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，由于其威胁而造成巨大的疾病和经济负担。 另一种全球性流行病，已投入大量资源加强流感监测、实验室 自 2009 年以来全球范围内的能力和大流行病防备情况。疾病负担估计对于评估至关重要 疫苗的好处，用于传达预防和控制信息，以及用于制定基于证据的 资源分配政策在估计流感疾病方面存在几个主要的分析挑战。 首次流感症状是非特异性的，检测是由医疗保健部门自行决定进行的。 感染后几周可能会出现严重并发症（例如肺炎和心血管事件）。 当不再检测到流感病毒或患者的症状可能不提示流感时。 在国家或全球范围内对流感负担进行政策相关的评估往往受到可用性的限制 一种常见的方法是创建乘数来推断可用的负担。 对其他地点或更多人口的估计，同时引入相当大的不确定性。 迫切需要开发支持负担估算的方法和工具，以提高准确性，改进 在这个为期 2 年的探索中，提高准确性、加强多伙伴协作并适当量化不确定性。 项目中，我们将研究从流行病学和证据合成到 在目标 1 中，我们将开发单点时间序列模型来归因计数。 我们的模型将解决流感对呼吸系统健康的不良后果。 分析挑战，包括残余时间自相关、过度离散和未测量的时间 通过利用独特的多州紧急部门 (ED) 访问数据库和三个国家 流感监测系统，这些方法将用于估计特定季节流感相关的 2005 年至 2018 年期间，美国有 102 名急诊科医生就诊。我们将估计特定年龄组、性别和年龄的负担 在目标 2 中，我们将开发数据集成模型来整合多个站点的信息。 并对站点进行预测而无需进行负担估计，这涉及使用隐私保护的分布式技术。 用于多站点分析的算法，可以合并个体参与者数据，提高准确性，解释 报告偏差，并可能鼓励参与的方法将应用于（1）估计年度季节- 美国全国范围内与流感相关的特定急诊就诊次数，以及 (2) 估计全球流感负担 作为与美国疾病控制中心持续合作的一部分，相关住院治疗和 该项目的预期成果包括（1）可行性和绩效评估。 拟议的时间序列和数据整合模型；以及（2）流感相关发病率的实质性发现 此外，根据呼吸系统疾病的急诊就​​诊和住院情况进行测量。 也广泛适用于其他呼吸道病原体。