Number of people treated for hepatitis C virus infection in 2014-2023 and applicable lessons for new HBV and HDV therapies
Bibliographic record
Abstract
BACKGROUND & AIMS: The year 2023 marked the 10-year anniversary of the launch of direct-acting antivirals (DAAs) for the treatment of hepatitis C virus (HCV). Monitoring HCV treatment trends by country, region, and globally is important to assess progress toward the World Health Organization's 2030 elimination targets. Additionally, historical patterns can help predict the uptake of future therapies for other liver diseases. METHODS: The number of people living with HCV (PLHCV) treated between 2014-2023 across 119 countries was estimated using national HCV registries, reported DAA sales data, pharmaceutical companies' reports, and estimates provided by national experts. For the countries with no available data, the average estimate of the corresponding Global Burden of Disease region was used. RESULTS: An estimated 13,816,000 (95% uncertainty intervals: 13,221,000-16,415,000) PLHCV were treated, of whom 12,748,000 (12,226,000-15,231,000) were treated with DAAs, of which 11,081,000 (10,542,000-13,338,000) were sofosbuvir-based DAA regimens. Country-level data accounted for 97% of these estimates. In high-income countries, there was a 41% drop in treatment from its peak, and reimbursement was a large predictor of treatment. In low- and middle-income countries, price played an important role in expanding treatment access through the public and private markets, and treatment continues to increase slowly after a sharp drop at the end of the Egyptian national program. CONCLUSIONS: In the last 10 years, 21% of all HCV infections were treated with DAAs. Regional and temporal variations highlight the importance of active screening strategies. Without program enhancements, the number of treated PLHCV stalled in every country/region, which may not reflect a lower prevalence but may instead reflect the diminishing returns of existing strategies. IMPACT AND IMPLICATIONS: Long-term hepatitis C virus (HCV) infection can lead to cirrhosis and liver cancer. Since 2014, these infections can be effectively treated with 8-12 weeks of oral therapies. In 2015, the World Health Organization established targets to eliminate HCV by 2030, which included treatment targets for member countries. The current study examines HCV treatment patterns across 119 countries and regions from 2014 to 2023 to assess the impact of national programs. This study can assist physicians and policymakers in understanding treatment patterns within similar regions or income groups and in utilizing historical data to refine their strategies in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".