Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria, South Africa.
Centre for Epidemic Response and Innovation, School for Data Science and Computational Thinking, Stellenbosch University, Stellenbosch, South Africa.
Microb Ecol. 2024 Nov 29;87(1):150. doi: 10.1007/s00248-024-02463-3.
The One Health concept recognises the interconnectedness of humans, plants, animals and the environment. Recent research strongly supports the idea that the environment serves as a significant reservoir for antimicrobial resistance (AMR). However, the complexity of natural environments makes efforts at AMR public health risk assessment difficult. We lack sufficient data on key ecological parameters that influence AMR, as well as the primary proxies necessary for evaluating risks to human health. Developing environmental AMR 'early warning systems' requires models with well-defined parameters. This is necessary to support the implementation of clear and targeted interventions. In this review, we provide a comprehensive overview of the current tools used globally for environmental AMR human health risk assessment and the underlying knowledge gaps. We highlight the urgent need for standardised, cost-effective risk assessment frameworks that are adaptable across different environments and regions to enhance comparability and reliability. These frameworks must also account for previously understudied AMR sources, such as horticulture, and emerging threats like climate change. In addition, integrating traditional ecotoxicology with modern 'omics' approaches will be essential for developing more comprehensive risk models and informing targeted AMR mitigation strategies.
“同一健康”理念认识到人类、植物、动物和环境的相互联系。最近的研究强烈支持这样一种观点,即环境是抗菌药物耐药性(AMR)的重要储存库。然而,自然环境的复杂性使得对抗菌药物耐药性公共卫生风险评估变得困难。我们缺乏影响 AMR 的关键生态参数以及评估对人类健康风险所需的主要代理数据。开发环境 AMR“早期预警系统”需要具有明确定义参数的模型。这对于支持明确和有针对性的干预措施的实施是必要的。在这篇综述中,我们全面概述了目前全球用于环境 AMR 人类健康风险评估的工具以及潜在的知识差距。我们强调需要标准化、具有成本效益的风险评估框架,这些框架在不同环境和地区具有适应性,以提高可比性和可靠性。这些框架还必须考虑到以前研究不足的 AMR 来源,如园艺,以及气候变化等新出现的威胁。此外,将传统生态毒理学与现代“组学”方法相结合对于开发更全面的风险模型和为有针对性的 AMR 缓解策略提供信息将是至关重要的。