Can hepatitis C elimination targets be sustained among people who inject drugs post-2030?
Bibliographic record
Abstract
BACKGROUND: In high-income countries, people who inject drugs (PWID) are a priority population for eliminating hepatitis C virus (HCV) by 2030. Despite evidence informing micro-elimination strategies, little is known regarding efforts needed to maintain elimination targets in populations with ongoing acquisition risks. This model-based study investigates post-elimination transmission dynamics of HCV and HIV among PWID under different scenarios where harm reduction interventions and HCV testing and treatment are scaled-down. METHODS: We calibrated a dynamic compartmental model of concurrent HCV and HIV transmission among PWID in Montréal (Canada) to epidemiological data (2003-2018). We then simulated achieving the World Health Organization elimination targets by 2030. Finally, we assessed the impact of four post-elimination scenarios (2030-2050): 1) scaling-down testing, treatment, opioid agonist therapy (OAT), and needle and syringe programs (NSP) to pre-2020 levels; 2) only scaling-down testing and treatment; 3) suspending testing and treatment, while scaling down OAT and NSP to pre-2020 levels; 4) suspending testing and treatment and maintaining OAT and NSP coverage required for elimination. RESULTS: Scaling down interventions to pre-2020 levels (scenario 1) leads to a modest rebound in chronic HCV incidence from 2.4 to 3.6 per 100 person-years by 2050 (95% credible interval - CrI: 0.8-7.2). When only scaling down testing and treatment (scenario 2), chronic HCV incidence continues to decrease. In scenario 3 (suspending treatment and scaling down OAT and NSP), HCV incidence and mortality rapidly increase to 11.4 per 100 person-years (95%CrI: 7.4-15.5) and 3.2 per 1000 person-years (95%CrI: 2.4-4.0), respectively. HCV resurgence was mitigated in scenario 4 (maintaining OAT and NSP) as compared to scenario 3. All scenarios lead to decreases in the proportion of reinfections among incident cases and have little impact on HIV incidence and HIV-HCV coinfection prevalence. CONCLUSION: Despite ongoing transmission risks, HCV incidence rebounds slowly after 2030 under pre-2020 testing and treatment levels. This is heightened by maintaining high-coverage harm reduction interventions. Overall, sustaining elimination would require considerably less effort than achieving it.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".