Dataset Information

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study.

ABSTRACT:

Introduction

Despite the effectiveness of COVID-19 vaccines in preventing severe COVID-19 outcomes, a small percentage of fully vaccinated persons will develop symptomatic or asymptomatic infections with SARS-CoV-2, which is referred to as 'breakthrough COVID-19'. People living with HIV (PLWH) appear to have an elevated risk of severe COVID-19 outcomes, yet the effectiveness of the COVID-19 vaccine in this population remains unclear due to the limited research efforts in this population in the real world. This study aims to characterise and compare the breakthrough COVID-19 (eg, prevalence and disease severity) between PLWH and non-PLWH and then examine whether HIV markers play a role in COVID-19 vaccine effectiveness within the PLWH population.

Methods and analysis

This cohort study will merge electronic health records data from multiple data sources in South Carolina (SC), including the 'HIV Cohort' (n=12 203) identified from the statewide Enhanced HIV/AIDS Reporting System, 'Vaccine Cohort' from the Statewide Immunisation Online Network which provides patient-level immunisation records (n=~1.71 million), and 'COVID-19 Cohort' which includes healthcare encounters and COVID-19 diagnosis information for all individuals who were tested for COVID-19 (n=~3.41 million). The PLWH will be matched with a comparison group of non-PLWH by the propensity score matching method. To distinguish the role of immunity level in affecting the vaccine effectiveness, we will conduct subgroup analyses to compare the outcome of virally controlled and immunosuppressed PLWH with non-PLWH. Conditional logistic regression and generalised linear models will be employed to analyse the relationship between HIV status and protection durability by adjusting for potential confounders.

Ethics and dissemination

The study was approved by the Institutional Review Board at the University of South Carolina (Pro00117583) as a Non-Human Subject study. The study's findings will be published in peer-reviewed journals and disseminated at national and international conferences and through social media.

SUBMITTER: Xueying Y

PROVIDER: S-EPMC9402448 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study.

Xueying Yang Y Zhang Jiajia J Olatosi Bankole B Weissman Sharon S Li Xiaoming X

BMJ open 20220823 8

<h4>Introduction</h4>Despite the effectiveness of COVID-19 vaccines in preventing severe COVID-19 outcomes, a small percentage of fully vaccinated persons will develop symptomatic or asymptomatic infections with SARS-CoV-2, which is referred to as 'breakthrough COVID-19'. People living with HIV (PLWH) appear to have an elevated risk of severe COVID-19 outcomes, yet the effectiveness of the COVID-19 vaccine in this population remains unclear due to the limited research efforts in this population ...[more]

PMID: 35998950

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Project description:BackgroundPeople living with HIV (PLHIV) in the southern United States (US) are at high risk for poor outcomes across the HIV care continuum leading to low rates of viral suppression. Understanding structural barriers to care-including transportation vulnerability-is critical to improve HIV outcomes. This study investigated relationships between travel time to HIV care, transportation vulnerability, and HIV care disruptions to inform future transportation interventions for PLHIV residing in South Carolina and other southern US states.MethodsA total of 160 PLHIV (N = 160) were recruited from a large immunology center in South Carolina. Participants reported on transportation experiences, transportation vulnerabilities, and residence. Differences in sociodemographic characteristics, transportation vulnerabilities, and HIV care disruptions were compared across travel time groups (< 15, 15-30, and > 30 min from residential location to the HIV clinic) using Mantel-Haenszel Chi-Square tests. Multivariable logistic regression tested our a priori hypothesis that travel time would predict HIV care disruptions.ResultsA majority of participants were aged 45-64 years old (54.4%), single (77.0%), male (63.8%), and Black (77.5%). Nearly 20% of participants lived < 15 min from their HIV clinic, 59.1% lived 15-30 min, and 21.4% lived > 30 min away. PLHIV who had to travel > 30 min to HIV care were more likely than those living < 15 min away to report transportation vulnerability (73.5% vs. 51.6%, p = 0.048), missed HIV care appointments (64.7% vs. 41.9%, p = 0.049), and transportation challenges that prevented them from seeing HIV care providers (67.7% vs. 39.4%; p = 0.014). Adjusted odds ratios (AOR) show that PLHIV who had to travel > 30 min were more likely to experience transportation-related disruptions to HIV care, including being late to appointments (AOR 5.25, 95% CI:1.06-25.92), missing appointments (AOR 3.85, 95% CI:1.04-15.89), and being unable to see HIV providers (AOR 7.06, 95% CI:0.59-14.89).ConclusionsIn South Carolina-a rural southern state with a disproportionate burden of HIV-long travel time (> 30 min) to HIV care is associated with care disruptions, including more missed visits. Transportation interventions, as well as other efforts to expand rural access to HIV care, are urgently needed to ensure that all PLHIV are able to engage in consistent HIV care in order to reach and maintain viral suppression.

Project description:ObjectivePeople with HIV were underrepresented in coronavirus disease 2019 (COVID-19) vaccine clinical trials. We estimated vaccine effectiveness (VE) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection for the BNT162b2, mRNA-1273, and ChAdOx1 vaccines among a population-based cohort of people with HIV in Ontario, Canada.DesignTest-negative design.MethodsWe identified people with HIV aged ≥19 years who were tested for SARS-CoV-2 by RT-PCR between December 14, 2020 (first availability of COVID-19 vaccines) and November 21, 2021 (pre-Omicron circulation). Outcomes included any infection, symptomatic infection, and COVID-19-related hospitalization/death. We compared the odds of vaccination between test-positive cases and test-negative controls using multivariable logistic regression with adjustment for age, sex, region, calendar time, SARS-CoV-2 test histories, influenza vaccination, comorbidities, and neighborhood-level socio-economic status. VE was derived as (1 - adjusted odds ratio) × 100%.ResultsAmong 21 023 adults living with HIV, there were 801 (8.3%) test-positive cases and 8,879 (91.7%) test-negative controls. 20.1% cases and 47.8% of controls received ≥1 COVID-19 vaccine dose; among two-dose recipients, 93.4% received ≥1 mRNA dose. Two-dose VE ≥7 days before specimen collection was 82% (95% confidence interval [CI] = 74-87%) against any infection, 94% (95% CI = 82-98%) against symptomatic infection, and 97% (95% CI = 85-100%) against hospitalization/death. Against any infection, VE declined from 86% (95% CI = 77-92%) within 7-59 days after the second dose to 66% (95% CI = -15-90%) after ≥180 days; we did not observe evidence of waning protection for other outcomes.ConclusionTwo doses of COVID-19 vaccine offered substantial protection against symptomatic illness and hospitalization/death in people with HIV prior to the emergence of the Omicron variant. Our findings do not support a broad conclusion that COVID-19 VE is lower among people with HIV in populations that, for the most part, are attending HIV care, taking antiretroviral medication, and are virally suppressed.

Dataset Information

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study.

Introduction

Methods and analysis

Ethics and dissemination

Publications

COVID-19 vaccine effectiveness among people living with and without HIV in South Carolina, USA: protocol of a population-based cohort study.

Similar Datasets

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