A decision tool to inform the optimal use of non-pharmaceutical interventions during the COVID-19 pandemic

一个决策工具，用于告知在 COVID-19 大流行期间如何最佳使用非药物干预措施

• 批准号: 10738630
• 负责人: Reza YAESOUBI
• 金额: $ 22.42万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-25 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10738630
• 关键词: 2019-nCoV; Address; Affect; Benchmarking; COVID-19; COVID-19 pandemic; COVID-19 test; Cessation of life; Clinical; Communicable Diseases; Communication; Consumption; Cost Effectiveness Analysis; Data; Decision Modeling; Economics; Education; Effectiveness; Epidemiology; Exposure to; Fatigue; Future; Geography; Health; Health Care Visit; Health Insurance; Hospitalization; Immune response; Immunization; Influenza; Influenza A Virus, H1N1 Subtype; Interruption; Intervention; Lifting; Masks; Measures; Mental Health; Methods; Mission; Modeling; Morbidity - disease rate; Outcome; Persons; Play; Policies; Policy Maker; Politics; Population; Public Health; Quality-Adjusted Life Years; Quarantine; Recommendation; Relaxation; Research; Role; SARS-CoV-2 exposure; SARS-CoV-2 positive; SARS-CoV-2 transmission; Sampling; Schedule; Schools; Social Distance; Societies; Source; Specific qualifier value; Structure; Substance Use Disorder; Target Populations; Time; United States; United States National Institutes of Health; Vaccines; Variant; Virus; Weight; adverse outcome; analytical tool; cost; design; economic disparity; epidemic response; epidemiologic data; evidence base; experience; experimental study; health disparity; improved; income insurance; innovation; intervention cost; marginalization; member; mortality; novel; pandemic disease; pandemic influenza; pandemic response; pathogen exposure; population health; predictive modeling; previous pandemic; programs; school closure; seasonal influenza; social; sociodemographics; tool; vaccine-induced immunity

PROJECT SUMMARY/ABSTRACT As the prospect for the elimination of COVID-19 in the near future remains uncertain, non-pharmaceutical interventions (NPIs) such as limiting social gatherings, quarantine after exposure to the virus, and school closure, will continue to play important roles in mitigating the morbidity and mortality associated with the pandemic. Since these interventions impose immense economic, social, and health-related costs, their use should be recommended only when epidemic control benefits outweigh their adverse consequences. Our overall objective in this proposal is to develop an analytical decision tool to optimize the use of NPIs based on latest information related to the local epidemiology of COVID-19, the effectiveness of different NPIs, and the population’s stated disutility associated with these interventions. This decision tool is structured to provide a transparent mechanism to communicate the rationale for the current policy regarding the use of NPIs and the conditions under which the policy would change. To develop our decision tools, this proposal has three specific aims: 1) to develop state-level decision models that identify the optimal combination of NPIs, in real-time, and based on the projected loss in the quality-adjusted life-years (QALYs) and the disutility borne by the population under various combinations of NPIs under various combinations of NPIs; 2) to design, conduct, and analyze discrete-choice experiments to estimate the disutility weights of different NPIs as borne by population members due to social, economic, and health consequences of these programs; and 3) to estimate the societal tolerance for loss in QALYs due to existing infectious diseases without triggering NPIs. This tolerance threshold can be estimated using historical data related to past pandemic and seasonal influenza and will serve as a benchmark to decide when the burden of COVID-19 is low enough to lift all NPIs, at least for a short term. The research proposed in this project is innovative as it develops a novel, principled approach to consolidate real-time data from three different sources to optimize the use of NPIs: 1) COVID-19 cases, hospitalizations, and deaths as projected by existing and new predictive models of COVID-19 pandemic, 2) effectiveness of various NPIs in breaking the transmission of SARS-CoV-2, and 3) disutility weights of NPIs directly elicited from target populations. The proposed research is significant because it meets the critical needs of policymakers to identify evidence-based and real-time recommendations regarding the efficient use of NPIs to contain the burden of COVID-19. The methods and decision tools developed as part of this project could also be used in responding to other existing and future infectious threats where NPIs are employed.

项目总结/摘要 由于近期消除COVID-19的前景仍不确定，非制药 干预措施（NPI），如限制社交聚会，接触病毒后隔离，以及学校 关闭，将继续发挥重要作用，减少发病率和死亡率与 流行病由于这些干预措施造成巨大的经济、社会和健康相关成本， 只有当流行病控制的好处超过其不利后果时，才应建议采取这种措施。我们 本提案的总体目标是开发一种分析性决策工具，以优化非盈利机构的使用， 有关本地COVID-19流行病学的最新资料、不同非牟利机构的成效，以及 与这些干预措施相关的人口负效用。该决策工具的结构旨在提供 建立透明机制，通报关于使用非盈利机构的现行政策的理由， 在何种情况下，政策会发生变化。为了开发我们的决策工具，该建议有三个具体的 目标：1）开发州一级的决策模型，实时确定NPI的最佳组合，以及 基于质量调整生命年（Qs）的预计损失和人口承担的负效用 在NPI的各种组合下; 2）设计，执行和分析 离散选择实验，以估计人口成员所承担的不同NPI的负效用权重 由于这些计划的社会，经济和健康后果; 3）估计社会容忍度 由于现有传染病而导致的QF损失，而不会触发NPI。该容限阈值可以是 使用与过去大流行和季节性流感有关的历史数据进行估计，并将作为基准 决定COVID-19的负担何时低到足以解除所有NPI，至少在短期内。研究 在这个项目中提出的是创新的，因为它开发了一种新颖的，原则性的方法来巩固实时数据 从三个不同的来源来优化NPI的使用：1）COVID-19病例，住院和死亡， 现有和新的COVID-19大流行预测模型预测，2）各种NPI在 阻断SARS-CoV-2的传播; 3）直接从靶点引出的NPI的负效用权重 人口。拟议的研究意义重大，因为它满足了决策者的关键需求， 确定关于有效利用NPI的循证和实时建议， COVID-19的负担。作为该项目一部分开发的方法和决策工具也可用于 应对使用NPI的其他现有和未来的传染性威胁。