Modeling the Cost-Effectiveness of the Next-Generation COVID-19 mRNA-1283 vaccine in the United States
Bibliographic record
Abstract
Abstract Aims COVID-19 disease burden in United States (US) adults ≥65 years and persons with underlying medical conditions remains high. This modeling study estimates the cost-effectiveness of the next-generation COVID-19 mRNA-1283 vaccine in those ages 12-64 at high-risk of severe COVID-19 outcomes and all adults ≥65 years. Methods mRNA-1283 was compared to no annual vaccination and originally licensed mRNA vaccines mRNA-1273 and BNT162b2. Analyses were conducted using a static decision-analytic model (1-year horizon). Vaccine effectiveness (VE) against infection and hospitalization for mRNA-1283 versus no vaccination was based on relative VE (rVE) from the Phase 3 pivotal randomized controlled trial comparing mRNA-1283 against mRNA-1273 and mRNA-1273 real-world data. rVE estimates for mRNA-1283 versus BNT162b2 were based on an indirect treatment comparison. The societal incremental cost per quality-adjusted life-year (QALY) gained and the benefit cost ratio (BCR) were calculated. Results During the 2025/2026 season, a single dose of mRNA-1283 was estimated to yield an incremental cost per QALY gained of $16,241 compared to no vaccine. The BCR for the base case strategy ranged from 2.16-9.74 USD returned for one dollar spent for mRNA-1283. mRNA-1283 was shown to dominate originally licensed COVID-19 vaccines in analyses of the target population. Results are sensitive to COVID-19 incidence, hospitalization rates, post-discharge mortality rates, and VE. Limitations The real-world effectiveness and safety of mRNA-1283 have not yet been established and relative VE estimates should be validated with real-world data. 2025/2026 COVID-19 incidence and vaccine uptake in the US is uncertain. Conclusions Study results suggest mRNA-1283 represents a highly cost-effective strategy (considering a $100,000-150,000 per QALY willingness-to-pay threshold) to reduce burden of COVID-19 among the target population. Given the finding of mRNA-1283 dominance in this population compared to originally approved mRNA vaccines, mRNA-1283 provides a valuable option to optimize US COVID-19 immunization programs and protect those most vulnerable.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".