Estimating Chronic Hepatitis B Prevalence and Undiagnosed Proportion in Canada, 2007-2021: Mathematical Framework Development (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic hepatitis B (CHB) remains a significant health burden for at least a hundred thousand Canadians. The government of Canada has endorsed the Global Strategy to eliminate hepatitis as a public health threat by 2030, but effectively targeting public health interventions is complicated by the silent nature of the disease, which can remain asymptomatic for decades. OBJECTIVE This study develops a framework to estimate the prevalence of chronic hepatitis B and the proportion of the infected population that remains undiagnosed. We apply the proposed method to national data from Canada, from 2007 to 2021. METHODS We infer the prevalence and undiagnosed proportion of CHB by fitting a mathematical state-transition model, based on the natural history of chronic hepatitis B, to observed CHB-related events. Data for the calibration were obtained from the Public Health Agency of Canada and Statistics Canada. RESULTS We estimate the national prevalence of CHB in Canada in 2021 to be 0.483% (95% CI: 0.443% - 0.535%). The corresponding percentage of undiagnosed cases was estimated to be 54.3% (95% CI: 50.9% - 58.9%). CONCLUSIONS The estimates of CHB prevalence obtained via our method are in line with previous estimates obtained from national seroprevalence studies. More specialized estimates, stratified by province or age cohort, may be achievable with detailed health administrative data. CLINICALTRIAL
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".