METHODS: Five graph models were fit using data from 1574 people who inject drugs in Hartford, CT, USA. We used a degree-corrected stochastic block model, based on goodness-of-fit, to model networks of injection drug users. We simulated transmission of HCV and HIV through this network with varying levels of HCV treatment coverage (0%, 3%, 6%, 12%, or 24%) and varying baseline HCV prevalence in people who inject drugs (30%, 60%, 75%, or 85%). We compared the effectiveness of seven treatment-as-prevention strategies on reducing HCV prevalence over 10 years and 20 years versus no treatment. The strategies consisted of treatment assigned to either a randomly chosen individual who injects drugs or to an individual with the highest number of injection partners. Additional strategies explored the effects of treating either none, half, or all of the injection partners of the selected individual, as well as a strategy based on respondent-driven recruitment into treatment.

FINDINGS: Our model estimates show that at the highest baseline HCV prevalence in people who inject drugs (85%), expansion of treatment coverage does not substantially reduce HCV prevalence for any treatment-as-prevention strategy. However, when baseline HCV prevalence is 60% or lower, treating more than 120 (12%) individuals per 1000 people who inject drugs per year would probably eliminate HCV within 10 years. On average, assigning treatment randomly to individuals who inject drugs is better than targeting individuals with the most injection partners. Treatment-as-prevention strategies that treat additional network members are among the best performing strategies and can enhance less effective strategies that target the degree (ie, the highest number of injection partners) within the network.

INTERPRETATION: Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.

METHODS: Opioid dependent PWID were interviewed and tested for HIV and HCV in five Ukrainian cities from January 2014 to March 2015. Logistic regression was used to examine the independent correlates of two cascade steps: a) anti-HCV positive status awareness; b) chronic HCV confirmation; and of c) annual HCV testing for PWID.

RESULTS: Among 1613 PWID, 1002 (62.1%) had anti-HCV positive test result, of which 568 (56.7%) were aware of it before the study and 346 (34.5%) reported previous confirmatory testing for chronic HCV. Independent correlates of being aware they had anti-HCV positivity included: current [AOR: 3.08; 95%CI: 2.16-4.40] or prior [AOR: 1.85; 95%CI: 1.27-2.68] opioid agonistic treatment (OAT) experience, relative to no prior OAT, living in Lviv [AOR: 0.50; 95%CI: 0.31-0.81] or Odesa [AOR: 2.73; 95%CI: 1.51-4.93] relative to Kyiv and being aware of having HIV [AOR: 4.10; 95%CI: 2.99-5.62]. Independent correlates of confirming HCV infection among those who were aware of their anti-HCV positive status included: current OAT [AOR: 2.00; 95%CI: 1.24-3.23], relative to prior OAT, the middle income category [AOR: 1.74, 95%CI: 1.15-2.63], relative to the lowest, and receiving ART [AOR: 4.54; 95%CI: 2.85-7.23]. Among 1613 PWID, 918 (56.9%) were either HCV negative or not aware of their HCV positive status, of which 198 (21.6%) reported recent anti-HCV test (during last 12 month). Recent anti-HCV test in this group was associated with current [AOR: 7.17; 95%CI: 4.63-11.13] or prior [AOR: 2.24; 95%CI: 1.32-3.81] OAT experience, relative to no prior OAT.

METHODS: Patients testing HBs antigen (Ag) or HCV antibody (Ab) positive within enrollment into TAHOD were considered HBV or HCV co-infected. Factors associated with HBV and/or HCV co-infection were assessed by logistic regression models. Factors associated with post-ART HIV immunological response (CD4 change after six months) and virological response (HIV RNA <400 copies/ml after 12 months) were also determined. Survival was assessed by the Kaplan-Meier method and log rank test.

RESULTS: A total of 7,455 subjects were recruited by December 2012. Of patients tested, 591/5656 (10.4%) were HBsAg positive, 794/5215 (15.2%) were HCVAb positive, and 88/4966 (1.8%) were positive for both markers. In multivariate analysis, HCV co-infection, age, route of HIV infection, baseline CD4 count, baseline HIV RNA, and HIV-1 subtype were associated with immunological recovery. Age, route of HIV infection, baseline CD4 count, baseline HIV RNA, ART regimen, prior ART and HIV-1 subtype, but not HBV or HCV co-infection, affected HIV RNA suppression. Risk factors affecting mortality included HCV co-infection, age, CDC stage, baseline CD4 count, baseline HIV RNA and prior mono/dual ART. Shortest survival was seen in subjects who were both HBV- and HCV-positive.

METHODS: An age-structured multi-state Markov model was developed to simulate the natural history of HCV infection. We tested three historical incidence scenarios that would give rise to the estimated prevalence in 2009, and calculated the incidence of cirrhosis, end-stage liver disease, and death, and disability-adjusted life-years (DALYs) under each scenario, to the year 2039. In the baseline scenario, current antiviral treatment levels were extended from 2014 to the end of the simulation period. To estimate the disease burden averted under current sustained virological response rates and treatment levels, the baseline scenario was compared to a counterfactual scenario in which no past or future treatment is assumed.

RESULTS: In the baseline scenario, the projected disease burden for the year 2039 is 94,900 DALYs/year (95% credible interval (CrI): 77,100 to 124,500), with 2,002 (95% CrI: 1340 to 3040) and 540 (95% CrI: 251 to 1,030) individuals predicted to develop decompensated cirrhosis and hepatocellular carcinoma, respectively, in that year. Although current treatment practice is estimated to avert a cumulative total of 2,200 deaths from DC or HCC, a cumulative total of 63,900 HCV-related deaths is projected by 2039.