COVID-19 Vaccination among People Living with HIV: Immunogenicity, Effectiveness, and Safety [COVAXHIV, study data contributed to the CITF Databank]
Bibliographic record
Abstract
Background: COVID-19 may pose a greater risk for people living with HIV (PLWH) who already face multiple vulnerabilities and more often belong to groups disproportionately affected by the pandemic. Vaccines that prevent COVID-19 can provide critical benefits for this priority vaccination group. However, people living with HIV have been understudied in COVID-19 vaccine clinical trials. Objectives of the CITF-funded study: The first aim of the funded study, from which data for the PLWH cohort was shared, was to compare the immunogenicity of COVID-19 vaccines among PLWH compared to people who are HIV-negative, particularly regarding the necessity for additional doses. Methods: COVAXHIV used a cohort design with the treated group followed from second dose of SARS-CoV-2 vaccination. Outcome measures included SARS-CoV-2 serology and neutralization capacity[, and hospitalization due to COVID-19] up to 12 months post third dose. PLWH 16 years and older were recruited from clinics in Montreal, Ottawa, Toronto, and Vancouver, Canada, and through social media. Enrollment was between April 2021 and January 2022. Participants were excluded if they had an active COVID infection or three doses prior to enrollment. (HIV negative participants 18 years and older were recruited through an existing research project, Stop the Spread Ottawa, and are not in the contributed data.) Data were collected through a standardized questionnaire capturing health behaviors, medical history, and serological samples to evaluate COVID-19-specific antibodies, with a focus on post-third and fourth vaccine doses. Lab collaboration with the Integrating Longitudinal Epidemiologic, Virologic and Immunologic Analyses to Understand COVID-19 Immunity and Infection Outcomes in Long Term Care [ILEVIA, study data contributed to the CITF Databank] Contributed dataset contents: The contributed data include baseline and follow-up visit questionnaire data for 375 participants living with HIV (346 with follow-up) variables. Serology and neutralisation results for 50 participants among 99 participants with blood samples of the participants are stored with the Romney dataset, linked by participant ID. Study protocol states that clinical chart review was used to supplement participant-reported information when needed. The contributed data included 375 participants living with HIV who completed baseline visits between January 2021 and May 2022 (344 participants with follow-up). All participants gave one or more blood samples for SARS-CoV-2 serology between April 2021 and August 2023. Two additional participants provided a blood sample without completing a questionnaire. A total of 1,534 blood samples were collected. ACE2 displacement and live virus neutralization results for 699 blood samples from 99 participants were collected. Cellular immunity test results for 169 blood samples from 50 participants are stored with the Romney T-cell mediated immunity dataset, linked by participant ID. Variables include data in the following areas of information: demographics (age, sex and gender, race and indigeneity), general health (tobacco use, chronic conditions, height and weight, flu vaccine), exposure risk factors (travel, occupation, gathering, compliance to COVID-19 preventive behaviors), longitudinal follow-up for COVID infections (COVID tests, symptoms, hospitalization), COVID vaccination history (number of vaccine doses, vaccination during pregnancy), SARS-CoV-2 serology (IgG against spike, nucleocapsid and RBD, ACE2 displacement, live virus neutralization) and cellular immunity (CD4+ and CD8+ T-cell responses against spike). This study was in collaboration with Romney et al [https://doi.org/10.5683/SP3/V68T4B]]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".