Cost-effectiveness modelling of birth and infant dose vaccination against hepatitis B virus in Ontario from 2020 to 2050
Bibliographic record
Abstract
BACKGROUND: The World Health Organization recommends universal birth dose vaccination for hepatitis B virus (HBV), yet only 3 provinces and territories in Canada provide birth dose vaccination, and Canadian-born children in Ontario are acquiring HBV before adolescent vaccination. We sought to determine whether birth and/or infant HBV vaccination is cost-effective. METHODS: We used a dynamic HBV model that incorporates population by year, disease stage, sex and the influence of immigration to quantify the disease and economic burden of chronic HBV infection in Ontario from 2020 to 2050. We compared 4 vaccination scenarios, which included a birth dose vaccine and variations of the 2 subsequent doses (either alone or as a part of the hexavalent vaccine) and a hexavalent-only strategy in infancy with the current adolescent vaccination strategy. Our costing estimates were based on values from 2020. RESULTS: All 4 infant vaccination approaches prevented an additional 550-560 acute and 160 chronic pediatric HBV infections from 2020 to 2050 compared with adolescent vaccination. Whereas birth dose could be cost-effective, incorporating vaccination into a hexavalent vaccine was cost saving. By 2050, the hexavalent approach led to $428 000 in cost savings per disability-adjusted life years averted. INTERPRETATION: At the current prevalence in Ontario, a switch to birth dose or infant dose will be cost-effective or even cost saving. Introducing any form of infant HBV immunization in Ontario will prevent acute and chronic pediatric HBV infections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".