Effectiveness and Duration of Protection of a Fourth Dose of COVID-19 mRNA Vaccine among Long-Term Care Residents in Ontario, Canada
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. Seven months have passed since fourth doses were implemented, allowing for the examination of fourth dose protection over time. 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 August 3, 2022. Outcome measures included any Omicron infection, symptomatic infection, and severe outcomes (hospitalization or death). Results We included 21,275 Omicron cases and 273,466 test-negative controls. The marginal effectiveness of a fourth dose <84 days ago compared to a third dose received ≥84 days ago was 23% (95% Confidence Interval [CI] 17-29%), 36% (95%CI 26-44%), and 37% (95%CI 24-48%) against SARS-CoV-2 infection, symptomatic infection, and severe outcomes, respectively. Additional protection provided by a fourth dose compared to a third dose was negligible against all outcomes ≥168 days after vaccination. Compared to unvaccinated individuals, vaccine effectiveness (VE) of a fourth dose decreased from 49% (95%CI 44%-54%) to 18% (95%CI 5-28%) against infection, 69% (95%CI 62-75%) to 44% (95%CI 24-59%) against symptomatic infection, and 82% (95%CI 77-86%) to 74% (95%CI 62-82%) against severe outcomes <84 days versus ≥168 days after vaccination. Conclusions Our findings suggest that fourth doses of mRNA COVID-19 vaccines provide additional protection against Omicron-related outcomes in LTC residents, but the protection wanes over time, with more waning seen against infection than severe outcomes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".