The External Exposome and COVID-19 Severity

项目摘要 2019年新型冠状病毒病（COVID-19）是一场全球性大流行病，严重影响了医疗和社会经济发展。 后果没有任何潜在健康状况的年轻人仍然可能发展为严重的COVID-19 在COVID-19住院率和死亡率方面存在种族和民族差异， 不能仅仅用年龄和潜在的健康状况来解释。严重COVID-19的风险因素超出 老年人和潜在的健康状况在很大程度上是未知的。目前， 严重COVID-19的已知风险因素以及受环境影响的健康状况 暴露，新的证据表明，长期的环境暴露可能是重要的 COVID-19严重程度的决定因素。传统的环境流行病学研究通常检查 环境因素单独考虑，而不考虑“外部环境的整体性”。此类研究 不仅耗时，因为他们分别检查个人暴露，但更重要的是， 考虑到共同暴露的混杂。外部困境是小说识别的理想框架 因为它可以系统有效地筛查成千上万的 环境暴露。在这个项目中，我们将利用一个独特的真实世界数据（RWD）资源- OneFlorida -一个大型的链接电子健康记录（EHR），索赔和生命统计数据库， 覆盖超过60%的佛罗里达州，为国家以患者为中心的临床研究网络做出贡献 （PCORnet）。在我们先前关于外部麻烦的工作的基础上，我们将扩大我们现有的外部 复杂的数据库，包括可能通过系统性评估影响COVID-19结果的其他因素 分析文献和资源。我们的目标是（1）开发用于识别COVID-19的表型分析算法 队列及其严重程度，并从OneFlorida现实世界中提取相关的个人水平风险因素 数据，以及（2）识别与严重COVID-19相关的外部麻烦因素，研究外部因素如何影响 在严重的COVID-19中，麻烦导致种族和民族差异，并建立预测模型， 严重的COVID-19和外部麻烦因素。这项研究将填补重要的知识空白，提供 及时的信息，以了解环境暴露如何影响COVID-19的严重程度， 改善对高风险COVID-19患者的识别，并为未来精确干预措施的设计提供信息。 我们的方法和佛罗里达的初步结果可以（1）通过PCORnet容易地扩展到多州研究 和（2）回答其他新的问题，如外部麻烦的贡献，地理差距， COVID-19结果。

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.