Cost Effectiveness of Implementing a Universal Birth Hepatitis B Vaccination Program in Ontario
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The World Health Organization recommends a universal hepatitis B vaccination within the first 24 h of birth. However, hepatitis B vaccines are given during adolescence in many jurisdictions including in Ontario, Canada. The objective of this study was to assess the cost effectiveness of shifting the hepatitis B vaccination timing from adolescence to birth. METHODS: A state-transition model of 18 health states representing the natural history of acute and chronic hepatitis B was developed to conduct a cost-utility analysis. Most input parameters were obtained from the Canadian literature or publicly available provincial data. The model followed a lifetime model time horizon with health outcomes and costs being discounted at 1.5% annually. Deterministic and probabilistic sensitivity analyses were performed to test the robustness of the model. Analyses were conducted from a public-payer perspective with all costs adjusted to 2021 Canadian dollars. RESULTS: Hepatitis B vaccination in newborns dominated the current strategy of adolescent vaccination. The probabilistic analysis showed that the newborn strategy was cost effective in 100% of the iterations at a willingness-to-pay threshold of $50,000/quality-adjusted life-year and cost saving in 79.39% of the iterations. A microsimulation projected that a newborn vaccination may lead to reductions in cases by 16.1% in acute hepatitis B, 43.2% in chronic hepatitis B, 48.2% in hepatocellular carcinoma, and 51.9% in hepatitis B liver-related death. CONCLUSIONS: Our analysis suggests that changing the age of the hepatitis B vaccination recommendation from adolescent to newborn is cost effective and mostly a cost-saving strategy. Newborn vaccination may lead to cost and health benefits while aligning with best available evidence and guidance from the World Health Organization.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".