Public health impact and return on investment of the pediatric National Immunization Program in Italy
Bibliographic record
Abstract
Objectives We conducted a cost-benefit analysis of the pediatric National Immunization Program (NIP) in Italy.Methods An economic model evaluated the benefit-cost ratio (BCR) of the Italian pediatric NIP, including 10 pathogens for mandatory vaccines and 4 pathogens for recommended vaccines for children aged 0-10 years from the healthcare-sector and societal perspectives. Separate decision trees were used to model each vaccine-preventable disease (VPD). The 2020 birth cohort (n = 420,084) was followed over their lifetime; the model projected and compared discounted disease cases, life-years, quality-adjusted life-years (QALYs), and costs (2021 euros) with and without immunization (based on current and pre – vaccine era disease incidence estimates, respectively).Results The pediatric NIP was estimated to prevent 1.8 million cases of VPDs and 3,330 deaths, resulting in 45,900 fewer life-years lost and 57,000 fewer QALYs lost. Vaccination costs of €285 million were offset by disease cost savings of €1.6 billion, resulting in a BCR of 5.6 from a societal perspective (BCR = 1.7 from a healthcare-sector perspective). When QALYs gained were valued, the BCR increased to 15.6.Conclusions The benefits of the Italian pediatric NIP, including averted disease-related morbidity, mortality, and associated costs, highlight the value of continued investment in pediatric immunization.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".