The potential clinical impact and cost-effectiveness of the updated COVID-19 mRNA Fall 2023 vaccines in the United States
Bibliographic record
Abstract
Abstract Objectives To assess the potential clinical impact and cost-effectiveness of COVID-19 mRNA vaccines updated for Fall 2023 in adults ≥18 years over a 1-year analytic time horizon (September 2023-August 2024). Methods A compartmental Susceptible-Exposed-Infected-Recovered model was updated to reflect COVID-19 in summer 2023. Numbers of symptomatic infections, COVID-19 related hospitalizations and deaths, and costs and quality-adjusted life-years (QALYs) gained were calculated using a decision tree model. The incremental cost-effectiveness ratio (ICER) of a Moderna updated mRNA Fall 2023 vaccine (Moderna Fall Campaign) was compared to no additional vaccination. Potential differences between the Moderna and the Pfizer-BioNTech Fall 2023 vaccines were examined. Results Base case results suggest the Moderna Fall Campaign would decrease the expected 64.2 million symptomatic infections by 7.2 million (11%) to 57.0 million. COVID-19-related hospitalizations and deaths are expected to decline by 343,000 (–29%) and 50,500 (–33%), respectively. The Moderna Fall Campaign would increase QALYs by 740,880 and healthcare costs by $5.7 billion relative to No Vaccine, yielding an ICER of $7,700 per QALY gained. Using a societal cost perspective, the ICER is $2,100. Sensitivity analyses suggest that vaccine effectiveness, COVID-19 incidence, hospitalization rates and costs drive cost-effectiveness. With a relative vaccine effectiveness (rVE) of Moderna versus Pfizer-BioNTech of 5.1% for infection and 9.8% for hospitalization, use of the Moderna vaccine is expected to prevent 24,000 more hospitalizations and 3,300 more deaths than the Pfizer-BioNTech vaccine. Limitations and Conclusions As COVID-19 becomes endemic, future incidence, including patterns of infection, are highly uncertain. Vaccine effectiveness of Fall 2023 vaccines is unknown, and it is unclear when a new variant that evades natural or vaccine immunity will emerge. Despite these limitations, the Moderna Fall 2023 vaccine can be considered cost-effective relative to no vaccine.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".