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拥抱传染病应对中动态公共卫生政策的影响:利用实施科学来改善实践。

Embracing dynamic public health policy impacts in infectious diseases responses: leveraging implementation science to improve practice.

机构信息

VA Boston Healthcare System, Department of Medicine, Section of Infectious Diseases, Boston, MA, United States.

VA Center for Healthcare Organization and Implementation Research (CHOIR), Boston, MA, United States.

出版信息

Front Public Health. 2023 Aug 17;11:1207679. doi: 10.3389/fpubh.2023.1207679. eCollection 2023.


DOI:10.3389/fpubh.2023.1207679
PMID:37663826
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10469790/
Abstract

RATIONALE: The host-pathogen relationship is inherently dynamic and constantly evolving. Applying an implementation science lens to policy evaluation suggests that policy impacts are variable depending upon key implementation outcomes (feasibility, acceptability, appropriateness costs) and conditions and contexts. COVID-19 CASE STUDY: Experiences with non-pharmaceutical interventions (NPIs) including masking, testing, and social distancing/business and school closures during the COVID-19 pandemic response highlight the importance of considering public health policy impacts through an implementation science lens of constantly evolving contexts, conditions, evidence, and public perceptions. As implementation outcomes (feasibility, acceptability) changed, the effectiveness of these interventions changed thereby altering public health policy impact. Sustainment of behavioral change may be a key factor determining the duration of effectiveness and ultimate impact of pandemic policy recommendations, particularly for interventions that require ongoing compliance at the level of the individual. PRACTICAL FRAMEWORK FOR ASSESSING AND EVALUATING PANDEMIC POLICY: Updating public health policy recommendations as more data and alternative interventions become available is the evidence-based policy approach and grounded in principles of implementation science and dynamic sustainability. Achieving the ideal of real-time policy updates requires improvements in public health data collection and analysis infrastructure and a shift in public health messaging to incorporate uncertainty and the necessity of ongoing changes. In this review, the Dynamic Infectious Diseases Public Health Response Framework is presented as a model with a practical tool for iteratively incorporating implementation outcomes into public health policy design with the aim of sustaining benefits and identifying when policies are no longer functioning as intended and need to be adapted or de-implemented. CONCLUSIONS AND IMPLICATIONS: Real-time decision making requires sensitivity to conditions on the ground and adaptation of interventions at all levels. When asking about the public health effectiveness and impact of non-pharmaceutical interventions, the focus should be on , and they can achieve public health impact. In the future, rather than focusing on models of public health intervention effectiveness that assume static impacts, policy impacts should be considered as dynamic with ongoing re-evaluation as conditions change to meet the ongoing needs of the ultimate end-user of the intervention: the public.

摘要

背景:宿主-病原体关系本质上是动态的,且不断演变。将实施科学视角应用于政策评估表明,政策的影响因关键实施结果(可行性、可接受性、适宜性、成本)以及条件和背景而异。

新冠疫情案例研究:在 COVID-19 大流行应对期间,非药物干预措施(包括口罩、检测和社交距离/企业和学校关闭)的经验强调了通过不断变化的背景、条件、证据和公众认知的实施科学视角来考虑公共卫生政策影响的重要性。随着实施结果(可行性、可接受性)的变化,这些干预措施的有效性也发生了变化,从而改变了公共卫生政策的影响。行为改变的维持可能是决定大流行政策建议有效性和最终影响持续时间的关键因素,特别是对于需要个人持续遵守的干预措施。

评估和评估大流行政策的实用框架:随着更多数据和替代干预措施的出现,更新公共卫生政策建议是循证政策方法,并且基于实施科学和动态可持续性原则。实现实时政策更新的理想目标需要改善公共卫生数据收集和分析基础设施,并转变公共卫生信息传递方式,以纳入不确定性和持续变化的必要性。在本综述中,提出了动态传染病公共卫生应对框架,作为一个模型,具有实用工具,可将实施结果迭代地纳入公共卫生政策设计中,以维持效益,并确定政策何时不再按预期发挥作用,需要进行调整或取消。

结论和意义:实时决策需要对现场条件保持敏感,并在各个层面上调整干预措施。在询问非药物干预措施的公共卫生效果和影响时,重点应该是干预措施是否可以实现公共卫生影响,以及它们可以实现公共卫生影响。在未来,与其关注假设静态影响的公共卫生干预措施有效性模型,不如将政策影响视为动态的,并在条件发生变化以满足干预措施最终用户(公众)的持续需求时进行持续重新评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/e940fe514743/fpubh-11-1207679-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/7f5585e62eaa/fpubh-11-1207679-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/8deb3ffb141d/fpubh-11-1207679-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/5b864e982743/fpubh-11-1207679-g0003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/ed8b917fd9ed/fpubh-11-1207679-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/a5630978fc85/fpubh-11-1207679-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/e940fe514743/fpubh-11-1207679-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/7f5585e62eaa/fpubh-11-1207679-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/8deb3ffb141d/fpubh-11-1207679-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/5b864e982743/fpubh-11-1207679-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/5d6049ba068c/fpubh-11-1207679-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/ed8b917fd9ed/fpubh-11-1207679-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/a5630978fc85/fpubh-11-1207679-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/10469790/e940fe514743/fpubh-11-1207679-g0007.jpg

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