Monovalent mRNA XBB.1.5 vaccine effectiveness against COVID-19 hospitalization in Quebec, Canada: impact of variant replacement and waning protection during 10-month follow-up
Bibliographic record
Abstract
Background. We evaluated mRNA COVID–19 vaccine effectiveness (VE) for the XBB.1.5 formulation against COVID–19 hospitalizations among adults aged ≥60 years during a ten–month follow–up period. Methods. We conducted a test–negative case–control study using Quebec population–based administrative data. Specimens collected from individuals aged ≥60 years tested at an acute–care hospital from October 2023 to August 2024 were considered test–positive cases if hospitalized for COVID–19, or controls if test–negative for SARS–CoV–2. Vaccination was defined by receipt of at least one mRNA XBB–vaccine (autumn or spring) dose. Multivariate logistic regression analyses estimated VE relative to several comparator groups, primarily those last–vaccinated in 2022, by subvariant predominant period (XBB, JN and KP), by time since XBB–vaccination and by number of XBB–vaccine doses (KP period). Results. Participants overall and by XBB, JN and KP periods included: 5532 (4.9%) test–positive cases (1321, 1838 and 1372, respectively) and 108473 (95.1%) test–negative controls (12881, 53414 and 28595, respectively); 14584 specimens were collected during periods of subvariant cocirculation. By subvariant period, 3322 (25.8%), 27041 (50.6%) and 15401 (53.9%) controls, respectively, were considered XBB–vaccinated. Overall VE was 30% (95%CI:24–35) and by XBB, JN or KP period: 54% (95%CI:46–62), 23% (95%CI:13–32) and 0% (95%CI:-18–15), respectively. During each subvariant period, the hospitalization risk was reduced only during the first four months post–vaccination. Conclusions. Among individuals aged ≥60 years, mRNA XBB–vaccination provided meaningful, albeit limited and short–term, protection against COVID–19 hospitalization due to XBB, JN and KP subvariants in. Better vaccines are needed to effectively reduce COVID–19 disease burden.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".