Methods for Estimating Disease Burden of Seasonal Influenza

流感是一种常见的呼吸道感染，疾病和经济负担都很大。由于威胁到 另一场全球大流行，已投入大量资源加强流感监测，实验室 自2009年以来，世界各地的能力和大流行防备。疾病负担评估是评估 疫苗的益处，用于交流预防和控制信息，以及发展循证证据 资源分配政策。在估计流感疾病时，有几个主要的分析挑战 负担。首先，流感症状是非特异性的，检测由医疗保健部门自行决定。 供应商。严重并发症(如肺炎和心血管事件)可能在感染几周后发生 当不再检测到流感病毒，或者患者的症状可能不表明是流感时。第二, 在国家或全球范围内对流感负担进行政策性评估往往受到可获得性的限制 高质量的监控数据。一种常见的方法是创建乘数来外推可用负担 对其他地点或更大人口的估计，同时带来了相当大的不确定性。有一个 迫切需要开发支持负担估计的方法和工具，以提高准确性、改善 精确度，增强多合作伙伴协作，并适当量化不确定性。在这个为期两年的探索中 项目中，我们将研究从流行病学到证据合成的最新方法的使用 流感负担估算。在目标1中，我们将开发单站点时间序列模型来归因于 流感对呼吸道健康造成的不利后果。我们的模型将解决几个常见的 分析挑战，包括剩余时间自相关、过度分散和不可测量的时间 混血儿。通过利用独特的多个州紧急情况部门(ED)访问数据库和三个国家 流感监测系统，这些方法将被应用于估计特定季节的流感相关 2005至2018年间，ED对美国进行了102次访问。我们将估计特定年龄组、性别和 流感类型。在目标2中，我们将开发用于组合多个站点的信息的数据集成模型 并在没有估计负担的情况下对站点进行预测。这涉及到使用隐私保护、分布式 用于多站点分析的算法，可以合并单个参与者数据，提高准确性，说明 报道偏见，并有可能鼓励参与。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室访问，以及(2)估计全球流感负担- 相关住院治疗是与美国疾病控制和预防中心持续合作的一部分 预防。该项目的预期成果包括：(1)对 拟议的时间序列和数据整合模型；以及(2)关于流感相关发病率的实质性结论 通过急诊科就诊和因呼吸系统疾病住院来衡量。此外，在本项目中开发的模型 也广泛适用于其他呼吸道病原体。

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.