The Potential Clinical and Economic Impact of the Next-Generation COVID-19 mRNA-1283 Vaccine in Canada
Bibliographic record
Abstract
Abstract Background With continued high disease burden observed in vulnerable groups and fiscal responsibility shifting to Canada’s jurisdictions, assessing the economic value of COVID-19 vaccines is critical for optimizing COVID-19 prevention. This study estimated the public health impact and economically justifiable price (EJP) of Moderna’s next-generation COVID-19 vaccine (mRNA-1283) versus no vaccination in Canada, and relative to currently authorized COVID-19 vaccines (mRNA-1273; BNT-162b2). Methods The target population included individuals aged ≥65 years and 12-64 years at high-risk of severe COVID-19 outcomes, consistent with 2025/2026 national guidelines. Analyses were conducted using a static decision-analytic model (1-year horizon) from a publicly funded healthcare payer perspective. Vaccine efficacy against infection and hospitalization for mRNA-1283 versus no 2024/2025 vaccination was based on mRNA-1283’s pivotal trial and mRNA-1273 real-world data. Clinical outcomes included symptomatic infections, hospitalizations, deaths, and number needed to vaccinate (NNV); economic outcomes included total costs, quality-adjusted life-years (QALY), and EJP at a $50,000/QALY willingness-to-pay threshold. Sensitivity analyses were performed. Results Compared to no vaccine, annual vaccination with mRNA-1283 prevented 288,912 symptomatic infections (NNV=15), 11,710 hospitalizations (NNV=364), and 2,194 deaths (NNV=1,944). The EJP for mRNA-1283 was $325 ($230-$771 in scenario analyses). Semi-annual dosing for those ≥65 years or ≥80 years averted additional hospitalizations and deaths compared to annual vaccination. mRNA-1283 prevented an additional 2,873-3,689 hospitalizations and 537-690 deaths compared to currently authorized vaccines. EJPs for mRNA-1283 were $78 and $103 when compared to mRNA-1273 and BNT162b2, respectively. Interpretation mRNA-1283 could reduce the COVID-19 clinical burden and provide economic value for the NACI-recommended population, exceeding current COVID-19 mRNA vaccines.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".