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中国采用“同一健康”方法控制抗菌药物耐药性的跨部门协同治理计划:一项混合方法研究的研究方案

Cross-sectoral synergy governance programme for antimicrobial resistance control in China using a 'One Health' approach: study protocol for a mixed-methods study.

作者信息

Fan Zhixin, Yin Jia, Zhang Zhibin, Wei Xiaolin, Yang Ding, Sun Qiang

机构信息

Department of Social Medicine and Health Management, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.

NHC Key Lab of Health Economics and Policy Research (Shandong University), Jinan, China.

出版信息

BMJ Open. 2025 Jul 30;15(7):e095062. doi: 10.1136/bmjopen-2024-095062.

Abstract

INTRODUCTION

Antimicrobial resistance (AMR) is a critical global public health concern, particularly acute in rural China. Counties, which cover extensive rural regions, face major challenges in AMR governance and thus require priority attention. Yet, AMR governance efforts across sectors are fragmented, with notable gaps in translating policy objectives into sustainable, practical governance measures. This programme will entail a series of studies focusing on county-level cross-sectoral synergy governance for AMR, aiming to identify optimal synergy governance strategies to curb AMR.

METHODS AND ANALYSIS

The study comprises three phases: (1) understanding and exploring the state of cross-sectoral synergy governance and its internal mechanisms; (2) empirically evaluating AMR synergy governance capability using a developed evaluation indicator tool; and (3) identifying optimal AMR synergy governance strategies through a simulation and prediction model. Phase I involves conducting a content analysis of policy documents and semistructured interviews to understand and explore the state of cross-sectoral synergy governance and internal mechanisms. An evaluation indicator tool for AMR synergy governance capability will be developed through a two-round modified Delphi survey, hierarchical analysis process and percentage weighting method, with a typical case analysis being used for empirical evaluation in phase II. Phase III entails developing a simulation and prediction model using a series of artificial intelligence technologies, such as distributed Scrapy crawler technology, large language models, generative adversarial networks and deep multilayer models, all aimed at identifying optimal AMR synergy governance strategies.

ETHICS AND DISSEMINATION

This study was approved by the ethics committee of the Centre for Health Management and Policy Research, Shandong University (No. ECSHCMSDU20240904). The results of the studies will be submitted for publication in peer-reviewed journals, presented at national and international academic conferences.

摘要

引言

抗菌药物耐药性(AMR)是一个严重的全球公共卫生问题,在中国农村地区尤为突出。覆盖广大农村地区的县在抗菌药物耐药性治理上面临重大挑战,因此需要优先关注。然而,各部门在抗菌药物耐药性治理方面的努力分散,在将政策目标转化为可持续的实际治理措施方面存在明显差距。本项目将开展一系列针对县级抗菌药物耐药性跨部门协同治理的研究,旨在确定遏制抗菌药物耐药性的最佳协同治理策略。

方法与分析

该研究包括三个阶段:(1)了解和探索跨部门协同治理的现状及其内部机制;(2)使用开发的评估指标工具对抗菌药物耐药性协同治理能力进行实证评估;(3)通过模拟和预测模型确定最佳的抗菌药物耐药性协同治理策略。第一阶段包括对政策文件进行内容分析和进行半结构化访谈,以了解和探索跨部门协同治理的现状及其内部机制。将通过两轮改进的德尔菲调查、层次分析法和百分比加权法开发抗菌药物耐药性协同治理能力评估指标工具,并在第二阶段使用典型案例分析进行实证评估。第三阶段需要使用一系列人工智能技术,如分布式Scrapy爬虫技术、大语言模型、生成对抗网络和深度多层模型,开发模拟和预测模型,所有这些都旨在确定最佳的抗菌药物耐药性协同治理策略。

伦理与传播

本研究已获得山东大学卫生管理与政策研究中心伦理委员会批准(编号:ECSHCMSDU20240904)。研究结果将提交至同行评审期刊发表,并在国内和国际学术会议上展示。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adeb/12315018/52103536faa9/bmjopen-15-7-g001.jpg

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