The mean attributable health care costs associated with hepatitis B virus in Ontario, Canada: A matched cohort study
Bibliographic record
Abstract
Background: No Canadian studies examined the economic impact of hepatitis B virus (HBV) using population-based, patient-level data. We determined attributable costs associated with HBV from a health care payer perspective. Methods: We conducted an incidence-based, matched cohort, cost-of-illness study. We identified infected subjects (positive HBV surface antigen, DNA, or e-antigen) between 2004 and 2014, using health administrative data. The index date was the first positive specimen. The cohort was organized into three groups: no HBV-related complications, HBV-related complications before index date, and HBV-related complications post-index date. To evaluate costs (2017 Canadian dollars), we adopted the phase-of-care approach defining six phases. Mean attributable costs were determined by evaluating mean differences between matched pairs. Hard match variables were sex, age group, index year, rurality, neighbourhood income quintile, comorbidities, and immigrant status. Costs were combined with crude survival data to calculate 1-, 5-, and 10-year costs. Results: We identified 41,469 infected subjects with a mean age of 44.2 years. The majority were males (54.7%), immigrants (58.4%), and residents of major urban centres (96.8%). Eight percent had HBV-related complications before index date and 11.5% had them post index date. Across groups, mean attributable costs ranged from CAD $27-$19 for pre-diagnosis, CAD $167-$1,062 for initial care, CAD $53-$407 for continuing care, CAD $1,033 for HBV-related complications, CAD $304 for continuing care for complications, and CAD $2,552-$4,281 for final care. Mean cumulative 1-, 5-, and 10-year costs ranged between CAD $253-$3,067, $3,067-$20,349, and $6,128-$38,968, respectively. Conclusions: HBV is associated with long-term economic burden. These results support decision-making on HBV prevention and monitoring strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".