The role of co-morbidities in the development of an AEFI after COVID-19 vaccination in a large prospective cohort with patient-reported outcomes in the Netherlands
Bibliographic record
Abstract
BACKGROUND: The effect of a preexisting comorbidity on the occurrence of adverse events after immunization (AEFIs) has been studied poorly. In this longitudinal cohort study, we assess the association between co-morbidities and the occurrence of AEFIs after COVID-19 vaccination. Also, we described the occurrence of flare-ups and their manifestation after COVID-19 vaccination in people with rheumatic diseases. RESEARCH DESIGN AND METHODS: We performed multivariable logistic regression to investigate the association between the occurrence of AEFIs and 10 common comorbidities using patient-reported data from people vaccinated with the AstraZeneca, Johnson&Johnson, Moderna, or Pfizer vaccine. RESULTS: Occurrence of any AEFI, injection site reactions, headache, fatigue, and/or malaise was significantly associated with presence of comorbidities, including psychological disorders, musculoskeletal disorders, and endocrine disorders after the first and second doses (OR ranges 1.23-1.77). One participant with rheumatoid arthritis experienced a flare-up after receiving the first dose of the AstraZeneca vaccine. DISCUSSION/CONCLUSION: The results showed that the odds of reporting an AEFI after COVID-19 vaccination is significantly higher in the presence of some comorbidities whilst flare-ups are uncommon after receiving COVID-19 vaccination in people with rheumatic disease. In-depth research is needed to validate our results and unravel the observed associations from a mechanistic perspective.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".