The External Exposome and COVID-19 Severity

外部暴露组和 COVID-19 严重程度

• 批准号: 10174270
• 负责人: Jiang Bian
• 金额: $ 22.19万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-20 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10174270
• 关键词: 2019-nCoV; Affect; Age; Air Pollution; Asthma; COVID-19; Cardiovascular Diseases; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Chronic lung disease; Clinical; Clinical Research; Consumption; Data; Data Sources; Databases; Diabetes Mellitus; Diagnosis; Disease; Elderly; Electronic Health Record; Environment; Environmental Exposure; Environmental Risk Factor; Ethnic group; Exposure to; Florida; Future; Geography; Health; Hospitalization; Individual; Joints; Knowledge; Laboratories; Link; Literature; Mediation; Medical; Methods; Neighborhoods; Obesity; Outcome; Patients; Physicians; Prevention Measures; Primary Prevention; Process; Public Health; Recording of previous events; Resolution; Resources; Review Literature; Risk Factors; Severities; Structure; Symptoms; Testing; Time; Vital Statistics; Work; base; cohort; comorbidity; coronavirus disease; data resource; demographics; deprivation; design; electronic structure; epidemiology study; high risk; improved; mortality; natural language; novel; pandemic disease; patient oriented; personalized intervention; phenotyping algorithm; predictive modeling; racial and ethnic; racial and ethnic disparities; repository; response; scale up; social; social health determinants; sociodemographics; socioeconomics; spatiotemporal; success; systematic review; walkability; young adult

PROJECT SUMMARY The 2019 novel coronavirus disease (COVID-19) is a global pandemic with severe medical and socioeconomic consequences. Young adults without any underlying health conditions can still develop severe COVID-19 disease, and there are racial and ethnic disparities in COVID-19 hospitalization and mortality rates which cannot be explained by age and underlying health conditions alone. Risk factors of severe COVID-19 beyond older age and underlying health conditions are large unknown. There are large overlaps between the currently known risk factors of severe COVID-19 and the health conditions that are affected by environmental exposures, and emerging evidence suggested that long-term environmental exposures may be important determinants of COVID-19 severity. Traditional environmental epidemiological studies usually examine environmental factors separately without considering “the totality of the external environment”. Such studies are not only time consuming as they examine individual exposures separately, but more importantly, cannot account for confounding by co-exposures. The external exposome is an ideal framework to identify novel exposures associated with severe COVID-19 as it can systematically and efficiently screen thousands of environmental exposures. In this project, we will leverage a unique real-world data (RWD) resource – OneFlorida – a large repository of linked electronic health records (EHR), claims and vital statistics data, covering more than 60% of Floridians, contributing to the national Patient-Centered Clinical Research Network (PCORnet). Building on our prior work on the external exposome, we will expand our existing external exposome database to include additional factors that may impact COVID-19 outcomes through a systematic analysis of literature and resources. We aim to (1) develop phenotyping algorithms for identifying a COVID-19 cohort and their severity and extracting associated individual-level risk factors from the OneFlorida real-world data, and (2) identify external exposome factors associated with severe COVID-19, examine how the external exposome contributes to racial and ethnic disparities in severe COVID-19, and build predictive models of severe COVID-19 with external exposome factors. This study will fill important knowledge gaps by providing timely information to understand how environmental exposures may impact COVID-19 severity that will improve identifications of high-risk COVID-19 patients and inform the design of future precision interventions. Our approach and initial results for Florida can (1) be readily scaled up to a multi-state study through PCORnet and (2) answer other novel questions such as the external exposome’s contribution to geographic disparities in COVID-19 outcomes.

项目摘要 2019年新颖的冠状病毒病（Covid-19）是全球大流行，严重的医学和社会经济 结果。没有任何潜在健康状况的年轻人仍然会发展严重的COVID-19 疾病，在199年的住院和死亡率中有种族和种族分布 不能仅靠年龄和基本的健康状况来解释。严重的Covid-19的风险因素超出 年龄较大的健康状况是很大的未知。当前之间有很大的重叠 严重COVID-19的已知危险因素以及受环境影响的健康状况 暴露和新兴证据表明，长期环境暴露可能很重要 COVID-19的严重程度的决定因素。传统的环境流行病学研究通常检查 环境因素分别考虑“外部环境的总体”。这样的研究 不仅要分别检查个人暴露，而且更重要的是，不仅要耗时 由共同曝光混淆。外部展览是识别新颖的理想框架 与严重的共同-19相关的暴露，因为它可以系统地有效地筛选数千个 环境暴露。在这个项目中，我们将利用独特的现实数据（RWD）资源 - Oneflorida - 链接的电子健康记录（EHR）的大量存储库，索赔和生命统计数据， 覆盖60％以上的佛罗里达人，为国家以患者为中心的临床研究网络做出了贡献 （PCORNET）。在我们先前在外部展览体上工作的基础，我们将扩展现有的外部 外向数据库，包括可能通过系统性影响Covid-19结果的其他因素 分析文献和资源。我们的目标是（1）开发表型算法以识别COVID-19 从Oneflorida现实世界中提取相关的个人级别风险因素 数据，以及（2）确定与严重的共同-19相关的外部释放因素，检查外部如何 宣传体有助于严重的共同竞争中的种族和种族分布，并建立了预测模型 严重的COVID-19具有外部释放因子。这项研究将通过提供重要的知识空白 及时的信息以了解环境暴露如何影响COVID-19的严重性 改善对高风险共证患者的识别，并告知未来精确干预措施的设计。 我们的方法和佛罗里达的初始结果可以（1）通过PCORNET很容易缩放到多州研究 （2）回答其他新颖的问题，例如外部展示体对地理分布的贡献 COVID-19结果。