Use of Cost-Effectiveness Analyses for Decisions About Vaccination Programs for Meningococcal Disease in the United States, United Kingdom, The Netherlands, and Canada
Bibliographic record
Abstract
INTRODUCTION: Meningococcal vaccines to protect against invasive meningococcal disease (IMD) vary in terms of vaccine technology and serogroup coverage (Polysaccharide MnACWY, conjugated C and ACWY, outer membrane vesicle-based or protein-based B vaccines), and the national recommendations for each of them vary in terms of target population and number of doses. We sought to understand factors associated with the evolution of meningococcal vaccination program recommendations in four countries with formal evaluation processes: the UK, US, the Netherlands, and Canada. AREAS COVERED: A targeted review of published literature and internet sources for the four countries relating to meningococcal vaccination decision-making was conducted. The review focused on the impact of cost-effectiveness analyses on vaccine policy decisions and the extent to which variation in incidence of IMD and its potential catastrophic consequences influenced policy decisions.The evolution of meningococcal vaccine recommendations in the four countries was mainly driven by changes in vaccine availability and changes in serogroup incidence. Public pressure due to the catastrophic nature of IMD influenced recommendations. The role of cost-effectiveness analyses varied across the 4 countries. EXPERT OPINION: The value of implementing meningococcal vaccination programs should be assessed using factors beyond those included in traditional cost-effectiveness analyses.
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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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".