Methods for Estimating Disease Burden of Seasonal Influenza

估计季节性流感疾病负担的方法

• 批准号: 10682150
• 负责人: Howard H Chang
• 金额: $ 24.7万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-06 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10682150
• 关键词: 2019-nCoV; Adopted; Age; Algorithms; Antiviral Agents; Attention; Cardiovascular system; Centers for Disease Control and Prevention (U.S.); Cessation of life; Cities; Collaborations; Communicable Diseases; Communication; Communities; Computer software; Data; Databases; Development; Economic Burden; Emergency department visit; Environmental Epidemiology; Epidemiology; Evaluation; Event; Future; Health; Health Personnel; Hospitalization; Immunization Programs; Individual; Infection; Influenza; Influenza vaccination; Infrastructure; Knowledge; Laboratories; Location; Measures; Methodology; Methods; Modeling; Morbidity - disease rate; Outcome; Participant; Patients; Performance; Pneumonia; Policies; Population; Population Heterogeneity; Prevention; Process; Proxy; Reporting; Residual state; Resource Allocation; Resources; Respiratory Disease; Respiratory Tract Infections; Respiratory syncytial virus; Seasons; Series; Site; Statistical Models; Symptoms; System; Testing; Time; Uncertainty; United States; World Health Organization; age group; burden of illness; data integration; disease transmission; evidence base; flu activity; improved; influenza surveillance; influenzavirus; interest; low and middle-income countries; novel; pandemic disease; pandemic preparedness; privacy preservation; respiratory; respiratory health; respiratory morbidity; respiratory pathogen; seasonal influenza; sex; surveillance data; tool; vaccine evaluation

Influenza is a common respiratory infection with substantial disease and economic burdens. Due to the threat of another global pandemic, significant resources have been devoted to increase influenza surveillance, laboratory capacity and pandemic preparedness worldwide since 2009. Disease burden estimates are critical for evaluating vaccine benefits, for communicating prevention and control messages, and for developing evidence-based policies for resource allocations. There are several major analytical challenges in estimating influenza disease burden. First influenza symptoms are non-specific and testing is conducted at the discretion of healthcare providers. Severe complications (e.g., pneumonia and cardiovascular events) may occur weeks after infection when influenza viruses are no longer detectable or the patient’s symptoms may not suggest influenza. Second, policy-relevant evaluation of influenza burdens at the national or global scales are often limited by the availability of high-quality surveillance data. A common approach is to create multipliers for extrapolating available burden estimates to other locations or larger populations, while introducing considerable uncertainties. There is a pressing need to develop methods and tools to support burden estimation that will increase accuracy, improve precision, enhance multi-partner collaboration, and quantify uncertainty appropriately. In this 2-year exploratory project, we will examine the use of state-of-the-art approaches from epidemiology and evidence synthesis to influenza burden estimation. In Aim 1, we will develop single-site time-series models for attributing counts of adverse respiratory health outcomes to influenza. Our models will address several commonly encountered analytic challenges, including residual temporal autocorrelation, overdispersion, and unmeasured temporal confounders. By leveraging a unique multi-state emergency department (ED) visits database and three national influenza surveillance systems, these methods will be applied to estimate season-specific influenza-associated ED visits for 102 U.S. during the period 2005 to 2018. We will estimate burdens for specific age groups, sex and influenza types. In Aim 2, we will develop data integration models for combining information across multiple sites and perform predictions to sites without burden estimates. This involves the use of privacy-preserving, distributed algorithms for multi-site analyses that can incorporate individual participant data, improve accuracy, account for reporting bias, and potentially encourage participation. Methods will be applied to (1) estimate annual season- specific influenza-associated ED visits in the U.S. nationally, and (2) estimate global burden of influenza- associated hospitalization as part of an ongoing collaboration with the U.S. Centers for Disease Control and Prevention. Anticipated outcomes from this project include (1) feasibility and performance evaluations of the proposed time-series and data integration models; and (2) substantive findings on influenza-associated morbidity as measured by ED visits and hospitalization for respiratory disease. Moreover, models developed in this project are also widely applicable to other respiratory pathogens.

流感是一种常见的呼吸道感染，具有实质性疾病和经济伯恩斯。由于威胁 另一个全球大流行的大量资源已致力于增加影响监视，实验室 自2009年以来，全球能力和大流行的准备。疾病燃烧估计对于评估至关重要 疫苗益处，用于通信预防和控制信息，以及开发基于证据的信息 资源分配的政策。估计影响力疾病有一些主要的分析挑战 负担。第一个影响力症状是非特异性的，并且由医疗保健决定进行测试 提供者。严重的并发症（例如肺炎和心血管事件）可能发生在感染后数周 当影响者病毒不再被发现或患者的症状可能不建议影响者时。第二， 在国家或全球范围内对造成影响的影响的与政策相关的评估通常受到可用性的限制 高质量的监视数据。一种常见的方法是创建用于推断可用伯恩的乘数 在引入相当大的不确定性的同时，对其他位置或更大的人群进行了估计。有一个 迫切需要开发方法和工具来支持燃烧估算，以提高准确性，提高 精确，增强多方协作并适当量化不确定性。在这个为期两年的探索性 项目，我们将研究流行病学和证据综合的最先进方法的使用 在AIM 1中，我们将开发单点时间序列模型以归因计数 影响力的不良呼吸健康结果。我们的模型将解决一些常见的几个 分析挑战，包括残留的临时自相关，过度分散和未得到的临时性 混淆者。通过利用独特的多州急诊科（ED）访问数据库和三个国家 影响力监视系统，这些方法将应用于估计与季节相关的季节特定影响 在2005年至2018年期间，ED访问102美国。我们将估计特定年龄段，性别和性别的伯恩斯 影响力类型。在AIM 2中，我们将开发数据集成模型，以组合多个站点的信息 并对没有伯宁估计的站点进行预测。这涉及使用隐私，分布式 多站点分析的算法，可以合并单个参与者数据，提高准确性，说明 报告偏见，并有可能鼓励参与。方法将应用于（1）估算年度季节 - 与全国各地的特定影响力相关的ED访问，（2）估计影响力的全球燃烧 与美国疾病控制中心进行的持续合作的一部分相关的住院和 预防。该项目的预期结果包括（1）可行性和绩效评估 提出的时间序列和数据集成模型； （2）有关影响扎相关的发病率的实质性发现 通过ED访问和住院治疗呼吸道疾病。此外，该项目开发的模型 也广泛适用于其他呼吸道病原体。