献，并整合社区知识，态度和实践的贡献，以模拟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的变化对抗药性的潜在影响。对行为驱动因素的理解（例如，知识、教育和社会关系）将指导与利益攸关方的最适当沟通方式。生物学、流行病学和社会经济分析将共同促成一个框架的制定，该框架将促进和维持抗生素耐药性的驱动因素的技术、经济和社会成果纳入其中。该框架将有助于确定社区使用抗生素的积极、中立和消极方面，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。

项目成果

Detection of Rare Antimicrobial Resistance Profiles by Active and Passive Surveillance Approaches.

• DOI: 10.1371/journal.pone.0158515
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Mather AE;Reeve R;Mellor DJ;Matthews L;Reid-Smith RJ;Dutil L;Haydon DT;Reid SW
• 通讯作者: Reid SW

Do antibiotic residues in soils play a role in amplification and transmission of antibiotic resistant bacteria in cattle populations?

• DOI: 10.3389/fmicb.2013.00193
• 发表时间: 2013
• 期刊: Frontiers in microbiology
• 影响因子: 5.2
• 作者: Call DR;Matthews L;Subbiah M;Liu J
• 通讯作者: Liu J

Identification of risk factors associated with carriage of resistant Escherichia coli in three culturally diverse ethnic groups in Tanzania: a biological and socioeconomic analysis.

• DOI: 10.1016/s2542-5196(18)30225-0
• 发表时间: 2018-11
• 期刊: The Lancet. Planetary health
• 作者: Caudell MA;Mair C;Subbiah M;Matthews L;Quinlan RJ;Quinlan MB;Zadoks R;Keyyu J;Call DR
• 通讯作者: Call DR

Antimicrobial Use and Veterinary Care among Agro-Pastoralists in Northern Tanzania.

坦桑尼亚北部的农业养护者之间的抗菌使用和兽医护理。

• DOI: 10.1371/journal.pone.0170328
• 发表时间: 2017
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Caudell MA;Quinlan MB;Subbiah M;Call DR;Roulette CJ;Roulette JW;Roth A;Matthews L;Quinlan RJ
• 通讯作者: Quinlan RJ

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

使用贝叶斯多元自回归模型估计病原体动态的时间协方差

• DOI: 10.48550/arxiv.1611.09063
• 发表时间: 2016
• 作者: Mair C