Safety of co-administration of COVID-19 and seasonal influenza vaccines in individuals with autoimmune diseases from the Canadian National Vaccine Safety Network
Bibliographic record
Abstract
The Canadian National Vaccine Safety Network conducted active safety surveillance for adverse events following COVID-19 and influenza immunizations. This analysis evaluated the association between the administration of mRNA COVID-19 vaccines, influenza vaccines, or co-administration of both, and health events that prevented daily activities, caused work absenteeism, or necessitated medical consultation among individuals with autoimmune diseases. Between September and December 2022, vaccinated and unvaccinated participants from seven provinces and territories self-reported health events within 7 days post-vaccination or over a 7-day period for unvaccinated individuals. This analysis focused on individuals self-reporting autoimmune diseases. Surveys were completed by 6,506 individuals: 1,743 received co-administered vaccines, 2,986 received COVID-19 vaccines alone, 491 received influenza vaccines alone, and 1,286 were unvaccinated. Health event rates were 9.5% for co-administration, 9.3% for COVID-19 alone, 5.9% for influenza alone, and 6.1% for unvaccinated controls. Compared to unvaccinated individuals, the risk of health events was higher for COVID-19 and influenza co-administration [adjusted relative risk (aRR): 1.89, 95% confidence interval (95% CI) 1.41–2.52], and COVID-19 alone [aRR: 1.86, 95% CI, 1.40–2.47], but not for influenza alone (aRR: 1.16, 95% CI, 0.76–1.78). No significant change in emergency department visits or hospitalizations was observed in any vaccine group compared to unvaccinated controls. In individuals with autoimmune diseases, mRNA COVID-19 vaccination increases mild to moderate health events compared to unvaccinated individuals. However, the rate of these events was similar when COVID-19 vaccines were administered alone or concomitantly with influenza vaccines, indicating no additional risk associated with co-administration.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".