Estimating the Clinical and Economic Impact of Switching from the 13-Valent Pneumococcal Conjugate Vaccine (PCV13) to Higher-Valent Options in Greek Infants
Bibliographic record
Abstract
In June 2010, Greece introduced the 13-valent pneumococcal conjugate vaccine (PCV13) for pediatric vaccination and has since observed a large decrease in pneumococcal disease caused by these vaccine serotypes, yet the disease prevalence of non-vaccine serotypes has increased. Two higher-valent conjugate vaccines, a 15-valent (PCV15) and a 20-valent (PCV20), were developed to improve serotype coverage and combat serotype replacement. A decision-analytic model was adapted to the Greek setting using historical pneumococcal disease trends from PCV13 to forecast future clinical and economic outcomes of higher-valent PCVs over a 10-year period (2023-2033). The model estimated outcomes related to invasive pneumococcal disease (IPD), hospitalized and non-hospitalized pneumonia, and otitis media (OM) resulting from a switch in vaccination programs to PCV15 in 2023 or switching to PCV20 in 2024. Cost-effectiveness was evaluated from the third-party payer's perspective in the Greek healthcare system. Compared to implementing PCV15 one year earlier, switching from PCV13 to PCV20 in 2024 was estimated to be a cost-saving strategy by saving the Greek health system over EUR 50 million in direct medical costs and averting over 250 IPD cases, 54,800 OM cases, 8450 pneumonia cases, and 255 deaths across all ages over a 10-year period.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".