National safety monitoring of vaccines from the Canadian Adverse Events Following Immunization Surveillance System (CAEFISS), 2018–2019
Bibliographic record
Abstract
Background: The Canadian Adverse Events Following Immunization Surveillance System (CAEFISS) is a comprehensive vaccine safety surveillance system that includes both passive and active surveillance of vaccines administered in Canada. This work presents a summary of adverse events following immunization (AEFI) nationally for 2018 and 2019. Methods: Data extracted from CAEFISS included all AEFI reports received by the Public Health Agency of Canada by April 30, 2022, for vaccines marketed in Canada and administered between January 1, 2018, and December 31, 2019. Descriptive statistics were conducted on AEFI reports by type of surveillance program (i.e., active vs. passive), AEFIs, demographics, healthcare utilization, outcome, seriousness of adverse events and type of vaccine. Results: Between 2018 and 2019, 5,875 AEFI reports were received from across Canada. The average annual AEFI reporting rate was 10.9/100,000 doses distributed in Canada for vaccines administered during 2018-2019 and was found to be inversely proportional to age. The majority of reports (91%) were non-serious events, involving vaccination site reactions, rash and allergic events. Overall, there were 511 serious adverse event reports during 2018-2019. Of the serious adverse event reports, the most common primary AEFIs were anaphylaxis followed by seizure. There were no unexpected vaccine safety issues identified or increases in frequency or severity of adverse events. Conclusion: Canada's continuous monitoring of the safety of marketed vaccines during 2018-2019 did not identify any increase in the frequency or severity of AEFIs, previously unknown AEFIs, or areas that required further investigation or research.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".