The clinical and economic costs associated with regional disparities in varicella vaccine coverage in Italy over 50 years (2020–2070)
Bibliographic record
Abstract
Italy implemented two-dose universal varicella vaccination (UVV) regionally from 2003 to 2013 and nationally from 2017 onwards. Our objective was to analyze regional disparities in varicella outcomes resulting from disparities in vaccine coverage rates (VCRs) projected over a 50-year time-horizon (2020-2070). A previously published dynamic transmission model was updated to quantify the potential public health impact of the UVV program in Italy at the national and regional levels. Four 2-dose vaccine strategies utilizing monovalent (V) and quadrivalent (MMRV) vaccines were evaluated for each region: (A) MMRV-MSD/MMRV-MSD, (B) MMRV-GSK/MMRV-GSK, (C) V-MSD/MMRV-MSD, and (D) V-GSK/MMRV-GSK. Costs were reported in 2022 Euros. Costs and quality-adjusted life-years (QALYs) were discounted 3% annually. Under strategy A, the three regions with the lowest first-dose VCR reported increased varicella cases (+ 34.3%), hospitalizations (+ 20.0%), QALYs lost (+ 5.9%), payer costs (+ 22.2%), and societal costs (+ 14.6%) over the 50-year time-horizon compared to the three regions with highest first-dose VCR. Regions with low first-dose VCR were more sensitive to changes in VCR than high first-dose VCR regions. Results with respect to second-dose VCR were qualitatively similar, although smaller in magnitude. Results were similar across all vaccine strategies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.002 | 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".