Modelling the impact of HIV and hepatitis C virus prevention and treatment interventions among people who inject drugs in Kenya
Bibliographic record
Abstract
OBJECTIVES: People who inject drugs (PWID) in Kenya have high HIV (range across settings: 14-26%) and hepatitis C virus (HCV; 11-36%) prevalence. We evaluated the impact of existing and scaled-up interventions on HIV and HCV incidence among PWID in Kenya. DESIGN: HIV and HCV transmission model among PWID, calibrated to Nairobi and Kenya's Coastal region. METHODS: For each setting, we projected the impact (percent of HIV/HCV infections averted in 2020) of existing coverages of antiretroviral therapy (ART; 63-79%), opioid agonist therapy (OAT; 8-13%) and needle and syringe programmes (NSP; 45-61%). We then projected the impact (reduction in HIV/HCV incidence over 2021-2030), of scaling-up harm reduction [Full harm reduction ('Full HR'): 50% OAT, 75% NSP] and/or HIV (UNAIDS 90-90-90) and HCV treatment (1000 PWID over 2021-2025) and reducing sexual risk (by 25/50/75%). We estimated HCV treatment levels needed to reduce HCV incidence by 90% by 2030. RESULTS: In 2020, OAT and NSP averted 46.0-50.8% (range of medians) of HIV infections and 50.0-66.1% of HCV infections, mostly because of NSP. ART only averted 12.9-39.8% of HIV infections because of suboptimal viral suppression (28-48%). Full HR and ART could reduce HIV incidence by 51.5-64% and HCV incidence by 84.6-86.6% by 2030. Also halving sexual risk could reduce HIV incidence by 68.0-74.1%. Alongside full HR, treating 2244 PWID over 2021-2025 could reduce HCV incidence by 90% by 2030. CONCLUSION: Existing interventions are having substantial impact on HIV and HCV transmission in Kenya. However, to eliminate HIV and HCV, further scale-up is needed with reductions in sexual risk and HCV treatment.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".