Cost-effectiveness analysis of 20-valent anti-pneumococcal vaccination in the Spanish pediatric population
Bibliographic record
Abstract
OBJECTIVES: We evaluated the cost-effectiveness of implementing different pneumococcal conjugate vaccines (PCV) - 20-valent (PCV20; 3 + 1), 13-valent (PCV13; 2 + 1), and 15-valent (PCV15; 2 + 1) - into the Spanish pediatric national immunization program (NIP) for pneumococcal disease prevention. METHODS: A Markov model adopting a Spanish National Healthcare System perspective and annual cycles estimated the health and cost impact of PCV20, PCV13, and PCV15 over 10 years among children. Epidemiological, cost, and utility inputs were derived from published literature and official databases; vaccine efficacy inputs were based on PCV13 clinical effectiveness and 7-valent PCV efficacy and impact studies. Sensitivity analyses evaluated model robustness. RESULTS: PCV20 implementation was predicted to reduce the pneumococcal disease burden, preventing > 1,000,000 pneumococcal disease cases and > 150 deaths, versus both comparators. The adoption of PCV20 was estimated to result in cost-savings of approximately €1 billion versus PCV13 and PCV15. PCV20 demonstrated dominance over both alternatives, with 100% of 1,000 probabilistic sensitivity analysis iterations indicating PCV20 dominance. CONCLUSION: Incorporating PCV20 3 + 1 into the Spanish pediatric NIP was predicted to be more effective at a lower cost than PCV13 2 + 1 and PCV15 2 + 1 due to its broader serotype coverage and enhanced protection against pneumococcal disease.
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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 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".