Predictors of Influenza Vaccination in Early Rheumatoid Arthritis 2017‐2021: Results From the Canadian Early Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: Adults with rheumatoid arthritis (RA) are at a higher risk for infections, including influenza and related complications. We identified influenza vaccination coverage in adults newly diagnosed with RA and examined sociodemographic RA characteristics and attitudes associated with vaccination. METHODS: We used data from patients enrolled in the Canadian Early Arthritis Cohort between September 2017 and February 2021. At enrollment, participants reported their vaccination status in the previous year and completed the Beliefs About Medicines Questionnaire (BMQ). Clinical data were obtained from medical records. Logistic regression was used to identify predictors of vaccination in the year after RA diagnosis. RESULTS: The baseline analytic sample of 431 patients were mostly White (80%) women (67%) with a mean age of 56 (SD 14) years. Prediagnosis, influenza vaccine coverage was 38%, increasing to 46% post diagnosis in the longitudinal sample (n = 229). Participants with previous influenza vaccination (odds ratio [OR] 15.33; 95% confidence interval [CI] 6.37-36.90), on biologics or JAKs (OR 5.42; 95% CI 1.72-17.03), and with a higher change in BMQ Necessity-Concerns Differential scores (OR 1.08; 95% CI 1.02-1.15) had greater odds, whereas women (OR 0.32; 95% CI 0.14-0.71), participants with a non-White racial background (OR 0.13; 95% CI 0.04-0.51), and participants currently smoking (OR 0.09; 95% CI 0.02-0.37) had lower odds of influenza vaccine coverage. CONCLUSION: Influenza vaccination coverage in patients with early RA remains below national targets in adults living with a chronic condition. Discussing vaccine history and medication attitudes at initial clinic visits with new patients with RA may enhance vaccine acceptance and uptake.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".