How can pooling health record data improve influenza research?

挑战

COVID-19疫苗显著降低了感染者的发病率和死亡率, 然而，还需要做更多的工作来充分了解它们对不同人群的效果, in different real-world conditions, 和 to learn more about the symptoms, 结果, 和 complications from this disease.

To answer these questions, 美国疾病控制与预防中心(CDC)利用现有的虚拟网络建立了VISION疫苗有效性(VE)网络, 包括VISION流感(流感)网络，以快速整合来自美国9个医疗系统的数据.S.:

• 贝勒斯科特 & White Health (Texas)
• Columbia University Irving Medical Center (New York)
• Children’s Minnesota
• HealthPartners (Minnesota 和 Wisconsin)
• Intermountain Healthcare (Utah)
• Kaiser Permanente Northwest (Oregon 和 Washington)
• Regenstrief Institute (Indiana)
• University of Colorado
• V和erbilt University Medical Center (Tennessee)

Needing to engage a large data management team of skilled epidemiologists, 生物统计学, 和IT专家, 能够开发和使用先进的数据收集和分析工具以及多模式流程, CDC selected 趣赢平台 in 2020 to serve as the VISION Data Coordinating Center. 该项目需要与VISION网络的临床站点和合作伙伴合作，协调统一的协议和数据管理系统，以便迅速和持续地收集有关疫苗接种的数据, 测试, 和 COVID-19-like illness. 趣赢平台 provides analytic support to measure the effectiveness of COVID-19 vaccines, 为保护公众免受这些疾病的破坏而制定的公共卫生政策提供了哪些信息.

解决方案

按社会人口状况和高危人群分析COVID-19疫苗有效性和临床流行病学的关键问题, 趣赢平台与9个卫生系统和伙伴研究组织建立了合作关系，以收集门诊与covid -19相关的医疗结果数据, 紧急护理, emergency department, 和 hospital 设置.

We developed a common protocol for harmonized data collection across sites; coordinated approval of the study protocol 和 data dictionary by a common institutional review board (趣赢平台’s IRB); 和 constructed a research data platform for site data collection.

The 9 health systems contribute test-negative design data (biweekly) 和, for a subset of sites, 来自电子健康记录(EHRs)和与SARS-CoV-2检测相关的疫苗登记的队列数据(每月), COVID-19 疫苗接种s, COVID-19-associated medical events, 还有严重的后果. Sites collect information on demographics, clinical characteristics, 出院诊断, SARS-CoV-2测试, 与covid -19样疾病相关的医疗事件的个人的疫苗接种状况.

Data are sent from the sites to 趣赢平台 using a secure data pipeline. 然后，我们进行质量检查，并将数据与其他衍生变量(如潜在的医疗条件)合并到分析数据集中, which are defined based on 出院诊断 for each encounter). These datasets are sent to CDC on a biweekly basis. This biweekly timeline is vital to informing key public health policies, such as booster recommendations, 和 resource allocations. 我们的项目人员与现场人员一起工作，以促进准确和完整的测试报告, 疫苗接种, 和 medical encounter data. 趣赢平台 staff also combine data across platforms 和 sites, conduct data cleaning 和 quality control, produce analytic datasets, 和 take the lead on analyses, 包括免疫功能低下人群和孕妇的COVID疫苗有效性(VE), 渐弱的COVID - VE, VE during different SARS-CoV-2 variant eras, 和, 最近, 流感(流感)VE.

趣赢平台 uses machine learning-based methods 和 complex analytics (e.g., gradient-boosted regression trees) to help CDC learn more about vaccine-induced immunity. By employing Amazon Web 服务 (AWS), 我们能够在中央数据库中捕获和协调数据，并制定了质量保证和控制程序. Our staff have developed their own R packages to analyze the data, 和 manage a shared Git repository for central code distribution 和 reproducibility.

结果

韦斯特的研究扩大了疾病预防控制中心对COVID-19和流感疫苗在不同人群中的实际有效性的认识, including those who are immunocompromised. It is also contributing to CDC’s underst和ing of the signs 和 symptoms associated with COVID-19 illness; who is receiving each vaccine; 和 how different groups of people respond to it. 这些研究为疾病预防控制中心提供了它所需要的关键情报，以制定有关美国人如何保护自己免受这两种疾病侵害的建议.

重要的是, should other pathogens emerge, VISION的结构将能够利用该平台快速开展这些疾病的研究，并评估为对抗这些疾病而开发的任何疫苗所提供的保护水平.

自2020年以来, 趣赢平台’s rapidly generated vaccine effectiveness estimates have informed CDC, the Advisory Committee on Immunization Practices, the Food 和 Drug Administration (FDA), 以及白宫利益相关者，他们正在考虑下一步措施，保护公众免受流感和COVID-19的侵害. The data have also been published in over a dozen high-impact publications, as well as covered in major news media.