Equity Considerations in COVID‐19 Vaccination Studies of Individuals With Autoimmune Inflammatory Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: We sought to examine the extent to which populations experiencing inequities were considered in studies of COVID-19 vaccination in individuals with autoimmune inflammatory rheumatic diseases (AIRDs). METHODS: We included all studies (n = 19) from an ongoing Cochrane living systematic review on COVID-19 vaccination in patients with AIRDs. We used the PROGRESS-Plus framework (place of residence, race/ethnicity, occupation, gender/sex, religion, education, socioeconomic status, and social capital, plus: age, multimorbidity, and health literacy) to identify factors that stratify health outcomes. We assessed equity considerations in relation to differences in COVID-19 baseline risk, eligibility criteria, and description of participant characteristics and attrition, controlling for confounding factors, subgroup analyses, and applicability of findings. RESULTS: All 19 studies were cohort studies that followed individuals with AIRDs after vaccination. Three studies (16%) described differences in baseline risk for COVID-19 across age. Two studies (11%) defined eligibility criteria based on occupation and age. All 19 studies described participant age and sex. Twelve studies (67%) controlled for age and/or sex as confounders. Eight studies (47%) conducted subgroup analyses across at least 1 PROGRESS-Plus factor, most commonly age. Ten studies (53%) interpreted applicability in relation to at least 1 PROGRESS-Plus factor, most commonly age (47%), then ethnicity (16%), sex (16%), and multimorbidity (11%). CONCLUSION: Sex and age were the most frequently considered PROGRESS-Plus factors in studies of COVID-19 vaccination in individuals with AIRDs. The generalizability of evidence to populations experiencing inequities is uncertain. Future COVID-19 vaccine studies should report participant characteristics in more detail to inform guideline recommendations.
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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 | 0.276 | 0.409 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".