Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data

识别和理解临床 COVID-19 数据中选择偏差和信息偏差的驱动因素

• 批准号: 10192372
• 负责人: Nicole Gray Weiskopf
• 金额: $ 20.1万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10192372
• 关键词: Academy; Address; Affect; Age; American; Automobile Driving; Behavioral; COVID-19; COVID-19 pandemic; COVID-19 patient; COVID-19 surveillance; Caring; Categories; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Clinical Data; Clinical Research; Community Surveys; Country; Data; Data Collection; Data Set; Data Sources; Decision Making; Disease; Electronic Health Record; Epidemiology; Ethnic Origin; Evaluation; Geography; Goals; Health; Health Sciences; Health Services Accessibility; Healthcare; Individual; Knowledge; Lead; Learning; Manuals; Measures; Mediating; Medical; Medicine; Oregon; Patient Care; Patients; Policies; Population; Prevalence; Probability; Race; Research; Resource Allocation; Resources; Retrospective Studies; Risk; Role; Sampling; Selection Bias; Source; Structure; Test Result; Testing; Time; Universities; Work; authority; care seeking; cohort; coronavirus disease; cost; data repository; data reuse; epidemiology study; health care availability; health care disparity; health care service utilization; health data; improved; member; multiple data sources; novel; operation; outcome prediction; pandemic disease; population health; prospective; sex; social; social determinants; social factors; social health determinants; social media; surveillance data; therapy development; trustworthiness

Project Summary / Abstract During the COVID-19 pandemic, there is an immediate need for high-quality data for studies that support patient care, predict outcomes, identify and evaluate treatments, allocate resources, and make operations and policy decisions. While prospective research produces higher-quality evidence, retrospective studies that reuse clinical data can be executed in a shorter time frame and for less cost, both of which are crucial for research in a pandemic. Unfortunately, it has been shown that the usefulness and validity of available COVID-19 data are constrained by various forms of selection bias and information bias, which may lead to non-valid findings in research and analytics and disparities in resulting healthcare practices. The objective of the proposed work is to study the selection and information biases present in clinically derived COVID-19 datasets by integrating COVID-19 datasets from OHSU and the National COVID Cohort Collaborative with novel and traditional sources of clinical, epidemiological, social media, and citizen-generated data. From each data source we will extract data indicating COVID-19, as well as a set of social determinants of health that are commonly associated with healthcare utilization and access. To test for the presence of selection bias, we will construct and compare categorical probability distributions for each social determinant across COVID-19 cases in each data source. Differences in these distributions will indicate selection bias in one or more of the data sources. Next we will determine information bias by extending and adapting tests for missingness and other forms of information bias in the COVID-19 datasets to determine if the quantity and quality of these data vary with respect to clinical factors and those related to social determinants of health. This proposal therefore addresses a significant gap in knowledge: understanding not just the disparities in who is impacted by COVID-19, but who is represented by the data we have available for learning more about the disease. The identification and estimation the influence of social determinants of health on selection bias and information bias in COVID-19 data can guide the use of statistical and analytic approaches that can improve the external and internal validity of research and analytics that rely on these data, including estimates of disease prevalence, understanding the natural course of COVID-19, and identifying patients who are at risk for severe disease.

项目摘要 /摘要 在COVID-19大流行期间，直接需要研究高质量数据，以进行研究 支持患者护理，预测结果，识别和评估治疗，分配资源，并使 运营和政策决策。前瞻性研究产生更高质量的证据，但回顾性 重复使用临床数据的研究可以在较短的时间范围内执行，成本更低，这两者都是 对于大流行的研究至关重要。不幸的是，已经证明 可用的Covid-19数据受到各种形式的选择偏见和信息偏见的约束，这可能 导致研究和分析和差异的非vali虫发现，导致医疗保健实践。 拟议工作的目的是研究临床上存在的选择和信息偏见 通过集成OHSU和国家COVID队列的COVID-19数据集，派生的COVID-19数据集 与临床，流行病学，社交媒体和公民生成的新颖和传统来源合作 数据。从每个数据源我们将提取指示COVID-19的数据以及一组社会决定因素 通常与医疗保健利用和获取有关的健康。测试存在 选择偏见，我们将为每个社会决定因素构建和比较分类概率分布 在每个数据源中的COVID-19案例中。这些分布的差异将表明选择偏见 一个或多个数据源。接下来，我们将通过扩展和调整测试来确定信息偏见 COVID-19数据集中的缺失性和其他形式的信息偏见，以确定数量和数量是否 这些数据的质量因临床因素以及与健康的社会决定因素有关。 因此，该提议解决了知识的重大差距：不仅了解差异 谁受到covid-19的影响，但谁由我们可以了解更多数据的数据代表 关于疾病。识别和估计健康决定因素对选择的影响 COVID-19数据中的偏差和信息偏见可以指导使用统计和分析方法的使用 提高依赖这些数据的研究和分析的外部和内部有效性，包括估计 疾病患病率，了解19009的自然过程，并确定有风险的患者 用于严重疾病。