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.

项目概要/摘要 由于在不久的将来消除 COVID-19 的前景仍不确定，非药物 干预措施 (NPI)，例如限制社交聚会、接触病毒后的隔离以及上学 关闭，将继续在减轻与该病相关的发病率和死亡率方面发挥重要作用 由于这些干预措施造成了巨大的经济、社会和健康相关成本，因此它们的使用 只有当疫情控制的益处大于其不利后果时才应推荐。 该提案的总体目标是开发一种分析决策工具，以优化基于 NPI 的使用 与当地 COVID-19 流行病学、不同 NPI 的有效性以及 该决策工具旨在提供与这些干预措施相关的人口的负效用。 透明的机制，用于传达有关使用 NPI 的当前政策的基本原理和 为了开发我们的决策工具，该提案提出了三个具体条件。 目标：1) 开发州级决策模型，实时识别 NPI 的最佳组合，并 基于质量调整生命年 (QALY) 的预计损失和人口承担的负效用 2）设计、实施和分析 离散选择实验来估计人口成员承担的不同非营利机构的负效用权重 由于这些计划的社会、经济和健康后果；以及 3) 估计社会容忍度 对于由于现有传染病而导致的 QALY 损失而不触发 NPI，此耐受阈值可以是。 使用与过去大流行和季节性流感相关的历史数据进行估计，并将作为基准 确定何时 COVID-19 的负担足够低以减轻所有非营利机构的负担，至少在短期内如此。 该项目中提出的方法具有创新性，因为它开发了一种新颖的、有原则的方法来整合实时数据 来自三个不同来源的数据，以优化 NPI 的使用：1) COVID-19 病例、住院和死亡情况 根据现有和新的 COVID-19 大流行预测模型进行预测，2) 各种 NPI 的有效性 打破 SARS-CoV-2 的传播，以及 3）直接从目标引出的 NPI 的负效用权重 拟议的研究意义重大，因为它满足了政策制定者的关键需​​求。 确定有关有效利用 NPI 的基于证据的实时建议，以遏制 作为该项目一部分开发的方法和决策工具也可用于 应对 NPI 参与的其他现有和未来的感染威胁。