Reported Adverse Events Following SARS-CoV-2 Vaccinations in the Canadian Province of Alberta and Associated Risk Factors: A Retrospective Cohort Study
Bibliographic record
Abstract
Background/objectives: Coronavirus-19 (COVID-19) vaccines represent a significant milestone in the fight against coronavirus disease. Ongoing post-marketing surveillance and research are crucial for ensuring vaccine safety and effectiveness, aiding public health planning. Methods: Our retrospective cohort study included Albertans five years and older and vaccinated with at least one dose of an approved COVID-19 vaccine between 14 December 2020 and 30 April 2022. This epidemiological study aimed to determine the incidence of reported adverse events following immunization (AEFI) in Alberta and identify associated risk factors. Results: The study included 3,527,106 vaccinated Albertans who met the study inclusion criteria. A total of 2541 individuals (72.0 per 100,000) reported an AEFI, with 2759 adverse events, most of which occurred following the first dose of vaccine and within the first week post-vaccination. Of these, 70.4% were female, and the highest incidence was in the 35–54 age group. Given that mRNA vaccines were predominantly administered across Canada, we report AEFI rates (per 100,000 doses) for the mRNA vaccine brands at 27.7 for Pfizer and 40.7 for Moderna. Allergic events were the most frequently reported AEFI, followed by adenopathy. Logistic regression analysis indicated that sex (with females at higher risk), presence of comorbidities, days to symptom onset, vaccine type (mRNA vs. mixed doses), and the number of doses were significant factors associated with an AEFI event. Conclusions: Our study provides valuable information to guide policies surrounding COVID-19 vaccination. While the risk of serious adverse events was low in the population-based sample, further research is warranted to identify and investigate other possible risk factors that are still unknown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".