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抗微生物耐药性监测:在二次数据使用中数据协调和数据选择.

Sinja Bleischwitz¹, Tristan Salomon Winkelmann¹, Yvonne Pfeifer²

¹Department of Biometry, Epidemiology and Information Processing, WHO Collaborating Centre for Research and Training for Health in the Human-Animal-Environment Interface, University for Veterinary Medicine, 30559 Hanover, Germany.

Antibiotics (Basel, Switzerland)

|July 27, 2024

概括

建立一个健康监测系统对于监测抗菌素耐药性 (AMR) 至关重要. 这项研究强调了整合各种数据源的复杂性,以有效跟踪和控制AMR.

关键词:

它们是Enterobacteriaceae的细菌.一个健康,一个健康.碳烯的使用方法集群分析集群分析集群分析科里斯 (Colistin) 是一个古老的数据收集数据收集数据收集 mcr-1-1 的使用情况.分子监控的分子监控.多种药物耐药性多种药物耐药性

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科学领域:

微生物学微生物学
公共卫生公共卫生
兽医医学兽医医学兽医医学

背景情况:

对最后手段抗生素的抗菌素耐药性 (AMR) 构成了全球健康的重大威胁.
有效的监测和监测系统对于在国家和国际范围内管理AMR至关重要.
一个健康的方法是必要的,以全面跟踪人类,动物,食品和环境部门的AMR.

研究的目的:

制定一项针对抗微生物药物耐药性的One Health监测系统的框架.
探索从各种来源获得的异质二次数据的整合,用于AMR监测.
解决协调各种流行病学,表型和分子数据的挑战.

主要方法:

从人类,动物,食品和环境来源收集和汇总了关于碳烯和抗素的细菌分离物的二次数据.
综合流行病学元数据与表型耐药性和分离水平的分子数据.
制定了数据协调的通用策略,包括变量定义,分类和可信性检查.

主要成果:

证明了管理AMR监测中异质二次数据的复杂性.
成功地将协调策略应用于一个聚合的数据集,使初步描述性分析成为可能.
利用现有数据,提供了关于开发抗菌耐药性监测计划的初始阶段的见解.

结论:

数据协调是建立综合"一个健康"抗菌耐药性监测的关键挑战.
系统的数据管理方法对于有效利用二次数据至关重要.
这项研究为开发强大的AMR监测计划提供了基础,该计划包含多种数据流.