Lister Hill National Center for Biomedical Communications, National Library of Medicine, NIH, 8600 Rockville Pike, Bethesda, MD, 20894, USA.
BMC Med Inform Decis Mak. 2024 Oct 31;24(1):321. doi: 10.1186/s12911-024-02743-5.
There have been many efforts to expand existing data collection initiatives to include COVID-19 related data. One program that expanded is UK Biobank, a large-scale research and biomedical data collection resource that added several COVID-19 related data fields including questionnaires (exposures and symptoms), viral testing, and serological data. This study aimed to analyze this COVID-19 data to understand how COVID-19 data was collected and how it can be used to attribute COVID-19 and analyze differences in cohorts and time periods.
A cohort of COVID-19 infected individuals was defined from the UK Biobank population using viral testing, diagnosis, and self-reported data. Changes over time, from March 2020 to October 2021, in total case counts and changes in case counts by identification source (diagnosis from EHR, measurement from viral testing and self-reported from questionnaire) were also analyzed. For the questionnaires, an analysis of the structure and dynamics of the questionnaires was done which included the amount and type of questions asked, how often and how many individuals answered the questions and what responses were given. In addition, the amount of individuals who provided responses regarding different time segments covered by the questionnaire was calculated along with how often responses changed. The analysis included changes in population level responses over time. The analyses were repeated for COVID and non-COVID individuals and compared responses.
There were 62 042 distinct participants who had COVID-19, with 49 120 identified through diagnosis, 30 553 identified through viral testing and 934 identified through self-reporting, with many identified in multiple methods. This included vast changes in overall cases and distribution of case data source over time. 6 899 of 9 952 participants completing the exposure questionnaire responded regarding every time period covered by the questionnaire including large changes in response over time. The most common change came for employment situation, which was changed by 74.78% of individuals from the first to last time of asking. On a population level, there were changes as face mask usage increased each successive time period. There were decreases in nearly every COVID-19 symptom from the first to the second questionnaire. When comparing COVID to non-COVID participants, COVID participants were more commonly keyworkers (COVID: 33.76%, non-COVID: 15.00%) and more often lived with young people attending school (61.70%, 45.32%).
To develop a robust cohort of COVID-19 participants from the UK Biobank population, multiple types of data were needed. The differences based on time and exposures show the important of comprehensive data capture and the utility of COVID-19 related questionnaire data.
为了包括与 COVID-19 相关的数据，已经有许多努力来扩展现有的数据收集计划。一个扩展的项目是 UK Biobank，这是一个大型的研究和生物医学数据收集资源，增加了几个与 COVID-19 相关的数据字段，包括问卷（暴露和症状）、病毒检测和血清学数据。本研究旨在分析这些 COVID-19 数据，以了解 COVID-19 数据是如何收集的，以及如何利用这些数据来归因 COVID-19 并分析不同队列和时间段的差异。
使用病毒检测、诊断和自我报告数据，从 UK Biobank 人群中定义 COVID-19 感染个体的队列。还分析了从 2020 年 3 月到 2021 年 10 月，总病例数和不同识别源（从电子健康记录中诊断、从病毒检测中测量和从问卷中自我报告）的病例数变化。对于问卷，分析了问卷的结构和动态，包括提出的问题的数量和类型、回答问题的频率和人数以及给出的回答。此外，还计算了提供问卷中不同时间段的回复的个体数量，以及回复的频率变化。该分析包括随时间变化的人群水平反应。还对 COVID 和非 COVID 个体进行了重复分析，并比较了反应。
有 62042 名参与者患有 COVID-19，其中 49120 名通过诊断识别，30553 名通过病毒检测识别，934 名通过自我报告识别，许多人通过多种方法识别。这包括总体病例数和病例数据来源分布随时间的巨大变化。6899 名完成暴露问卷的 9952 名参与者中的 934 名参与者回答了问卷涵盖的每个时间段，包括随时间的巨大变化。最常见的变化发生在就业情况上，有 74.78%的人在首次和最后一次询问时改变了这种情况。在人群水平上，随着每个连续时间段的口罩使用增加，情况发生了变化。几乎每个 COVID-19 症状从第一份问卷到第二份问卷都有所减少。与非 COVID 参与者相比，COVID 参与者更常见的是关键工作者（COVID：33.76%，非 COVID：15.00%），并且更经常与上学的年轻人住在一起（61.70%，45.32%）。
为了从 UK Biobank 人群中建立一个稳健的 COVID-19 参与者队列，需要多种类型的数据。基于时间和暴露的差异表明，全面数据捕获的重要性和 COVID-19 相关问卷数据的实用性。
