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.

项目总结 新冠肺炎大流行与美国现有的艾滋病毒疫情的碰撞加剧了几十年的 艾滋病毒方面的旧的种族/民族差异。例如，黑人占艾滋病毒诊断和死亡的42%-44% 艾滋病毒携带者(PLWH)仅占总人口的12%。这些种族主义的差异在 新冠肺炎上出现的同样的差异进一步加剧了美国的艾滋病毒流行。我们已经展示了 PLWH似乎比非艾滋病毒携带者(PNLWH)患上更差的新冠肺炎结局的风险更高， 而在女性中感染新冠肺炎的几率在黑人和女性中分别高出60%和118% 拉丁裔分别比白人多。这些种族主义的差距很可能主要是由社会 健康决定因素(SDoH)是我们健康系统的基础--了解SDoH途径 阐明这些差异是开发下一代艾滋病毒干预措施的迫切需要 结构和社会层面，现在更是在新冠肺炎的背景下。 国家COVID队列协作(N3C)利用真实世界的国家数据并提供 前所未有的机会告知NIH的优先事项旨在了解可能的社会和生物因素 对艾滋病毒和新冠肺炎的结果都有影响。N3C是美国最大的电子健康记录(EHR)存储库。 病史(&gt；1000万患者)，包含无与伦比的个人级别的细粒度临床和历史数据，以及 代表了美国最大的PLWH队列和他们的艾滋病毒和新冠肺炎结果数据(&gt；77K)，允许 美国将评估现有艾滋病毒感染和新冠肺炎结果的双向影响。此外， N3C中的个人级别数据具有独特的定位，可以合并测量面积的公开可用的数据集- SDoH级别。我们的中心假设是，在艾滋病毒和新冠肺炎中观察到的种族/民族差异发生在 个人嵌入社会、政治和经济背景的更大背景，即SDoH。理解 这些力量以SDoH为中心，使我们能够确定下一代艾滋病毒干预措施。 我们的三个目标是响应NIH的号召，使用数据科学、严格的机器和统计学习，以及 多层次调解和疫情建模。目标1(艾滋病毒成果)的目标是确定多层次的、社会的 年期间艾滋病毒结果的种族/民族差异的决定因素(例如，病毒抑制和住院) 新冠肺炎大流行。目标2(COVID结果)的目标是理解独立和 SDoH和临床特征对新冠肺炎结果和艾滋病毒免疫功能障碍的综合影响 疫苗有效性(2a)和量化艾滋病毒对美国新冠肺炎结果的不同影响。 按种族/族裔分列的人口水平(2b)。目标3(艾滋病毒流行模型)的目标是量化 新冠肺炎关于艾滋病毒治疗(VS和住院)和预防(暴露前预防)的大流行 [准备]使用和艾滋病毒/性传播感染[STI]检测频率)按种族/民族分列的结果 在人口层面，为国家结束艾滋病毒流行倡议的优先管辖区。