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 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) | 0.000 | 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".