Department of Internal Medicine, Section of Infectious Diseases, AIDS Program, Yale School of Medicine, New Haven, CT, USA.
Institute for Community Research, Hartford, CT, USA.
Lancet Infect Dis. 2018 Feb;18(2):215-224. doi: 10.1016/S1473-3099(17)30676-X. Epub 2017 Nov 15.
Chronic infections with hepatitis C virus (HCV) and HIV are highly prevalent in the USA and concentrated in people who inject drugs. Treatment as prevention with highly effective new direct-acting antivirals is a prospective HCV elimination strategy. We used network-based modelling to analyse the effect of this strategy in HCV-infected people who inject drugs in a US city.
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.
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.
Successful HCV treatment as prevention should incorporate the baseline HCV prevalence and will achieve the greatest benefit when coverage is sufficiently expanded.
National Institute on Drug Abuse.
慢性丙型肝炎病毒（HCV）和 HIV 感染在美国十分普遍，主要集中在注射毒品的人群中。使用高效新型直接作用抗病毒药物的治疗即预防策略是 HCV 消除的一种前瞻性策略。我们使用基于网络的模型分析了该策略在一个美国城市感染 HCV 的注射毒品者中的效果。
使用来自美国康涅狄格州哈特福德市的 1574 名注射毒品者的数据拟合了五个图模型。我们使用基于拟合优度的校正度随机块模型来构建注射吸毒者网络模型。我们通过改变 HCV 治疗覆盖率（0%、3%、6%、12%或 24%）和注射吸毒者的 HCV 基线流行率（30%、60%、75%或 85%），模拟 HCV 和 HIV 通过该网络的传播。我们比较了七种治疗即预防策略在 10 年和 20 年内降低 HCV 流行率的效果，与不治疗相比。这些策略包括分配给随机选择的注射毒品者或具有最多注射伙伴的个体的治疗。其他策略探讨了治疗选定个体的所有、一半或无注射伙伴的效果，以及基于应答者驱动的治疗招募的策略。
我们的模型估计表明，在注射吸毒者的 HCV 基线流行率最高（85%）的情况下，任何治疗即预防策略的治疗覆盖率的扩大都不会显著降低 HCV 的流行率。然而，当 HCV 基线流行率为 60%或更低时，每年每 1000 名注射毒品者中治疗 120 人（12%）以上可能会在 10 年内消除 HCV。平均而言，将治疗随机分配给注射毒品者比针对具有最多注射伙伴的个体要好。治疗更多网络成员的治疗即预防策略是表现最好的策略之一，并且可以增强针对网络中程度（即，最高注射伙伴数量）的不太有效的策略。
成功的 HCV 治疗即预防应结合 HCV 的基线流行率，并且当覆盖率得到充分扩大时将获得最大的收益。
美国国立药物滥用研究所。
