Associations between HIV infection and clinical spectrum of COVID-19: a population level analysis based on US National COVID Cohort Collaborative (N3C) data,The Lancet HIV

当前位置： X-MOL 学术 › Lancet HIV › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Associations between HIV infection and clinical spectrum of COVID-19: a population level analysis based on US National COVID Cohort Collaborative (N3C) data
The Lancet HIV ( IF 12.8 ) Pub Date : 2021-10-13 , DOI: 10.1016/s2352-3018(21)00239-3
Xueying Yang ₁ , Jing Sun ₂ , Rena C Patel ₃ , Jiajia Zhang ₄ , Siyuan Guo ₄ , Qulu Zheng ₂ , Amy L Olex ₅ , Bankole Olatosi ₆ , Sharon B Weissman ₇ , Jessica Y Islam ₈ , Christopher G Chute ₉ , Melissa Haendel ₁₀ , Gregory D Kirk ₂ , Xiaoming Li ₁ ,

Affiliation

South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA.
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Medicine, University of Washington, Seattle, WA, USA; Department of Global Health, University of Washington, Seattle, WA, USA.
South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA.
Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA.
South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA.
South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Internal Medicine, School of Medicine, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA.
Center for Immunization and Infection in Cancer, Cancer Epidemiology Program, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Schools of Medicine, Public Health, and Nursing, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

Background

Evidence of whether people living with HIV are at elevated risk of adverse COVID-19 outcomes is inconclusive. We aimed to investigate this association using the population-based National COVID Cohort Collaborative (N3C) data in the USA.

Methods

We included all adult (aged ≥18 years) COVID-19 cases with any health-care encounter from 54 clinical sites in the USA, with data being deposited into the N3C. The outcomes were COVID-19 disease severity, hospitalisation, and mortality. Encounters in the same health-care system beginning on or after January 1, 2018, were also included to provide information about pre-existing health conditions (eg, comorbidities). Logistic regression models were employed to estimate the association of HIV infection and HIV markers (CD4 cell count, viral load) with hospitalisation, mortality, and clinical severity of COVID-19 (multinomial). The models were initially adjusted for demographic characteristics, then subsequently adjusted for smoking, obesity, and a broad range of comorbidities. Interaction terms were added to assess moderation effects by demographic characteristics.

Findings

In the harmonised N3C data release set from Jan 1, 2020, to May 8, 2021, there were 1 436 622 adult COVID-19 cases, of these, 13 170 individuals had HIV infection. A total of 26 130 COVID-19 related deaths occurred, with 445 among people with HIV. After adjusting for all the covariates, people with HIV had higher odds of COVID-19 death (adjusted odds ratio 1·29, 95% CI 1·16–1·44) and hospitalisation (1·20, 1·15–1·26), but lower odds of mild or moderate COVID-19 (0·61, 0·59–0·64) than people without HIV. Interaction terms revealed that the elevated odds were higher among older age groups, male, Black, African American, Hispanic, or Latinx adults. A lower CD4 cell count (<200 cells per μL) was associated with all the adverse COVID-19 outcomes, while viral suppression was only associated with reduced hospitalisation.

Interpretation

Given the COVID-19 pandemic's exacerbating effects on health inequities, public health and clinical communities must strengthen services and support to prevent aggravated COVID-19 outcomes among people with HIV, particularly for those with pronounced immunodeficiency.

Funding

National Center for Advancing Translational Sciences, National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.

中文翻译：

HIV 感染与 COVID-19 临床谱之间的关联：基于美国国家 COVID 队列协作 (N3C) 数据的人群水平分析

背景

HIV 感染者出现 COVID-19 不良后果的风险是否较高的证据尚无定论。我们旨在利用美国基于人口的国家新冠肺炎队列协作 (N3C) 数据来调查这种关联。

方法

我们纳入了来自美国 54 个临床中心的所有有过医疗保健经历的成人（年龄≥18 岁）COVID-19 病例，并将数据存入 N3C。结果是 COVID-19 疾病严重程度、住院情况和死亡率。自 2018 年 1 月 1 日或之后开始在同一医疗保健系统中的遭遇也被包括在内，以提供有关先前存在的健康状况（例如合并症）的信息。采用逻辑回归模型来估计 HIV 感染和 HIV 标记物（CD4 细胞计数、病毒载量）与 COVID-19 住院、死亡率和临床严重程度（多项）的关联。这些模型最初根据人口特征进行调整，然后针对吸烟、肥胖和广泛的合并症进行调整。添加交互项以评估人口特征的调节效果。

发现

在 2020 年 1 月 1 日至 2021 年 5 月 8 日期间发布的统一 N3C 数据集中，共有 1 436 622 例成人 COVID-19 病例，其中 13 170 人感染了 HIV。共有 26 130 人死亡与 COVID-19 相关，其中 445 人为艾滋病毒感染者。调整所有协变量后，HIV 感染者发生 COVID-19 死亡的几率较高（调整后比值比 1·29，95% CI 1·16–1·44）和住院治疗（1·20、1·15–1· 26），但感染轻度或中度 COVID-19 的几率（0·61、0·59–0·64）低于未感染 HIV 的人。交互项显示，老年群体、男性、黑人、非裔美国人、西班牙裔或拉丁裔成年人的患病几率更高。较低的 CD4 细胞计数（<200 个细胞/μL）与所有不良的 COVID-19 结局相关，而病毒抑制仅与住院率减少相关。