Hepatitis C Attributable Healthcare Costs and Mortality among Immigrants: A Population-Based Matched Cohort Study
Bibliographic record
Abstract
Background: Data on the economic burden of chronic hepatitis C (CHC) among immigrants are limited. Our objective was to estimate the CHC-attributable mortality and healthcare costs among immigrants in Ontario, Canada. Methods: We conducted a population-based matched cohort study among immigrants diagnosed with CHC between May 31, 2003, and December 31, 2018, using linked health administrative data. Immigrants with CHC (exposed) were matched 1 : 1 to immigrants without CHC (unexposed) using a combination of hard (index date, sex, and age) and propensity-score matching. Net costs (2020 Canadian dollars) collected from the healthcare payer perspective were calculated using a phase-of-care approach and used to estimate long-term costs adjusted for survival. Results: We matched 5,575 exposed individuals with unexposed controls, achieving a balanced match. The mean age was 47 years, and 52% was male. On average, 10.5% of exposed and 3.5% of unexposed individuals died 15 years postindex (relative risk = 2.9; 95% confidence interval (CI): 2.6-3.5). The net 30-day costs per person were $88 (95% CI: 55 to 122) for the prediagnosis, $324 (95% CI: 291 to 356) for the initial phase, $1,016 (95% CI: 900 to 1,132) for the late phase, and $975 (95% CI: -25 to 1,974) for the terminal phase. The mean net healthcare cost adjusted for survival at 15 years was $90,448. Conclusions: Compared to unexposed immigrants, immigrants infected with CHC have higher mortality rates and greater healthcare costs. These findings will support the planning of HCV elimination efforts among key risk groups in the province.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".