COVID-19 vaccine uptake in a retrospective population-based cohort of people living with and without HIV in Ontario, Canada
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".