Cost-effectiveness analysis of implementing 20-valent pneumococcal conjugate vaccine into the Romanian pediatric national immunization program
Bibliographic record
Abstract
INTRODUCTION: Despite the inclusion of pneumococcal conjugate vaccines (PCV) in the pediatric national immunization program (NIP) since 2017, Romania continues to face a substantial clinical, economic, and societal burden of pneumococcal disease. Higher-valent vaccines, such as 20-valent PCV (PCV20), offer broader serotype coverage versus the current standard of care (13-valent PCV; PCV13) with the potential to reduce disease burden. To test this, we conducted a cost-effectiveness analysis of switching from PCV13 or a potential future comparator (15-valent PCV; PCV15), both under a 2 + 1 schedule, to PCV20 under a 3 + 1 schedule in the Romanian pediatric NIP. METHODS: A population-based, multi-cohort Markov model with a target population of children aged <2 years was utilized to estimate the cost and health impact of PCV20 versus lower-valent comparators over 10 years. The model adopted a Romanian societal perspective, encompassing both direct and indirect costs, with an annual cycle. Sensitivity and scenario analyses were conducted to assess the robustness of the model and its assumptions. RESULTS: In the base-case analysis, PCV20 demonstrated dominance versus PCV13 and PCV15 (i.e. was more effective and less costly), with total predicted cost-savings of 79,123,267 and 206,623,098 Romanian Leu, respectively, plus reduction in pneumococcal disease cases by 246,245 and 223,914, respectively. The majority of sensitivity and scenario analyses of both pairwise comparisons were aligned with the base case. CONCLUSION: The results of this analysis indicate that PCV20 implementation into the Romanian pediatric NIP would greatly reduce pneumococcal disease burden and would be a cost-effective approach versus PCV13 or PCV15 from a societal perspective over 10 years.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.005 | 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".