Progress towards hepatitis C virus elimination in high‐income countries: An updated analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: Elimination of HCV by 2030, as defined by the World Health Organization (WHO), is attainable with the availability of highly efficacious therapies. This study reports progress made in the timing of HCV elimination in 45 high-income countries between 2017 and 2019. METHODS: Disease progression models of HCV infection for each country were updated with latest data on chronic HCV prevalence, and annual diagnosis and treatment levels, assumed to remain constant in the future. Modelled outcomes were analysed to determine the year in which each country would meet the WHO 2030 elimination targets. RESULTS: Of the 45 countries studied, 11 (Australia, Canada, France, Germany, Iceland, Italy, Japan, Spain, Sweden, Switzerland, and United Kingdom) are on track to meet WHO's elimination targets by 2030; five (Austria, Malta, Netherlands, New Zealand, and South Korea) by 2040; and two (Saudi Arabia and Taiwan) by 2050. The remaining 27 countries are not expected to achieve elimination before 2050. Compared to progress in 2017, South Korea is no longer on track to eliminate HCV by 2030, three (Canada, Germany, and Sweden) are now on track, and most countries (30) saw no change. CONCLUSIONS: Assuming high-income countries will maintain current levels of diagnosis and treatment, only 24% are on track to eliminate HCV by 2030, and 60% are off track by at least 20 years. If current levels of diagnosis and treatment continue falling, achieving WHO's 2030 targets will be more challenging. With less than ten years remaining, screening and treatment expansion is crucial to meet WHO's HCV elimination targets.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".