Prospective annual estimates of influenza vaccine effectiveness and burden of disease

流感疫苗有效性和疾病负担的前瞻性年度估计

• 批准号: 9323271
• 负责人: Michael L Jackson
• 金额: $ 79.83万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9323271
• 关键词: Prospective; annual; estimates; influenza; vaccine

Summary/Abstract Although influenza vaccination is the best available tool for reducing illnesses and deaths due to influenza, influenza vaccine effectiveness (VE) can vary substantially from year to year, depending on the antigenic match between circulating viruses and vaccine strains. To guide the ongoing development of influenza vaccination recommendations, we propose to conduct annual estimates of influenza VE, influenza burden of illness, and cases prevented by vaccination. We will conduct active surveillance for medically attended, laboratory-confirmed influenza in a predefined cohort. We will identify patients seeking ambulatory care for acute respiratory illness; eligible and consenting patients will be enrolled in the study. We will collect specimens for respiratory virus testing from all participants, which will be tested for influenza (including type, subtype, and lineage) via nucleic acid amplification. We will determine risk factors for influenza, and illness outcomes, through a combination of questionnaires and administrative healthcare databases. We will determine subjects' influenza vaccination history through self-report, validated using an immunization registry. Data will be shared with CDC and other participating sites to provide mid-season and end-of-season VE estimates. We will estimate VE using a test-negative design, comparing the odds of vaccination among subjects who test positive for influenza with the odds among subjects testing negative. We will provide annual estimates stratified by virus type/subtype/lineage and by age group. Because we are identifying patients with influenza from a defined cohort, we will also estimate the incidence of medically attended influenza in our study population, and estimate the number of influenza cases averted by vaccination. This project will also serve as a resource for studying VE and epidemiology of a novel influenza virus, should an influenza pandemic occur during the study period. We will work with CDC and other sites to prepare and pilot-test protocols for pandemic studies. In addition, this project provides a platform for respiratory syncytial virus (RSV) surveillance, which can provide important data on the epidemiology of RSV prior to licensure of RSV vaccines. We will test specimens for RSV and estimate the incidence of medically attended RSV in our study population. Finally, we will use data collected from this study to further explore potential biases and limitations of the test-negative design and to anticipate possible effects of RSV vaccine licensure on influenza VE estimates from test-negative studies. The proposed research will 1) generate data to guide influenza prevention actions and recommendations; 2) provide baseline data on RSV incidence prior to vaccine licensure; and 3) enhance our understanding of the test-negative study design.

摘要/摘要 虽然接种流感疫苗是减少因流感导致的疾病和死亡的最佳工具， 流感疫苗的效力(VE)每年可能有很大的不同，这取决于抗原 正在传播的病毒与疫苗毒株的匹配。指导流感的持续发展 疫苗接种建议，我们建议每年进行流感VE、流感负担的估算 疾病，以及通过接种疫苗预防的病例。我们将对有医疗护理的人进行积极监测， 实验室确认的预定义队列中的流感。我们将确定寻求门诊护理的患者 急性呼吸道疾病；符合条件的和同意的患者将被纳入研究。我们会收集 所有参与者的呼吸道病毒测试样本，将进行流感测试(包括流感类型， 亚型和谱系)通过核酸扩增。我们将确定流感和疾病的危险因素 结果，通过问卷调查和行政保健数据库相结合。我们会 通过自我报告确定受试者的流感疫苗接种史，并通过免疫登记进行验证。 数据将与CDC和其他参与站点共享，以提供赛季中期和季末VE 估计。我们将使用测试阴性设计来估计VE，比较以下人群接种疫苗的几率 流感检测呈阳性的受试者，受试者中检测为阴性的几率。我们将每年为您提供 按病毒类型/亚型/谱系和年龄组分层的估计。因为我们正在确认患者是否患有 在我们的研究中，我们还将估计有医疗护理的流感的发病率。 并估计通过接种疫苗避免的流感病例数量。 该项目还将作为研究VE和一种新型流感病毒流行病学的资源， 如果在研究期间发生流感大流行。我们将与疾控中心和其他网站合作准备 以及用于大流行研究的试点测试方案。此外，本项目还为呼吸系统提供了一个平台 合胞病毒(RSV)监测，可提供有关RSV流行病学的重要数据 RSV疫苗的许可。我们将对样本进行RSV检测，并估计接受医疗护理的发病率 我们研究人群中的呼吸道合胞病毒。最后，我们将使用从这项研究中收集的数据来进一步探索潜力 试验阴性设计的偏差和局限性以及预测RSV疫苗许可的可能影响 关于来自试验阴性研究的流感VE估计。 拟议的研究将1)产生数据以指导流感预防行动和 建议；2)在疫苗获得许可之前提供RSV发病率的基线数据；以及3)加强我们的 理解测试阴性研究设计。