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.
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 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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.060 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".