COVID-19 vaccine uptake among people with HIV: identifying characteristics associated with vaccine hesitancy
Bibliographic record
Abstract
People with HIV (PWH) are at increased risk of COVID-19 infection. Both Canadian (NACI) and US (CDC) guidelines recommend that all PWH receive at least 2 doses of COVID-19 vaccine, and a booster. We examined vaccination uptake among PWH in Southern Alberta, Canada. Among adult PWH, we evaluated COVID-19 vaccination uptake between December 2020 and August 2022. Poisson regression models with robust variance (approximating log binomial models) estimated crude and adjusted prevalence ratios (aPR) and 95% confidence intervals (CI) for receiving (1) any vs. no vaccine, and (2) primary series with booster (≥ 3 vaccines) versus primary series without booster. Among 1885 PWH, 10% received no COVID-19 vaccinations, 37% < 3 vaccines and 54% received ≥ 3 vaccines. Females (vs. males) were less likely to receive a vaccine booster. Receiving no COVID-19 vaccines was associated with White ethnicity, unsuppressed HIV viral load (> 200 copies/mL), and using illegal substances. Factors associated with decreased booster uptake included being younger, Black (vs. White) ethnicity, substance use, lower educational attainment, and having an unsuppressed HIV viral load. COVID-19 booster uptake among PWH does not meet vaccine guidelines, and receipt of vaccines is unevenly distributed. Booster uptake is lowest among young females and marginalized individuals. Focused outreach is necessary to close this gap.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".