Vaccine safety surveillance in Canada: Reports to CAEFISS, 2013–2016
Bibliographic record
Abstract
BACKGROUND: Canada has one of the strongest vaccine safety surveillance systems in the world. This system includes both passive surveillance of all vaccines administered and active surveillance of all childhood vaccines. OBJECTIVES: To provide 1) a descriptive analysis of the adverse events following immunization (AEFI) reports for vaccines administered in Canada, 2) an analysis of serious adverse events (SAEs) and 3) a list of the top ten groups of vaccines with the highest reporting rates. METHODS: Descriptive analyses were conducted of AEFI reports received by the Public Health Agency of Canada (PHAC) by August 14, 2017, for vaccines marketed in Canada and administered from January 1, 2013 to December 31, 2016. Data elements in this analysis include: type of surveillance program, AEFIs, demographics, health care utilization, outcome, seriousness of adverse events and type of vaccine. RESULTS: Over the four year period, 11,079 AEFI reports were received from across Canada. The average annual AEFI reporting rate was 13.4/100,000 doses distributed in Canada for vaccines administered during 2013-2016 and was found to be inversely proportional to age. The majority of reports (92%) were non-serious events, involving vaccination site reactions rash and allergic events. Overall, there were 892 SAE reports, for a reporting rate of 1.1/100,000 doses distributed during 2013-2016. Of the SAE reports, the most common primary AEFIs were anaphylaxis followed by seizure. Meningococcal serogroup C conjugate vaccines (given concomitantly) were responsible for the highest rates of AEFIs, at 91.6 per 100,000 doses distributed. There were no unexpected vaccine safety issues identified or increases in frequency or severity of expected adverse events. CONCLUSION: Canada's continuous monitoring of the safety of marketed vaccines during 2013-2016 did not identify any increase in the frequency or severity of AEFIs, previously unknown AEFIs, or areas that required further investigation or research. Vaccines marketed in Canada continue to have an excellent safety profile.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".