The Effectiveness and Influence of COVID‐19 Vaccination on Perinatal Individuals and Their Newborns: An Updated Meta‐Analysis
Bibliographic record
Abstract
Background: The COVID‐19 pandemic has disproportionately affected pregnant individuals, increasing risks of severe illness and adverse outcomes. While vaccination is a key mitigation strategy, initial exclusion from clinical trials led to limited safety data. Despite evidence of vaccine effectiveness, hesitancy persists in this population. Objective and Sources: This meta‐analysis aims to evaluate the efficacy and impact of COVID‐19 vaccination in pregnant individuals, synthesizing evidence from 82 studies (3,676,654 participants) retrieved from PubMed, Embase, Cochrane Library, and Scopus (2019–2024). Study quality was assessed using the Newcastle–Ottawa scale (80/82 scored ≥ 7). Key Findings: Vaccination reduced maternal SARS‐CoV‐2 infection risk by 48% (odds ratio [OR] = 0.52), with mRNA vaccines showing higher efficacy (52% vs. 43% for inactivated). Maternal hospitalization risk decreased by 42% (OR = 0.58), and severe outcomes by 50% (OR = 0.50). Furthermore, neonatal outcomes improved, including reduced infection (OR = 0.69), preterm birth (OR = 0.87), stillbirth (OR = 0.64), and neonatal death (OR = 0.47). Protection against neonatal death was stronger in individuals without prior infection (OR = 0.43). Third‐trimester vaccination may offer better protection against preterm birth. Conclusion: Overall, COVID‐19 vaccination during pregnancy effectively mitigates infection and adverse maternal/neonatal outcomes, supporting its clinical recommendation.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".