Suboptimal Immunization Coverage among Canadian Rheumatology Patients in Routine Clinical Care
Bibliographic record
Abstract
OBJECTIVE: To assess vaccination coverage and predictors of vaccination among a Canadian population of rheumatology patients in routine clinical care. METHODS: In this cross-sectional study, consecutive adult patients presenting to a tertiary rheumatology clinic at the McGill University Health Center between May and September 2015 were asked to fill a survey on vaccination. Patients self-identified as having rheumatoid arthritis (RA), systemic autoimmune rheumatic diseases (SARD), spondyloarthropathies (SpA), or other diseases (OD). Multivariate logistical regression analyses were performed to evaluate patient and physician factors associated with various vaccinations [for influenza, pneumococcus, and hepatitis B virus (HBV)]. Published Quebec general population influenza and pneumococcal vaccination rates in those aged ≥ 65 years were used as comparative baseline rates. RESULTS: Three hundred fifty-two patients were included in the analysis (RA: 136, SARD: 113, SpA: 47, OD: 56). Vaccination rates were reported as follows: (1) influenza: RA 48.5%, SARD 42.0%, SpA 31.9%, OD 88.9%, Quebec general population 58.5%; (2) pneumococcal: RA 42.0%, SARD 37.8%, SpA 29.7%, OD 33.3%, Quebec general population 53.2%; (3) HBV: RA 33.6%, SARD 55.6%, SpA 73.5%, OD 36.8%; and (4) herpes zoster: RA 5.6%, SARD 28.6%, SpA 25.0%, OD 16.7%. Physician recommendation was the strongest independent predictor of vaccination across all vaccine types (influenza: OR 8.56, 95% CI 2.80-26.2, p < 0.001; pneumococcal: OR 314, 95% CI 73.0-1353, p < 0.001; HBV: OR 12.8, 95% CI 5.27-31.1, p < 0.001). Disease group, disease duration, comorbidities, treatment type, and being followed by a primary care physician were not significantly associated with vaccination. CONCLUSION: There is suboptimal immunization coverage among ambulatory rheumatology patients. An important role for patient and physician education is highlighted in our study, especially because physician recommendation of vaccination was strongly predictive of vaccine 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".