Updated Public Health Impact and Cost Effectiveness of Recombinant Zoster Vaccine in Canadian Adults Aged 50 Years and Older
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to update previously estimated public health impact and cost effectiveness of recombinant zoster vaccine (RZV) for the prevention of herpes zoster (HZ) in Canadians aged ≥50 years using longer-term RZV efficacy and waning data and real-world coverage and completion. METHODS: A multicohort Markov model was used to conduct a cost-utility analysis comparing RZV with no HZ vaccination among Canadians aged ≥50 years. Real-world data were used for first-dose coverage (17.5%) and second-dose completion (65%). Vaccine efficacy and waning data were applied from up to 8-year follow-up from the ZOE-50 and ZOE-70 clinical trials. Incremental costs and benefits were calculated using a lifetime horizon from the healthcare payer (base case) and societal perspectives. A discount rate of 1.5% was applied to costs and quality-adjusted life-years (QALYs). RESULTS: The model estimated that RZV would prevent 303,835 HZ cases, 83,256 post-herpetic neuralgia (PHN) cases, 39,653 other complications, and 99 HZ-related deaths compared with no HZ vaccination. Incremental cost-effectiveness ratios (ICERs) were estimated to be $27,486 and $22,097 per QALY (2022 Canadian dollars [CAN$]) from the healthcare payer and societal perspectives, respectively. The base-case ICER was most sensitive to a lower percentage of initial HZ cases with PHN. Almost all probabilistic sensitivity analysis simulations (98.1%) resulted in ICERs
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".