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Record W4411870078 · doi:10.1016/j.vaccine.2025.127422

COVID-19 vaccine uptake in a retrospective population-based cohort of people living with and without HIV in Ontario, Canada

2025· article· en· W4411870078 on OpenAlexafffundabout
Cassandra Freitas, Curtis Cooper, Abigail Kroch, Rahim Moineddin, Gordon Arbess, Anita C. Benoit, Sarah A. Buchan, Catharine Chambers, Muluba Habanyama, Claire Kendall, Jeffrey C. Kwong, Lawrence Mbuagbaw, John McCullagh, Nasheed Moqueet, Devan Nambiar, Sergio Rueda, Vanessa Tran, Sharon Walmsley, Ann N. Burchell

Bibliographic record

VenueVaccine · 2025
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsPublic Health Agency of CanadaCanada Research ChairsOttawa HospitalUniversity of OttawaMcMaster UniversityHIV Legal NetworkWomen's College HospitalUniversity Health NetworkUniversity of TorontoMcMaster University Medical CentrePublic Health Ontario
FundersDepartment of Family and Community Medicine, University of TorontoOntario Ministry of Health and Long-Term CareBruyère Research InstituteInstitute for Clinical Evaluative SciencesCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsMedicinePoisson regressionDemographyPopulationCohortCohort studyVaccinationRetrospective cohort studyConfidence intervalHIV vaccineGerontologyEnvironmental healthImmunologyVaccine trialInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: COVID-19 vaccination significantly reduces COVID-19-related hospitalization and mortality and is important for those who may be at increased risk of SARS-CoV-2 infection, including people living with HIV. Using a population-based approach, we examined COVID-19 vaccine uptake among people living with and without HIV in Ontario, Canada. METHODS: A retrospective population-based matched cohort study was conducted using provincial clinical and health administrative data from December 14, 2020 to August 31, 2022. Community-dwelling adults living with HIV aged ≥19 years were matched one-to-one with a person without a diagnosis of HIV based on age, sex, geography, and immigration status. To identify predictors of vaccine uptake, modified Poisson regression with robust standard errors accounting for geographical clustering was used. To estimate vaccine uptake comparing the HIV and non-HIV cohorts, conditional Poisson regression with robust error variance was used to estimate crude and adjusted risk ratios with 95 % confidence intervals (CI). RESULTS: Among 20,903 people living with HIV, most (85.4%) had received ≥2 COVID-19 vaccine doses, with 64.7% receiving a third dose and 24.3% receiving a fourth dose. Disparities in uptake of ≥3 doses by sex were observed (males vs females: 68.5% vs 50.9%). Predictors of receiving ≥3 doses among people living with HIV included older age, male sex, and receipt of a recent influenza vaccine. Men living with HIV were more likely to receive ≥3 doses compared with men living without HIV, whereas women living with HIV were less likely than women living without HIV to receive ≥3 doses. CONCLUSIONS: Uptake of the first two doses of COVID-19 vaccine was high among people living with HIV in Ontario, Canada, however, disparities in uptake of ≥3 doses remain, especially by sex. Continued monitoring of COVID-19 vaccine uptake is crucial to informing immunization programs, policies and guidelines for people living with HIV in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.290
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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