Measuring hepatitis C virus elimination as a public health threat: Beyond global targets
Bibliographic record
Abstract
An increasing number of countries are committing to meet the World Health Organization (WHO) targets to eliminate hepatitis C virus (HCV) as a public health threat by 2030. These include service coverage targets (90% diagnosed and 80% of diagnosed patients treated) and impact targets (80% and 65% reductions in incidence and mortality, respectively, compared to 2015 levels). Currently, a dozen countries are on track to reach 2030 WHO HCV targets. However, while striving for the WHO targets is important, it should be recognized that progress on impact targets is derived from mathematical models projecting decreases in incidence and mortality on a global scale. Despite HCV treatment access in many counties for a number of years, limited empirical data are available to evaluate progress towards elimination. In some countries, substantial incidence and mortality reductions based on reaching the WHO service coverage targets may be unachievable. For example, in countries with ageing hepatitis C-infected populations, even if they have a quality hepatitis C response, high hepatitis C-related morbidity at baseline may not be reversible even with increased HCV treatment uptake and diagnosis. Finally, WHO targets are not necessarily easily or reliably measurable. Measuring relative impact targets requires high-quality data at baseline (ie 2015) and longitudinal data to assess temporal trends. In this commentary, we propose alternative additional measures to track progress on reducing the HCV burden, offer examples where the WHO targets may not be informative or achievable, and potential practical solutions.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".