Background incidence rates of adverse events of special interest related to COVID-19 vaccines in Ontario, Canada, 2015 to 2020, to inform COVID-19 vaccine safety surveillance
Bibliographic record
Abstract
Abstract Background Background incidence rates are critical in pharmacovigilance to facilitate identification of vaccine safety signals. We estimated background incidence rates of nine adverse events of special interest related to COVID-19 vaccines in Ontario, Canada. Methods We conducted a population-based retrospective observational study using linked health administrative databases for hospitalizations and emergency department visits among Ontario residents. We estimated incidence rates of Bell’s palsy, idiopathic thrombocytopenia, febrile convulsions, acute disseminated encephalomyelitis, myocarditis, pericarditis, Kawasaki disease, Guillain-Barré syndrome, and transverse myelitis during five pre-pandemic years (2015– 2019) and 2020. Results The average annual population was 14 million across all age groups with 51% female. The pre-pandemic mean annual rates per 100,000 population during 2015–2019 were 43.9 for idiopathic thrombocytopenia, 27.8 for Bell’s palsy, 25.0 for febrile convulsions, 22.8 for acute disseminated encephalomyelitis, 11.3 for myocarditis/pericarditis, 8.6 for pericarditis, 2.9 for myocarditis, 1.9 for Guillain-Barré syndrome, 1.7 for transverse myelitis, and 1.6 for Kawasaki disease. Females had higher rates of acute disseminated encephalomyelitis and transverse myelitis while males had higher rates of myocarditis, pericarditis, and Guillain-Barré syndrome. Bell’s palsy, acute disseminated encephalomyelitis, and Guillain-Barré syndrome increased with age. The mean rates of myocarditis and/or pericarditis increased with age up to 79 years; males had higher rates than females: from 12–59 years for myocarditis and ≥12 years for pericarditis. Febrile convulsions and Kawasaki disease were predominantly childhood diseases and generally decreased with age. Conclusions Our estimated background rates will permit estimating numbers of expected events for these conditions and facilitate detection of potential safety signals following COVID-19 vaccination.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".