Feasibility of hepatitis C elimination by screening and treatment alone in high-income countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Despite the availability of highly effective direct-acting antiviral therapy, chronic hepatitis C (CHC) continues to cause a major public health burden. In many high-income countries, treatment rates have been declining, which was exacerbated by the impact of the COVID-19 pandemic, threatening the ability to meet the World Health Organization (WHO)'s targets for eliminating HCV as a public health threat by 2030. We sought to model the impact of CHC in Canada, a resource-rich country with ongoing immigration from HCV-endemic regions; which relies exclusively on risk-based screening for case identification. APPROACH AND RESULTS: We developed an agent-based model to characterize the HCV epidemic in a high-income country with ongoing immigration. Combinations of prevention such as harm reduction, screening, and treatment strategies were considered. Model parameters were estimated from the literature and calibrated against historical HCV data. Sensitivity analyses were performed to assess uncertainty. Under the current status quo of risk-based screening, we predict the incidence of CHC-induced decompensated cirrhosis, HCC, and liver-related deaths would decrease by 79.4%, 76.1%, and 62.1%, respectively, between 2015 and 2030, but CHC incidence would only decrease by 11.1%. The results were sensitive to HCV transmission rate and an annual number of people initiating treatment. CONCLUSIONS: Current risk-based screening, and subsequent treatment, will be inadequate to achieve WHO goals. With extensive scale-up in screening, and treatment, the mortality target may be achievable, but the target for preventing new CHC cases is unlikely reachable, highlighting the importance of developing enhanced harm-reduction strategies for HCV elimination.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".