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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".