Influenza Vaccine Hesitancy and Its Determinants Among Rheumatology Patients
Bibliographic record
Abstract
OBJECTIVE: Patients with rheumatic disease (RD) have an increased risk of influenza and its complications. Despite inactivated influenza vaccine (IIV) recommendations, IIV uptake in patients with RD is suboptimal, a problem of increasing importance in the COVID-19 era. We estimated the frequency of IIV hesitancy and associated factors among Canadian patients with RD. METHODS: A cross-sectional vaccine hesitancy survey was completed by rheumatology clinic patients (November 2019 to January 2020). Patients rated their likelihood of receiving the influenza vaccine (scale of 0-10). We categorized these as follows: likely to refuse (scale of 0-2), uncertain (scale of 3-7), or likely to accept (scale of 8-10). Multivariate logistical regression was used to evaluate factors associated with vaccine hesitancy. RESULTS: A total of 282 patients (63.5% of those approached) completed the survey, with 165 (58.5%) being likely to accept, 67 (23.8%) being likely to refuse, and 50 (17.7%) uncertain. Uncertain patients were younger and more likely to be employed than those in the other two groups. No previous influenza vaccination (odds ratio [OR] 36.6, 95% confidence interval [CI] 5.3-252.9), belief that vaccination should not be mandatory (OR 0.1, 95% CI 0.0-0.7), unwillingness to take time off work to be vaccinated (OR 6.8, 95% CI 1.5-30.6), and distrust in pharmaceutical companies (OR 41.0, 95% CI 5.6-301.5) predicted likeliness to refuse. Reluctance to pay for vaccination (OR 2.8, 95% CI 1.1-7.5) and no previous influenza vaccination (OR 18.9, 95% CI 3.3-109.7) predicted uncertainty. CONCLUSION: More than 40% of rheumatology patients are either likely to refuse or uncertain about receiving IIV. This contributes to suboptimal vaccine coverage in this population. Interventions addressing these concerns are needed, particularly in the COVID-19 era.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".