Cost-effectiveness of vaccination against meningococcal B among Dutch infants
Bibliographic record
Abstract
OBJECTIVE: Recently, a vaccine with the capacity to protect against serogroup B meningococcal (MenB) disease received a positive opinion of the European Medicines Agency. Previously, such a vaccine was estimated to be cost-effective. However, since then, the MenB disease incidence has declined drastically in the Netherlands. Therefore, we re-assessed the potential incremental cost-effectiveness ratio (ICER) of vaccinating infants in the Netherlands with a MenB vaccine. RESULTS: Routine infant vaccination (2, 3, 4+11 mo) could prevent 39 cases of MenB disease in a single birth cohort, corresponding to a total gain of 133 quality-adjusted life years (QALYs). However, this strategy is unlikely to be cost-effective if the vaccine costs €40 per dose (€243,778 per QALY). At a disease incidence of 5.7 per 100,000 person-years or a vaccine price of €10 per dose including administration costs, the ICER becomes more acceptable and remains below a threshold of €50,000 per QALY. METHODS: A cohort of 185,000 Dutch newborns was followed in a Markov model to compare routine vaccination against MenB disease with no vaccination. The ICER was estimated for different disease incidences. The study was performed from a societal perspective. CONCLUSIONS: At the current low level of disease incidence, introduction of routine infant vaccination, following a 2, 3, 4+11 mo schedule, against MenB disease is unlikely cost-effective in the Netherlands. If the MenB disease incidence increases or the vaccine price is substantially lower than €40, routine infant vaccination has the potential to be cost-effective.
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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 | 0.010 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".