Effectiveness of a Fourth Dose of COVID-19 Vaccine among Long-Term Care Residents in Ontario, Canada: Test-Negative Design Study
Bibliographic record
Abstract
Abstract Background As of December 30, 2021, Ontario long-term care (LTC) residents who received a third dose of COVID-19 vaccine ≥84 days previously were offered a fourth dose to prevent a surge in COVID-19-related morbidity and mortality due to the Omicron variant. Methods We used a test-negative design and linked databases to estimate the marginal effectiveness (4 versus 3 doses) and vaccine effectiveness (VE; 2, 3, or 4 doses versus no doses) of mRNA vaccines among Ontario LTC residents aged ≥60 years who were tested for SARS-CoV-2 between December 30, 2021 and April 27, 2022. Outcome measures included any Omicron infection, symptomatic infection, and severe outcomes (hospitalization or death). Results We included 13,654 Omicron cases and 205,862 test-negative controls. The marginal effectiveness of a fourth dose (with 95% of fourth dose vaccine recipients receiving mRNA-1273) ≥7 days after vaccination versus a third dose received ≥84 days prior was 19% (95% Confidence Interval [CI], 12-26%) against infection, 31% (95%CI, 20-41%) against symptomatic infection, and 40% (95%CI, 24-52%) against severe outcomes. VE (compared to an unvaccinated group) increased with each additional dose, and for a fourth dose was 49% (95%CI, 43-54%), 69% (95%CI, 61-76%), and 86% (95%CI, 81-90%), against infection, symptomatic infection, and severe outcomes, respectively. Conclusions Our findings suggest that compared to a third dose received ≥84 days ago, a fourth dose improved protection against infection, symptomatic infection, and severe outcomes caused by Omicron among long-term care residents. Compared to unvaccinated individuals, fourth doses provide strong protection against severe outcomes, but the duration of protection remains unknown.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".