COVID-19 Vaccination during Pregnancy and Infant Health Outcomes
Bibliographic record
Abstract
Background: Pregnant women are considered a high-risk group for severe Coronavirus Disease of 2019 (COVID-19); maternal COVID-19 vaccination is recommended during pregnancy in Canada and many countries worldwide. There is limited comparative epidemiological evidence on infant outcomes following maternal COVID-19 vaccination during pregnancy. Objective: To evaluate newborn and early infant health outcomes associated with maternal mRNA COVID-19 vaccination during pregnancy. Methods: We used data on mother-infant dyads from multiple linked health administrative databases in Ontario. Study designs included test-negative, cohort, and exposure-discordant sibling-matched analysis. We adjusted for measured potential confounders using multivariable logistic regression and propensity score methods. We performed quantitative bias analyses to assess the sensitivity of results to residual confounding and selection bias. Results: Maternal COVID-19 vaccination with two doses during pregnancy was highly effective against delta and moderately effective against omicron infection or hospital admission in infants during the first six months of life. A third vaccine dose bolstered protection against omicron. Effectiveness for two doses against omicron was highest with vaccination in the third trimester, and effectiveness decreased in infants beyond eight weeks of age. Postpartum maternal vaccination was moderately effective against delta infection but conferred little protection against omicron in infants younger than six months of age. Compared with no maternal COVID-19 vaccination preconception or at any point during pregnancy, vaccination with at least one dose during pregnancy was associated with lower risks of severe neonatal morbidity, neonatal death, and neonatal intensive care unit admission and no increases in neonatal readmission and hospital admission up to six months of age. Maternal vaccination during the first trimester was not associated with major or most organ system congenital anomalies. There was a small increase in ankyloglossia (tongue-tie) in infants of mothers vaccinated in the first versus second or third trimesters; residual confounding might have contributed to this apparent association and type I error, due to multiple comparisons, cannot be ruled out. Conclusions: This research substantially contributes to the understanding of the effectiveness and safety of maternal COVID-19 vaccination during pregnancy for newborns and young infants and can inform clinical decisions and guidance on COVID-19 vaccination during pregnancy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".