Preferences for COVID-19 Vaccination in People With Chronic Immune-Mediated Inflammatory Diseases
Bibliographic record
Abstract
OBJECTIVE: To understand how people with chronic immune-mediated inflammatory diseases (IMIDs) trade off the benefits and risks of coronavirus disease 2019 (COVID-19) vaccine options. METHODS: We conducted an online discrete-choice experiment in people with IMIDs to quantify the relative importance (RI) of attributes relevant to COVID-19 vaccination. Participants were recruited between May and August 2021 through patient groups and clinics in Canada, and completed 10 choices where they selected 1 of 2 hypothetical vaccine options or no vaccine. The RI of each attribute was estimated and heterogeneity was explored through latent class analysis. RESULTS: The survey was completed by 551 people (89% female, mean age 46 yrs) with a range of IMIDs (inflammatory bowel disease [48%], rheumatoid arthritis [38%], systemic lupus erythematosus [16%]). Most had received 1 (94%) or 2 (64%) COVID-19 vaccinations. Across the ranges of levels considered, vaccine effectiveness was most important (RI = 66%), followed by disease flare (21%), rare but serious risks (9%), and number/timing of injections (4%). Patients would accept a risk of disease flare requiring a treatment change of ≤ 8.8% for a vaccine with a small absolute increase in effectiveness (10%). Of the 3 latent classes, the group with the greatest aversion to disease flare were more likely to be male and have lower incomes, but this group still valued effectiveness higher than other attributes. CONCLUSION: Patients perceived the benefits of COVID-19 vaccination to outweigh rare serious risks and disease flare. This supports COVID-19 vaccine strategies that maximize effectiveness, while recognizing the heterogeneity in preferences that exists.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".