A global survey of adverse event following immunization surveillance systems for pregnant women and their infants
Bibliographic record
Abstract
Strengthening antenatal care as a platform for maternal immunization is a priority of the World Health Organization (WHO). Systematic surveillance for adverse events following immunization (AEFI) in pregnancy is needed to identify vaccine safety events. We sought to identify active and passive AEFI surveillance systems for pregnant women and infants. Representatives from all National Pharmacovigilance Centers and a convenience sample of vaccine safety experts were invited to complete a 14-item online survey in English, French or Spanish. The survey captured maternal immunization policies, and active and passive AEFI surveillance systems for pregnant women and infants in respondents' countries. The analysis was descriptive. We received responses from 51/185 (28%) invited persons from 47/148 (32%) countries representing all WHO regions, and low, middle and high-income countries. Thirty countries had national immunization policies targeting pregnant women. Eleven countries had active surveillance systems to detect serious AEFI in pregnant women and/or their infants, including six low and middle-income countries (LMIC). Thirty-nine countries had passive surveillance systems, including 23 LMIC. These active and passive surveillance programs cover approximately 8% and 56% of the worldwide annual birth cohort, respectively. Data from one active and four passive systems have been published. We identified 50 active and passive AEFI surveillance systems for pregnant women and infants, but few have published their findings. AEFI surveillance appears to be feasible in low and high resource settings. Further expansion of AEFI surveillance for pregnant women and sharing of vaccine safety information will provide additional evidence in support of maternal immunization policies.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".