The External Exposome and COVID-19 Severity

项目总结 2019年新型冠状病毒病(新冠肺炎)是一种具有严重医学和社会经济意义的全球性大流行 后果。没有任何潜在健康状况的年轻人仍然可以发展为严重的新冠肺炎 在新冠肺炎的住院率和死亡率方面存在种族和民族差异 不能仅用年龄和潜在的健康状况来解释。严重新冠肺炎超越的危险因素 年龄和潜在的健康状况是一个很大的未知数。在当前的 严重新冠肺炎的已知危险因素和受环境影响的健康状况 暴露，新出现的证据表明，长期的环境暴露可能是重要的 新冠肺炎严重程度的决定因素。传统的环境流行病学研究通常会检查 单独考虑环境因素，而不考虑“外部环境的总体”。这类研究 不仅耗时，因为他们单独检查个人接触，更重要的是，不能 解释了共同曝光造成的混乱。外部曝光是识别小说的理想框架 与严重新冠肺炎相关的暴露，因为它可以系统和高效地筛选数以千计的 暴露在环境中。在本项目中，我们将利用一种独特的真实数据(RWD)资源- One佛罗里达州-链接的电子健康记录(EHR)、索赔和生命统计数据的大型存储库， 覆盖超过60%的佛罗里达州人，为全国以患者为中心的临床研究网络做出贡献 (PCORnet)。在我们之前关于外部曝光的工作的基础上，我们将扩展我们现有的外部 暴露数据库，包括可能通过系统性的影响新冠肺炎结果的其他因素 文献和资源分析。我们的目标是(1)开发识别新冠肺炎的表型算法 队列及其严重性，并从One佛罗里达现实世界中提取相关的个人水平的风险因素 数据，和(2)确定与严重新冠肺炎相关的外部暴露因素，检查外部因素如何 暴露有助于严重新冠肺炎的种族和民族差异，并构建预测模型 重症新冠肺炎伴外感因素。这项研究将通过以下方式填补重要的知识空白 及时提供信息，了解环境暴露可能如何影响新冠肺炎的严重性 改进对高危新冠肺炎患者的识别，并为未来精准干预的设计提供信息。 我们的方法和佛罗里达州的初步结果可以(1)通过PCORnet很容易地扩展到多州研究 以及(2)回答其他新颖的问题，如外部曝光组对地理差异的贡献 新冠肺炎成果丰硕。

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.