Reporting Rates of Yellow Fever Vaccine 17D or 17DD-Associated Serious Adverse Events in Pharmacovigilance Data Bases: Systematic Review
Bibliographic record
Abstract
PURPOSE: To assess the reporting rates of serious adverse events attributable to yellow fever vaccination with 17D and 17DD strains as reported in pharmacovigilance databases, and assess reasons for differences in reporting rates. METHODS: We searched 9 electronic databases for peer reviewed and grey literature (government reports, conferences), in all languages. Reference lists of key studies were also reviewed to identify additional studies. RESULTS: We identified 2,415 abstracts, of which 472 were selected for full text review. We identified 15 pharmacovigilance databases which reported adverse events attributed to yellow fever vaccination, of which 10 contributed data to this review with about 107,600,000 patients (allowing for overlapping time periods for the studies of the US VAERS database), and the data are very heavily weighted (94%) by the Brazilian database. The estimates of serious adverse events form three groups. The estimates for Australia were low at 0/210,656 for "severe neurological disease" and 1/210,656 for YEL-AVD, and also low for Brazil with 9 hypersensitivity events, 0.23 anaphylactic shock events, 0.84 neurologic syndrome events and 0.19 viscerotropic events cases/million doses. The five analyses of partly overlapping periods for the US VAERS database provide an estimate of 3.6/cases per million YEL-AND in one analysis and 7.8 in another, and 3.1 YEL-AVD in one analysis and 3.9 in another. The estimates for the UK used only the inclusive term of "serious adverse events" not further classified into YEL-And or YEL-AND and reported 34 "serious adverse events." The Swiss database used the term "serious adverse events" and reported 7 such events (including 4 "neurologic reactions") for a reporting rate of 25 "serious adverse events"/million doses. CONCLUSIONS: Reporting rates for serious adverse events following yellow fever vaccination are low. Differences in reporting rates may be due to differences in definitions, surveillance system organisation, methods of reporting cases, administration of YFV with other vaccines, incomplete information about denominators, time intervals for reporting events, the degree of passive reporting, access to diagnostic resources, and differences in time periods of reporting.
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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 | 0.042 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.020 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".