Cost–benefit analysis of the National Immunization Program in Spain
Bibliographic record
Abstract
Broad benefits of vaccination programs are well acknowledged but difficult to measure, especially when considering all vaccines included in a National Immunization Program (NIP). The aim was to conduct a cost-benefit analysis of the entire NIP in Spain, and an expanded NIP including four potential additional programs. A cost-benefit analysis was performed in Excel to assess the economic and health benefits (€) of vaccinating a single cohort of newborns over a lifetime horizon compared to no vaccination, from a societal perspective: firstly, according to the 2020 NIP in Spain (including 2021 recommendation for herpes zoster in 65-year-olds); and secondly, with an expanded NIP (adding rotavirus and meningococcal B in infants, and pertussis booster in adults aged >65 years and herpes zoster in all adults >50 years). The main inputs were taken from published literature and Spanish databases. Results were presented as a benefit-cost ratio (economic benefit per €1 invested). A cohort of 343,126 newborns were included in the analysis. The total investment needed to vaccinate the cohort throughout their lifetime, according to the 2020 NIP and the expanded NIP, was estimated at €168.5 million and €275.5 million, respectively. Potential economic benefits were €772.2 million and €803.0 million, respectively. The societal benefit-cost ratio was €4.58 and €2.91 per €1 invested, respectively. Even with the addition of new vaccination programs, the Spanish NIP yielded positive benefit-cost ratios from the societal perspective, demonstrating that NIPs spanning the full life course are an efficient public health measure.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.006 | 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".