Comparative Effectiveness of BNT162b2 and mRNA-1273 Vaccines Against COVID-19 Infection Among Patients With Systemic Autoimmune Rheumatic Diseases on Immunomodulatory Medications
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness of mRNA vaccines (BNT162b2 vs mRNA-1273) against coronavirus disease 2019 (COVID-19) infection among patients with systemic autoimmune rheumatic diseases (SARDs) on immunomodulatory medications. METHODS: We identified patients with SARDs being treated with disease-modifying antirheumatic drugs (DMARDs) and/or glucocorticoids in the Mass General Brigham healthcare system who received either BNT162b2 or mRNA-1273 as their initial vaccine series. Patients were followed until positive SARS-CoV-2 test, death, or February 22, 2022. We compared the risk of breakthrough infection between BNT162b2 and mRNA-1273 vaccine recipients using time-stratified, overlap propensity score (PS)-weighted Cox proportional hazard models. RESULTS: We identified 9838 patients with SARDs who received BNT162b2 or mRNA-1273. Demographic and clinical characteristics were similar in both groups after overlap weighting: mean age 61 years, 75% female, 52% with rheumatoid arthritis, 74% receiving conventional synthetic DMARDs, and 43% receiving biologic DMARDs. Of 5516 BNT162b2 and 4322 mRNA-1273 recipients, 446 and 329 had a breakthrough infection, respectively. The corresponding time-stratified PS-weighted rate difference of breakthrough infection was 0.71 (95% CI -0.70 to 2.12) per 1000 person-months with a weighted hazard ratio (HR) of 1.12 (95% CI 0.90 to 1.39). When follow-up was censored prior to the Omicron wave, there was a trend toward higher breakthrough risk with BNT162b2 vs mRNA-1273 (weighted HR 1.34, 95% CI 0.91 to 1.98). CONCLUSION: Among patients with SARDs, the risk of breakthrough COVID-19 infection is similar after receiving either BNT162b2 or mRNA-1273. Patients with SARDs initiating the vaccine series should be encouraged to receive whichever mRNA vaccine is available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".