Does influenza vaccination improve pregnancy outcome? Methodological issues and research needs
Bibliographic record
Abstract
Evidence that influenza vaccination during pregnancy is safe and effective at preventing influenza disease in women and their children through the first months of life is increasing. Several reports of reduced risk of adverse outcomes associated with influenza vaccination have generated interest in its potential for improving pregnancy outcome. Gavi, the Vaccine Alliance, estimates maternal influenza immunization programs in low-income countries would have a relatively modest impact on mortality compared to other new or under-utilized vaccines, however the impact would be substantially greater if reported vaccine effects on improved pregnancy outcomes were accurate. Here, we examine the available evidence and methodological issues bearing on the relationship between influenza vaccination and pregnancy outcome, particularly preterm birth and fetal growth restriction, and summarize research needs. Evidence for absence of harm associated with vaccination at a point in time is not symmetric with evidence of benefit, given the scenario in which vaccination reduces risk of influenza disease and, in turn, risk of adverse pregnancy outcome. The empirical evidence for vaccination preventing influenza in pregnant women is strong, but the evidence that influenza itself causes adverse pregnancy outcomes is inconsistent and limited in quality. Studies of vaccination and pregnancy outcome have produced mixed evidence of potential benefit but are limited in terms of influenza disease assessment and control of confounding, and their analytic methods often fail to fully address the longitudinal nature of pregnancy and influenza prevalence. We recommend making full use of results of randomized trials, re-analysis of existing observational studies to account for confounding and time-related factors, and quantitative assessment of the potential benefits of vaccination in improving pregnancy outcome, all of which should be informed by the collective engagement of experts in influenza, vaccines, and perinatal health.
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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 | 0.488 | 0.761 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".