The tale of two pandemics: Understanding racial and ethnic disparities from the collision of HIV and COVID-19 in the U.S.

两种流行病的故事：从美国 HIV 和 COVID-19 的碰撞中了解种族和民族差异

• 批准号: 10662546
• 负责人: Rena Chiman Patel
• 金额: $ 1.13万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-08 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10662546
• 关键词: Address; Adult; Affect; American; Area; Biological Factors; Black Populations; Black race; COVID-19; COVID-19 impact; COVID-19 pandemic; COVID-19 pandemic effects; Caring; Cessation of life; Characteristics; Clinical; Color; Communities; Data; Data Science; Data Set; Disparity; Economics; Electronic Health Record; Epidemic; Equity; Ethnic Origin; Frequencies; Goals; HIV; HIV Infections; HIV diagnosis; Health system; Hospitalization; Housing; Immune System Diseases; Incidence; Individual; Interruption; Intervention; Laboratory Markers; Latinx; Latinx population; Machine Learning; Measures; Mediating; Mediation; Methods; Modeling; Outcome; Pathway interactions; Patients; Persons; Politics; Population; Population Attributable Risks; Positioning Attribute; Prevention; Prevention approach; Public Health; Race; Racial Equity; Recording of previous events; Research; Risk; SARS-CoV-2 infection; Sexually Transmitted Diseases; Structural Racism; Testing; United States National Institutes of Health; Vaccination; Viral; breakthrough infection; cohort; comorbidity; coronavirus disease; current pandemic; ethnic disparity; ethnic minority; future pandemic; health disparity; health equity; high risk; implementation strategy; improved; insight; learning strategy; minority communities; multilevel analysis; next generation; pandemic disease; post-COVID-19; pre-exposure prophylaxis; predictive modeling; public health intervention; racial disparity; racial minority; repository; response; severe COVID-19; social; social determinants; social factors; social health determinants; social interventions; social vulnerability; statistical learning; transmission process; vaccine effectiveness

PROJECT SUMMARY The collision of the COVID-19 pandemic with the existing HIV epidemic in the U.S. has exacerbated the decades old racial/ethnic disparities in HIV. For example, Blacks account for 42-44% of HIV diagnoses and deaths among people living with HIV (PLWH) while accounting for only 12% of the population. These racialized disparities in the U.S. HIV epidemic are further compounded by the same disparities emerging in COVID-19. We have shown that PLWH appear to be at higher risk of poorer COVID-19 outcomes than persons not living with HIV (PNLWH), and that the odds of incident COVID-19 infection among PLWH are 60% and 118% higher among Black and Latinx persons, respectively, than whites. These racialized disparities are likely largely driven by social determinants of health (SDoH) underlying our health systems—an understanding of the SDoH pathways that elucidate these disparities is urgently needed to develop the next generation of HIV interventions operating at the structural and social levels, and ever more now in the context of COVID-19. The National COVID Cohort Collaborative (N3C) leverages real-world, national data and presents an unprecedented opportunity to inform the NIH priority aims to understand the social and biologic factors that may affect both HIV and COVID-19 outcomes. N3C is the largest electronic health record (EHR) repository in U.S. history (>10M patients), contains both unparalleled individual-level granular clinical and historical data, and represents the largest U.S. cohort of PLWH with their HIV and COVID-19 outcomes data (>77K), allowing us to evaluate the bi-directional impact of existing HIV infection and COVID-19 outcomes. Furthermore, individual-level data in the N3C are uniquely positioned to merge publicly available datasets that measure area- level SDoH. Our central hypothesis is that the observed racial/ethnic disparities in HIV and COVID-19 occur in a larger context of individuals embedded in social, political, and economic contexts, i.e., SDoH. Understanding these forces, centered on SDoH, allows us to determine the next generation of HIV interventions. Our three aims respond to the NIH call using data science, rigorous machine and statistical learning, and multi-level mediation and epidemic modeling. The goal of Aim 1 (HIV outcomes) is to identify multilevel, social determinants of racial/ethnic disparities in HIV outcomes (e.g., viral suppression [VS] and hospitalization) during the COVID-19 pandemic. The goal of Aim 2 (COVID outcomes) is to understand the independent and aggregated impact of SDoH and clinical characteristics on HIV immune dysfunction for COVID-19 outcomes and vaccine effectiveness (2a) and quantify the differential impact of HIV on COVID-19 outcomes at the U.S. population level by race/ethnicity (2b). The goal of Aim 3 (HIV epidemic modeling) is to quantify the impact of the COVID-19 pandemic on HIV treatment (VS and hospitalization) and prevention (pre-exposure prophylaxis [PrEP] use and HIV/sexually transmitted infections [STI] testing frequency) outcomes by race/ethnicity at the population-level for the national Ending the HIV Epidemic (EHE) initiative’s priority jurisdictions.

项目摘要 联盟19日大流行与美国现有的艾滋病毒流行的碰撞加剧了数十年 艾滋病毒中的古老种族/种族差异。例如，黑人占HIV诊断和死亡的42-44％ 患有艾滋病毒（PLWH）的人只占人口的12％。这些种族化的分布 美国艾滋病毒的流行进一步加剧了19日在Covid-19中出现的相同差异。我们已经显示了 与不忍受艾滋病毒（PNLWH）的人相比 在PLWH中发生Covid-19的事件的几率在黑色和 拉丁人分别比白人。这些种族化的分布可能在很大程度上是由社会驱动的 确定我们的卫生系统基础的健康（SDOH） - 对SDOH途径的理解 迫切需要阐明这些分布来发展下一代的HIV干预措施 结构和社会层面，现在在Covid-19的背景下更多。 国家共同队列合作（N3C）利用现实世界，国家数据并呈现 前所未有的机会通知NIH优先事项的旨在了解可能的社会和生物学因素 影响HIV和COVID-19的结果。 N3C是美国最大的电子健康记录（EHR）存储库 病史（> 10m患者）包含无与伦比的个体颗粒临床和历史数据，以及 代表最大的美国PLWH队列，其HIV和COVID-19结果数据（> 77K），允许 美国评估现有的HIV感染和COVID-19结果的双向影响。此外， N3C中的个人级别数据是唯一的定位，可以合并公开可用的数据集，以衡量区域 - 级别SDOH。我们的中心假设是，艾滋病毒和互联19中观察到的种族/种族差异发生在 嵌入社会，政治和经济环境中的个人的更大背景，即SDOH。理解 这些以SDOH为中心的力使我们能够确定下一代HIV干预措施。 我们的三个目标使用数据科学，严格的机器和统计学习以及 多级调解和流行建模。目标1（艾滋病毒结果）的目标是确定多级，社会 艾滋病毒结局中种族/种族差异的决定因素（例如，病毒抑制[VS和住院） 19009年大流行。 AIM 2的目标（共同结果）是了解独立和 SDOH和临床特征对COVID-19结果的HIV免疫功能障碍的综合影响和 疫苗有效性（2A），并量化HIV对美国共同结果的差异影响 种族/种族的人口水平（2b）。目标3（HIV流行建模）的目的是量化 艾滋病毒治疗（VS和住院）和预防（暴露前预防 [PREP]使用和HIV/性传播感染[STI测试频率） 国家终止艾滋病毒流行（EHE）倡议的优先管辖权的人口级别。