An intensive model of care for hepatitis C virus screening and treatment with direct‐acting antivirals in people who inject drugs in Nairobi, Kenya: a model‐based cost‐effectiveness analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Hepatitis C virus (HCV) treatment is essential for eliminating HCV in people who inject drugs (PWID), but has limited coverage in resource-limited settings. We measured the cost-effectiveness of a pilot HCV screening and treatment intervention using directly observed therapy among PWID attending harm reduction services in Nairobi, Kenya. DESIGN: We utilized an existing model of HIV and HCV transmission among current and former PWID in Nairobi to estimate the cost-effectiveness of screening and treatment for HCV, including prevention benefits versus no screening and treatment. The cure rate of treatment and costs for screening and treatment were estimated from intervention data, while other model parameters were derived from literature. Cost-effectiveness was evaluated over a life-time horizon from the health-care provider's perspective. One-way and probabilistic sensitivity analyses were performed. SETTING: Nairobi, Kenya. POPULATION: PWID. MEASUREMENTS: Treatment costs, incremental cost-effectiveness ratio (cost per disability-adjusted life year averted). FINDINGS: The cost per disability-adjusted life-year averted for the intervention was $975, with 92.1% of the probabilistic sensitivity analyses simulations falling below the per capita gross domestic product for Kenya ($1509; commonly used as a suitable threshold for determining whether an intervention is cost-effective). However, the intervention was not cost-effective at the opportunity cost-based cost-effectiveness threshold of $647 per disability-adjusted life-year averted. Sensitivity analyses showed that the intervention could provide more value for money by including modelled estimates for HCV disease care costs, assuming lower drug prices ($75 instead of $728 per course) and excluding directly-observed therapy costs. CONCLUSIONS: The current strategy of screening and treatment for hepatitis C virus (HCV) among people who inject drugs in Nairobi is likely to be highly cost-effective with currently available cheaper drug prices, if directly-observed therapy is not used and HCV disease care costs are accounted for.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".