Effectiveness of COVID-19 Vaccination and Prior Infections to Reduce Long COVID Risk During the Pre-Omicron and Omicron Periods
Bibliographic record
Abstract
BACKGROUND: We estimated vaccine effectiveness (VE) against COVID-19 and long COVID during pre-Omicron and Omicron periods, by number of doses and prior infection history. METHODS: We combined survey information from a cohort of healthcare workers in Quebec, Canada, with immunization registry and laboratory administrative data. We defined COVID-19 cases as symptomatic laboratory-confirmed infections and long COVID as self-reported symptoms persisting ≥12 weeks. We assessed VE against COVID-19 and long COVID, stratified by infection history, using a test-negative design where vaccinated participants were compared to unvaccinated participants during the pre-Omicron period or to those twice vaccinated ≥6 months before laboratory testing during the Omicron period. RESULTS: Analyses included 8230 COVID-19 participants and 43 361 tested specimens. During the pre-Omicron period, 1- and 2-dose VE was 75% (95% CI: 64-83) and 95% (95% CI: 84-98) against COVID-19, respectively, and 91% (95% CI: 79-96) and 87% (95% CI: 22-98) against long COVID, respectively. During the Omicron period, booster dose VE was 41% (95% CI: 34-47) against COVID-19 and 57% (95% CI: 46-66) against long COVID, waning by 6 months. Hybrid VE in vaccinated and previously infected individuals ranged from 81% (95% CI: 38-94) to 92% (95% CI: 87-95) regardless of number of doses, prior infecting variant or median time since last immunological event up to 9 months. CONCLUSIONS: COVID-19 vaccination prevented long COVID during the pre-Omicron period and reduced the risk by more than half post-Omicron. With most of the population by now both vaccinated and infected, repeated booster doses may add little incremental value against long COVID, an observation with important public health, immunization program, and cost implications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".