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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".