Infant Respiratory Outcomes Associated with Prenatal Exposure to Maternal 2009 A/H1N1 Influenza Vaccination
Bibliographic record
Abstract
BACKGROUND: Infants are at high risk for influenza illness, but are ineligible for vaccination before 6 months. Transfer of maternal antibodies to the fetus has been demonstrated for 2009 A/H1N1 pandemic vaccines; however, clinical effectiveness is unknown. Our objective was to evaluate the association between 2009 A/H1N1 pandemic vaccination during pregnancy and rates of infant influenza and pneumonia. METHODS: We linked a population-based birth cohort to administrative databases to measure rates of influenza and pneumonia diagnosed during ambulatory physician visits, hospitalizations and emergency department visits during one year of follow-up. We estimated incidence rate ratios and 95% confidence intervals (95% CI) using Poisson regression, comparing infants born to A/H1N1-vaccinated women (vaccine-exposed infants) with unexposed infants, adjusted for confounding using high-dimensional propensity scores. RESULTS: Among 117,335 infants in the study, 36,033 (31%) were born to A/H1N1-vaccinated women. Crude rates of influenza during the pandemic (per 100,000 infant-days) for vaccine-exposed and unexposed infants were similar (2.19, 95% CI: 1.27-3.76 and 3.60, 95% CI: 2.51-5.14, respectively), as were crude rates of influenza and pneumonia combined. We did not observe any significant differences in rates of study outcomes between study groups during the second wave of the 2009 A/H1N1 pandemic, nor during any post-pandemic time period. CONCLUSION: We observed no difference in rates of study outcomes among infants born to A/H1N1-vaccinated mothers relative to unexposed infants born during the second A/H1N1 pandemic wave; however, due to late availability of the pandemic vaccine, the available follow-up time during the pandemic time period was very limited.
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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 | 0.000 |
| 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) | 0.002 | 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".