Epidemiology and natural history of chronic Hepatitis B in the Canadian province of Alberta from 2012 to 2021: A population-based study
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: There are limited recent data on the burden of chronic hepatitis B (CHB) in the North American general population. We aimed to identify the CHB burden from a Canadian population-based perspective. PATIENTS AND METHODS: Using a retrospective cohort design, we searched Alberta Analytics administrative databases including the Provincial Laboratory database, to describe CHB epidemiology and natural history in Alberta, Canada between fiscal years 2012-2020. We analyzed incidence and prevalence trends using a Poisson regression model and conducted Kaplan-Meier analyses to examine the incident cohort's survival. RESULTS: The age/sex-adjusted incidence of CHB between 2015-2020 was 27.1/100,000 person/years (29.6/100,000 in males and 24.5/100,000 in females) and was highest among individuals aged 45-64 years. Despite a decrease in annual incidence of CHB from 36.4 to 13.4/100,000 between 2015-2020, prevalence increased from 98.9 to 210.3/100,000 in the same period. Of 6,860 incident cases, 2.1% died, and 0.2% underwent liver transplantation during a median follow-up of 3.6 years (interquartile range 2.0-4.9 years). CHB patients had significantly lower survival rates compared to age/sex-matched Canadians, with a standardized mortality ratio of 3.9 (95% confidence interval [CI] 3.3-4.6). Male sex (hazard ratio [HR] 1.7; 95% CI 1.2-2.5), older age at diagnosis (HR, 1.08; 95% CI 1.07-1.09) independently predicted mortality. CONCLUSIONS: CHB incidence decreased in Alberta, which is consistent with nationwide trends. Males and individuals aged 45-64 had higher CHB incidence and prevalence. CHB patients' lower survival rates emphasize the need to address barriers to guideline recommended HBV care linkage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".