COVID-19 Vaccine Booster Uptake and Effectiveness Among Persons With Systemic Autoimmune and Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To assess coronavirus disease 2019 (COVID-19) booster uptake and vaccine effectiveness (VE) in reducing COVID-19 hospitalization in persons with systemic autoimmune and rheumatic diseases (SARDs). METHODS: Adult patients with SARDs receiving disease-modifying antirheumatic drugs at 4 health systems in the United States were identified retrospectively. Exposures were (1) receipt of an additional dose of monovalent COVID-19 vaccine prior to January 1, 2022, with follow-up to August 31, 2022; and (2) receipt of bivalent COVID-19 vaccine between September 1, 2022 and August 31, 2023. RESULTS: Among 201,165 patients with SARDs, 126,756 (63%) had received 1 monovalent booster as of January 1, 2022. During 94,842 person-years (PY) of follow-up, the COVID-19 hospitalization rate was 15.6 per 1000 PY among those who had received a monovalent booster vs 20.1 per 1000 PY among those who had not, with an adjusted VE of 38% (95% CI 31-44%) and a number needed to vaccinate of 267 (95% CI 230-325). Among 246,991 patients with SARDs with 233,622 PY of follow-up in the bivalent study period, the COVID-19 hospitalization rate was 7.9 per 1000 PY for the 87,906 (36%) patients who received the bivalent vaccine vs 10.2 per 1000 PY for the patients who did not. The adjusted VE of the bivalent vaccine was 32% (95% CI 24-39%) with a number needed to vaccinate of 617 (95% CI 500-838). CONCLUSION: COVID-19 booster vaccinations provided significant protection against severe COVID-19 in persons with SARDs. Thus, increasing vaccine uptake should be prioritized in this high-risk immunocompromised population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".