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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

基本信息

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
项目类别：
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

项目摘要

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