National Near Real-Time Vaccine Effectiveness Against COVID-19 Severe Outcomes Using the Screening Method Among Older Adults Aged ≥50 Years in Canada
Bibliographic record
Abstract
Background/Objectives: It is critical to monitor real-world COVID-19 vaccine effectiveness (VE) in older adults, as they have been identified as a priority group for vaccination. This is the first study that aims to estimate national absolute vaccine effectiveness (aVE) against severe COVID-19 outcomes among Canadian older adults aged ≥50 years. Methods: The screening method (SM) was implemented using standard and spline-based logistic regression models to estimate aVE and 95% confidence intervals (CIs) by outcome, age group, vaccination status, time since last dose, vaccine schedules, and variant of concern (VOC) period. Results: From 1 August 2021 to 30 November 2023, there were 103,822 severe COVID-19 cases, of which 72.9% were hospitalized, 8.2% were admitted to ICU, and 18.9% had died. A total of 23.1% of these cases were unvaccinated against COVID-19, 21.9% completed a primary series only, and 55.0% received at least one additional/booster dose. National aVE against severe COVID-19 outcomes remained moderate to high during Delta and original Omicron VOC predominance periods. Monthly age-specific aVE of at least two additional/booster doses remained stable during recombinant XBB.1.5/EG.5 VOC predominance, ranging from 61.0% (95% CI: 51.9–68.4%) to 69.8% (95% CI: 67.5–72.0%) against hospitalization, and 71.0% (95% CI: 62.8–77.4%) to 77.2% (95% CI: 74.2–79.9%) against ICU admission/death. Adjusted aVE was higher for last booster doses received within the past six months and with heterologous mRNA vaccine schedules. Conclusions: The SM is a useful method to estimate aVE in near real-time, enabling the assessment of temporal changes in aVE, guiding vaccine policy, and building vaccine confidence among populations at higher risk of severe outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".