One Health抗菌素耐药性建模:从科学到政策。

Science in One Health Pub Date : 2026-01-01 Epub Date: 2026-01-10 DOI:10.1016/j.soh.2026.100146
Carys J. Redman-White , Gwen Knight , Cristina Lanzas , Rodolphe Mader , Bram van Bunnik , Fernando O. Mardones , Adrian Muwonge , Guillaume Lhermie , Andrew R. Peters , Dominic Moran
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引用次数: 0

摘要

现代人类和兽医防治传染病的干预措施依赖于抗微生物药物的持续功效。抗菌素耐药性(AMR)是威胁人类和动物健康和福利的典型健康挑战,并对土壤和水的生态群落产生环境影响。关于抗微生物药物耐药性的政策指导需要预测不同干预措施和行动方针可能产生的结果。为此,跨学科合作以了解抗菌素耐药性的发展、传播和影响至关重要。我们报告了一个国际研讨会的结果,该研讨会探讨了在单一健康环境中模拟抗菌素耐药性的挑战和机遇。它们包括数据质量和可用性的差异,在关键领域(如抗菌素使用(AMU)和抗菌素耐药性之间的关系)存在更广泛的知识差距,以及由于抗菌素耐药性的异质性,难以将其定义为单一结果。微生物种类、耐药基因、环境(如陆生vs水生)和实际环境(如人类临床vs兽医,或个体vs群体)之间的差异使模型应用的普遍性复杂化。然而,概括性的AMR指标是必要的,以减少政策制定的复杂性。我们讨论了相对于决策的建模证据层次的AMR建模的地位。最后,我们考虑从其他恶劣环境挑战的建模中学习,以制定一种实用的方法来为政策提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

One Health antimicrobial resistance modelling: from science to policy

One Health antimicrobial resistance modelling: from science to policy

One Health antimicrobial resistance modelling: from science to policy
Modern human and veterinary medical interventions to combat infectious diseases depend on the continued efficacy of antimicrobial drugs. Antimicrobial resistance (AMR) is the quintessential One Health challenge threatening human and animal health and welfare and has environmental effects on ecological communities in soil and water. Policy guidance on AMR needs to anticipate the likely outcomes of different interventions and courses of action. For that, transdisciplinary collaboration to understand the development, spread, and impacts of AMR is crucial. We report the outcomes of an international workshop that explored the challenges and opportunities for modelling AMR across One Health settings. They include the disparity of data quality and availability, the broader knowledge gaps in key areas such as the relationship between antimicrobial use (AMU) and AMR, and the difficulty of defining AMR as a single outcome given its heterogeneity. Differences between microbial species, resistance genes, environments (i.e., terrestrial vs. aquatic) and practical settings (e.g., human clinical vs. veterinary, or individual vs. population) complicate the generalizability of model applications. However, synoptic AMR metrics are necessary to cut through the complexity for policymaking. We discuss the status of AMR modelling with respect to a hierarchy of modelling evidence for decision-making. Finally, we consider learnings from modelling other wicked environmental challenges to develop a pragmatic approach to inform policy.
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