Risk factors affecting COVID-19 vaccine effectiveness identified from 290 cross-country observational studies until February 2022: a meta-analysis and meta-regression
Bibliographic record
Abstract
BACKGROUND: Observational studies made it possible to assess the impact of risk factors on the long-term effectiveness of mRNA and adenoviral vector (AdV) vaccines against COVID-19. METHODS: A computerized literature search was undertaken using the MEDLINE, EMBASE, and MedRxiv databases to identify eligible studies, with no language restrictions, published up to 28 February 2022. Eligible were observational studies assessing vaccine effectiveness (VE) by disease severity with reference groups of unvaccinated participants or participants immunized with one, two, or three vaccine doses. Our study was carried out in compliance with the PRISMA and MOOSE guidelines. The risk of study bias was identified using the Newcastle-Ottawa Quality Assessment Scale. The GRADE guidelines were applied to assess the strength of evidence for the primary outcome. The synthesis was conducted using a meta-analysis and meta-regression. RESULTS: Out of a total of 14,155 publications, 290 studies were included. Early VE of full vaccination against COVID-19 of any symptomatology and severity decreased from 96% (95% CI, 95-96%) for mRNA and from 86% (95% CI, 83-89%) for AdV vaccines to 67% for both vaccine types in the last 2 months of 2021. A similar 1-year decline from 98 to 86% was found for severe COVID-19 after full immunization with mRNA, but not with AdV vaccines providing persistent 82-87% effectiveness. Variant-reduced VE was only associated with Omicron regardless of disease severity, vaccine type, or vaccination completeness. The level of protection was reduced in participants aged >65 years, with a comorbidity or those in long-term care or residential homes independently of the number of doses received. The booster effect of the third mRNA dose was unclear because incompletely restored effectiveness, regardless of disease severity, declined within a short-term interval of 4 months. CONCLUSIONS: Full vaccination provided an early high, yet waning level of protection against COVID-19 of any severity with a strong impact on the high-risk population. Moreover, the potential risk of new antigenically distinct variants should not be underestimated, and any future immunization strategy should include variant-updated vaccines.
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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 | 0.048 | 0.090 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.082 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".