Effectiveness of COVID-19 vaccines in people living with HIV in British Columbia and comparisons with a matched HIV-negative cohort: a test-negative design
Bibliographic record
Abstract
OBJECTIVES: We estimated the effectiveness of COVID-19 vaccines against laboratory-confirmed SARS-CoV-2 infection among people living with HIV (PLWH) and compared the estimates with a matched HIV-negative cohort. METHODS: We used the British Columbia COVID-19 Cohort, a population-based data platform, which integrates COVID-19 data on SARS-CoV-2 tests, laboratory-confirmed cases, and immunizations with provincial health services data. The vaccine effectiveness (VE) was estimated with a test-negative design using the multivariable logistic regression. RESULTS: The adjusted VE against SARS-CoV-2 infection was 71.1% (39.7, 86.1%) 7-59 days after two doses, rising to 89.3% (72.2, 95.9%) between 60 and 89 days. VE was preserved 4-6 months after the receipt of two doses, after which noticeable waning was observed (51.3% [4.8, 75.0%]). In the matched HIV-negative cohort (n = 375,043), VE peaked at 91.4% (90.9, 91.8%) 7-59 days after two doses and was sustained for up to 4 months, after which evidence of waning was observed, dropping to 84.2% (83.4, 85.0%) between 4 and 6 months. CONCLUSION: The receipt of two COVID-19 vaccine doses was effective against SARS-CoV-2 infection among PLWH pre-Omicron. VE estimates appeared to peak later in PLWH than in the matched HIV-negative cohort and the degree of waning was relatively quicker in PLWH; however, peak estimates were comparable in both populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".