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为指导在基于证据的政策决策中使用数学建模的框架
Jacquie Oliwa1,2, Fatuma Hassan Guleid3, Collins J Owek4
1Health Services Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya joliwa@kemri-wellcome.org.
BMJ open
|April 5, 2025
概括
数学建模对于公共卫生决策至关重要,特别是在低收入国家. 这项研究开发了一个框架,通过解决能力,基础设施和协作障碍来加强对建模证据的使用.
科学领域:
- 公共卫生 公共卫生
- 卫生政策 卫生政策
- 数学建模的数学建模
背景情况:
- 随着COVID-19大流行,低收入和中等收入国家 (LMICs) 数学建模能力存在关键差距.
- 在LMICs中有效使用建模需要解决诸如有限的资金,容量,数据基础设施和知识翻译机制等挑战.
- 从大流行中吸取的经验教训为开发一个框架提供了信息,以改善基于证据的决策.
研究的目的:
- 共同创建一个框架和政策路线图,以加强在公共卫生决策中常规使用数学建模证据.
- 为利益相关者提供指导,特别是在资源有限的环境中,建立能力和促进有利于基于证据的政策的环境.
- 整合供应 (建模者) 和需求 (政策制定者) 两侧,以及上下文因素,以实现有效的知识转化.
主要方法:
- 在COVID-19大流行期间经验的定性分析,涉及LMIC的政策制定者和研究人员.
- 为基于证据的政策决策共同创建一个通用的,无病的框架.
- 制定一个政策路线图,并提出可行的建议.
主要成果:
- 强大的研究人员与政策制定者的关系和共同创造是知识转化的主要促进因素.
- 怀疑,政治压力和对快速成果的需求被确定为重大障碍.
- 拟议的框架包括五个关键组成部分:资金,能力建设,数据基础设施,知识翻译平台和证据使用文化.
结论:
- 开发的框架是基于COVID-19流行病的现实世界经验.
- 它旨在指导LMIC的利益相关者建立建模能力,并将建模证据纳入公共卫生决策中.
- 进一步实施和评估是必要的,以验证框架的稳定性和在不同环境中的影响.
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