Modeling the effects of improving varicella vaccination coverage on clinical and economic outcomes in Peru
Bibliographic record
Abstract
Despite the implementation of a single-dose universal varicella vaccination program in Peru since 2018, vaccination coverage rates (VCRs) remain low, with a VCR of 66% as of 2022. We employed a dynamic transmission model (DTM) to evaluate the impact of increasing varicella VCRs in Peru. We parameterized a previously published DTM with publicly available demographic, healthcare resource use, cost, and epidemiological data inputs specific to Peru (or suitable regional proxy), including Peruvian varicella VCRs up to 2022. We modeled six single-dose UVV strategies over 10 years (2023-2032) that increased VCRs to 80-90% over 1-, 2- or 5-year periods, compared with the reference strategy assuming the continuation of the current VCR of 65.6%. Clinical and economic outcomes were reported; economic outcomes were reported in 2023 USD with 5% annual discounting. Parameter uncertainty was evaluated through probabilistic and deterministic sensitivity analyses. All six strategies with increased VCR resulted in 13%-25% fewer varicella cases, and 13%-24% fewer outpatient and inpatient cases, over 10 years, compared to continuing the current varicella VCR, with shorter VCR ramp-up periods resulting in more clinical outcomes averted. However, this led to a 12%-21% ($0.05-0.08 per person per year) increase in costs from the payer perspective. The PSA indicated that model results were robust to parameter uncertainty. Increasing varicella VCR led to improved clinical outcomes, with small increase in costs. Improving VCR at faster rates leads to better clinical outcomes relative to the reference strategy at a small per-capita cost increase.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".