Comparative Effectiveness of the Bivalent (Original/Omicron BA.4/BA.5) mRNA COVID-19 Vaccines mRNA-1273.222 and BNT162b2 Bivalent in Adults with Underlying Medical Conditions in the United States
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: This retrospective cohort study evaluated the relative vaccine effectiveness (rVE) of two bivalent (original/Omicron BA.4/BA.5) vaccines mRNA-1273.222 versus the BNT162b2 Bivalent in preventing COVID-19-related outcomes in adults with underlying medical conditions associated with increased risk for severe COVID-19. METHODS: In a linked electronic health record/claims dataset, US adults (≥18 years) with ≥1 underlying medical condition of interest who received either the bivalent vaccine between 31 August 2022 and 28 February 2023 were identified. The inverse probability of treatment weighting was used to adjust for cohort differences. Cohorts were followed up for COVID-19-related hospitalizations and outpatient encounters until 31 May 2023. Hazard ratios and rVEs were estimated using Cox regression. Subgroup analyses were performed on individuals with pre-specified comorbid conditions. RESULTS: 757,572 mRNA-1273.222 and 1,204,975 BNT162b2 Bivalent recipients were identified. The adjusted rVE over a median follow-up of 198 days was 10.9% (6.2%-15.2%) against COVID-19-related hospitalization and 3.2% (1.7%-4.7%) against COVID-19-related outpatient encounters. rVE estimates for COVID-19 hospitalizations among subgroups with comorbid conditions were as follows: diabetes 15.1% (8.7%-21.0%), cerebro- and cardiovascular disease 14.7% (9.0%-20.1%), chronic lung disease 11.9% (5.1%-18.2%), immunocompromised 15.0% (7.2%-22.2%), chronic kidney disease 8.4% (0.5%-15.7%). CONCLUSIONS: Overall, among adults with underlying medical conditions, mRNA-1273.222 was more effective than BNT162b2 Bivalent, especially in preventing COVID-19-related hospitalizations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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".