Model-Based Estimation of the Prevalence of Chronic Hepatitis B in Canada and Cost-Effectiveness Analysis of Implementing a Universal Birth Hepatitis B Vaccination Program in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND Hepatitis B is a potentially life-threatening acute or chronic disease that is caused by hepatitis B virus (HBV). The development of chronic disease is age-dependent, with the highest risk in the infant population. As such, the World Health Organization recommends universal hepatitis B vaccination within the first 24 hours of birth. Yet, hepatitis B vaccination is provided at age 12 years in Ontario, Canada. Chronic hepatitis B (CHB) is also characterized by the lack of symptoms until progression to end-stage liver disease, making it difficult identify infected patients early in the disease. This has resulted in uncertainties regarding the true prevalence and the impact of CHB in Canada. \n \nOBJECTIVES The objectives of this thesis are to: 1) assess the cost-effectiveness of implementing a universal hepatitis B vaccination program in newborns versus adolescents in Ontario, and 2) estimate the true prevalence of CHB and the proportion of undiagnosed cases in Canada. \n \nMETHODOLOGY Two models were developed to achieve the study objectives. First, a state-transition model representing the natural history of acute and chronic hepatitis B was developed in TreeAge Pro to assess the cost-effectiveness of two hepatitis B vaccine schedules. Analyses were performed from a public payer perspective with a lifetime time horizon and a 1.5% annual discount rate. Second, a modified version of the natural history model was adopted to create a prevalence model in MATLAB to estimate the prevalence of CHB using Markov Chain Monte Carlo. Model input data were obtained from peer-reviewed literature and publicly available databases from Statistics Canada and Public Health Agency of Canada. \n \nRESULTS Birth vaccination was found to be cost-saving compared to the current adolescent vaccination strategy in Ontario. Probabilistic analysis resulted in a mean cost of $317,261 and 43.36 QALYs for birth vaccination versus $317,735 and 43.18 QALYs for adolescent vaccination. A microsimulation showed that the birth vaccination strategy leads to decreases in liver-related cases by 15.96% in acute hepatitis B, 44.27% in CHB, 47.45% in compensated cirrhosis, 47.54% in hepatocellular carcinoma, 56.44% in decompensated cirrhosis, 50.00% in liver transplant, and 51.16% in liver death. \nIn Canada, the model estimated both the prevalence of CHB and proportion of undiagnosed cases to have trended downwards in the total population from 2011 to 2017. Overall, when all age cohorts were combined, CHB prevalence was estimated to be 0.85% and the undiagnosed proportion was estimated to be 32.77%. The model-generated estimate for CHB prevalence of 0.85% was approximately doubled the previously estimated seroprevalence of 0.4% from a national seroprevalence study. \n \nCONCLUSION The results of the study indicate that by switching to a birth hepatitis B vaccination program, the Ontario government can save healthcare spendings while increasing clinical benefits. The results of the study provide policy makers with actionable recommendations on re-assessing the current hepatitis B vaccination schedule in Ontario. The second model also showed that the prevalence of CHB may be much higher than previously estimated and that a significant proportion of patients remain undiagnosed. The prevalence model demonstrates a feasible framework for future analyses using administrative databases to more accurately identify the true burden of CHB in Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".