Rates and determinants of seasonal influenza vaccination in pregnancy and association with neonatal outcomes
Bibliographic record
Abstract
BACKGROUND: There is growing evidence that seasonal influenza vaccination in pregnancy has benefits for mother and baby. We determined influenza vaccination rates among pregnant women during the 2 nonpandemic influenza seasons following the 2009 H1N1 pandemic, explored maternal factors as predictors of influenza vaccination status and evaluated the association between maternal influenza vaccination and neonatal outcomes. METHODS: We used a population-based perinatal database in the province of Nova Scotia, Canada, to examine maternal vaccination rates, determinants of vaccination status and neonatal outcomes. Our cohort included women who gave birth between Nov. 1, 2010, and Mar. 31, 2012. We compared neonatal outcomes between vaccinated and unvaccinated women using logistic regression analysis. RESULTS: Overall, 1958 (16.0%) of 12,223 women in our cohort received the influenza vaccine during their pregnancy. Marital status, parity, location of residence (rural v. urban), smoking during pregnancy and maternal influenza risk status were determinants of maternal vaccine receipt. The odds of preterm birth was lower among infants of vaccinated women than among those of nonvaccinated women (adjusted odds ratio [OR] 0.75, 95% confidence interval [CI] 0.60-0.94). The rate of low-birth-weight infants was also lower among vaccinated women (adjusted OR 0.73, 95% CI 0.56-0.95). INTERPRETATION: Despite current guidelines advising all pregnant women to receive the seasonal influenza vaccine, influenza vaccination rates among pregnant women in our cohort were low in the aftermath of the 2009 H1N1 pandemic. This study and others have shown an association between maternal influenza vaccination and improved neonatal outcomes, which supports stronger initiatives to promote vaccination during pregnancy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 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".