Assessing the Impact of COVID-19 Vaccination on Preterm Birth: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
During the coronavirus diseases 2019 (COVID-19) pandemic, the safety and efficacy of vaccination during pregnancy, particularly regarding the risk of preterm birth, have been a subject of concern. This systematic review aims to evaluate the impact of COVID-19 vaccination on preterm birth risk and to inform clinical practice and public health policies. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a database search included PubMed, Embase, and Scopus, conducted up until October 2023. Inclusion criteria focused on studies that examined COVID-19 vaccination during pregnancy and its correlation with preterm birth, defined as a birth before 37 weeks of gestation. Six studies met these criteria, encompassing 35,612 patients. A quality assessment was performed using the Newcastle–Ottawa Scale and the Cochrane Collaboration’s tool, with the risk of bias evaluated via a funnel plot analysis and an Egger’s regression test. The studies demonstrated geographical diversity, mainly from Israel, Romania, and the United States, with a blend of prospective and retrospective designs. The patient cohort’s mean age was 31.2 years, with common comorbidities such as gestational diabetes and obesity affecting 9.85% of the total population. The vaccination types varied across the studies, with BNT162b2 being the most used. The results indicated a low heterogeneity among the included studies, evidenced by a Cochran’s Q statistic of 2.10 and an I2 statistic of 13%. The meta-analysis yielded a pooled odds ratio (OR) for a preterm birth risk post-vaccination of approximately 1.03 (95% CI: 0.82–1.30), suggesting no significant increase in preterm birth risk was associated with COVID-19 vaccination. Notable findings included a low preterm birth rate (as low as 0.6% and up to 6.1%) with minimal differences in neonatal outcomes, such as birth weight and APGAR (appearance, pulse, grimace, activity, and respiration) scores between vaccinated and unvaccinated groups. This study concludes that a COVID-19 vaccination during pregnancy does not significantly increase the risk of preterm birth. These findings are crucial for reassuring healthcare providers and pregnant women about the safety of COVID-19 vaccines and supporting their use in public health strategies during the pandemic.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.049 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".