| 引用本文：严越, 牛佳苗, 张宗久.基于三维分析框架的基因治疗药物政策量化分析[J].中国卫生政策研究,2024,17(10):68-75 |
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| 基于三维分析框架的基因治疗药物政策量化分析 |
| 投稿时间：2024-06-12 修订日期：2024-10-01 PDF全文浏览 HTML全文浏览 |
| 严越1, 牛佳苗2, 张宗久1 |
1. 清华大学医学院 北京 100084;
2. 清华大学医院管理研究院 广东深圳 518055 |
| 摘要:目的：分析基因治疗药物政策文本的特征与现状，优化我国基因治疗药物政策体系。方法：采用内容分析法和词频分析法，构建“政策工具—互动主体—政策环节”的三维分析框架，对2009年3月—2024年3月发布的相关政策文件开展多维分类与交叉比较。结果：在纳入的37份国家级政策文本中，政策工具维度共筛选出246个文本条目，供给型(45.9%)、环境型(41.5%)政策工具占比较多，需求型(12.6%)政策工具占比较少，政策工具分布偏倚、内部结构存在差异；互动主体维度共筛选出195个文本条目，药品生产研发企业(36.9%)、药品监管评审机构(22.1%)、患者与受试者(19.0%)、医疗机构与医务人员(11.8%)、其他政府部门(10.2%)等各主体作用发挥不均；政策环节维度方面分为药物研发、生产和使用等三个阶段，部分政策涵盖多环节。结论和建议: 组合政策工具，强化需求型政策，优化内部使用；考虑不同互动主体的需求来综合施策，合作促进共赢；环节联动增效，促进产业链协同发展，构建全链条生态。 |
| 关键词:基因治疗药物 政策工具 三维交叉分析法 |
| 基金项目:北京市功能区医疗卫生规划研究项目(北京市卫生健康委员会)委托项目 |
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| A quantitative analysis of gene therapy drug policy based on a three-dimensional analytical framework |
| YAN Yue1, NIU Jia-miao2, ZHANG Zong-jiu1 |
1. School of Medicine of Tsinghua University, Beijing 100084, China;
2. Institute for Hospital Management of Tsinghua University, Shenzhen Guangdong 518055, China |
| Abstract:Objective: This study aims to analyze the characteristics and current status of gene therapy drug policy documents and to provide recommendations for optimizing China's gene therapy drug policy system. Methods: A three-dimensional analytical framework of “policy instruments-interactive subjects-policy phases” was constructed using content analysis and word frequency analysis. This framework was applied to the relevant policy documents issued from March 2009 to March 2024, enabling multidimensional classification and cross-comparison. Results: Among the 37 national-level policy documents included, 246 text items were identified under the dimension of policy instruments, with supply-based instruments (45.9%) and environmental instruments (41.5%) being more prevalent, while demand-based instruments (12.6%) were less represented. The distribution of policy instruments was skewed, and internal structural differences were observed. In the dimension of interactive subjects, 195 text items were identified, with drug manufacturing and R&D enterprises (36.9%), drug regulatory and accreditation agencies (22.1%), patients and subjects (19.0%), medical institutions and healthcare professionals (11.8%), and other government departments (10.2%) playing uneven roles. In the dimension of policy phases, the policies were categorized into three stages: drug R&D, production, and usage, with some policies covering multiple phases. Conclusions and suggestions: The study suggests combining and optimizing policy instruments for balanced application, strengthening demand-oriented policies, considering the needs of different interacting entities to formulate comprehensive policies, promoting cross-subject collaboration for win-win outcomes, and enhancing phase-linkage efficiency to build a comprehensive chain ecosystem for gene therapy drug development. |
| Key words:Gene therapy drugs Policy instruments Three-dimensional cross analysis |
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