Substantial reduction in the clinical and economic burden of disease following variant-adapted mRNA COVID-19 vaccines in immunocompromised patients in France
Bibliographic record
Abstract
ABSTRACT An economic evaluation was conducted to predict the economic and clinical burden of vaccinating immunocompromised (IC) individuals aged ≥30 years with mRNA-1273 variant-adapted COVID-19 vaccines in Fall 2023 and Spring 2024 versus BNT162b2 variant-adapted vaccines in France. The number of symptomatic COVID-19 infections, hospitalizations, deaths, and long COVID cases, costs and quality-adjusted life years (QALYs) was estimated using a static decision-analytic model. Predicted vaccine effectiveness (VE) were based on real-world data from prior versions, suggesting higher protection against infection and hospitalization with mRNA-1273 vaccines. VE estimates were combined with COVID-19 incidence and probability of COVID-19 severe outcomes. Uncertainty surrounding VE, vaccine coverage, infection incidence, hospitalization and mortality rates, costs and QALYs were tested in sensitivity analyses. The mRNA-1273 variant-adapted vaccine is predicted to prevent an additional 3,882 infections, 357 hospitalizations, 81 deaths, and 326 long COVID cases when compared to BNT162b2 variant-adapted vaccines in 230,000 IC individuals. This translates to €10.1 million cost-savings from a societal perspective and 645 QALYs saved. Results were consistent across all analyses and most sensitive to variations surrounding VE and coverage. These findings highlight the importance of increasing vaccine coverage, and ability to induce higher levels of protection with mRNA-1273 formulations in this vulnerable population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".