Clinical impact and cost-effectiveness of the updated COVID-19 mRNA Autumn 2023 vaccines in Germany
Bibliographic record
Abstract
ABSTRACT Objectives To assess the potential clinical impact and cost-effectiveness of coronavirus disease 2019 (COVID-19) mRNA vaccines updated for Autumn 2023 in adults aged ≥60 years and high-risk persons aged 30-59 years in Germany over a 1-year analytic time horizon (September 2023--August 2024). Methods A compartmental Susceptible-Exposed-Infected-Recovered model was updated and adapted to the German market. Numbers of symptomatic infections, number of COVID-19 related hospitalisations and deaths, costs, and quality-adjusted life-years (QALYs) gained were calculated using a decision tree model. The incremental cost-effectiveness ratio of an Autumn 2023 Moderna updated COVID-19 (mRNA-1273.815) vaccine was compared to no additional vaccination. Potential differences between the mRNA-1273.815 and the Autumn Pfizer-BioNTech updated COVID-19 (XBB.1.5 BNT162b2) vaccines, as well as societal return on investment for the mRNA-1273.815 vaccine relative to no vaccination, were also examined. Results Compared to no Autumn vaccination, the mRNA-1273.815 campaign is predicted to prevent approximately 1,697,900 symptomatic infections, 85,400 hospitalisations, and 4,100 deaths. Compared to an XBB.1.5 BNT162b2 campaign, the mRNA-1273.815 campaign is also predicted to prevent approximately 90,100 symptomatic infections, 3,500 hospitalisations, and 160 deaths. Across both analyses we found the mRNA-1273.815 campaign to be dominant. Conclusions The mRNA-1273.815 vaccine can be considered cost-effective relative to the XBB.1.5 BNT162b2 vaccine and highly likely to provide more benefits and save costs compared to no vaccine in Germany, and to offer high societal return on investment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".