Hepatitis C Transmission and Treatment in Contact Networks of People Who Inject Drugs

Hepatitis C virus (HCV) chronically infects over 180 million people worldwide, with over 350,000 estimated deaths attributed yearly to HCV-related liver diseases. It disproportionally affects people who inject drugs (PWID). Currently there is no preventative vaccine and interventions feature long treatment durations with severe side-effects. Upcoming treatments will improve this situation, making possible large-scale treatment interventions. How these strategies should target HCV-infected PWID remains an important unanswered question. Previous models of HCV have lacked empirically grounded contact models of PWID. Here we report results on HCV transmission and treatment using simulated contact networks generated from an empirically grounded network model using recently developed statistical approaches in social network analysis. Our HCV transmission model is a detailed, stochastic, individual-based model including spontaneously clearing nodes. On transmission we investigate the role of number of contacts and injecting frequency on time to primary infection and the role of spontaneously clearing nodes on incidence rates. On treatment we investigate the effect of nine network-based treatment strategies on chronic prevalence and incidence rates of primary infection and re-infection. Both numbers of contacts and injecting frequency play key roles in reducing time to primary infection. The change from “less-” to “more-frequent” injector is roughly similar to having one additional network contact. Nodes that spontaneously clear their HCV infection have a local effect on infection risk and the total number of such nodes (but not their locations) has a network wide effect on the incidence of both primary and re-infection with HCV. Re-infection plays a large role in the effectiveness of treatment interventions. Strategies that choose PWID and treat all their contacts (analogous to ring vaccination) are most effective in reducing the incidence rates of re-infection and combined infection. A strategy targeting infected PWID with the most contacts (analogous to targeted vaccination) is the least effective.

丙型肝炎病毒（Hepatitis C virus, HCV）在全球范围内慢性感染超过1.8亿人，每年预估有超过35万例死亡与HCV相关肝脏疾病相关。该病毒对注射毒品人群（people who inject drugs, PWID）的影响尤为显著。目前尚无预防性疫苗，现有干预措施疗程冗长且伴随严重副作用。即将问世的治疗手段将改善这一困境，使得大规模治疗干预成为可能。但此类策略应如何定向针对HCV感染的注射毒品人群，仍是一个亟待解答的重要问题。既往的HCV模型缺乏基于实证的注射毒品人群接触模型。本研究基于社会网络分析领域新近开发的统计方法，构建实证接触网络以模拟HCV的传播与治疗过程，现将相关研究结果报道如下。我们的HCV传播模型是一套详细的、随机的、基于个体的模型，包含自发清除感染的节点。在传播层面，我们探究了接触数量与注射频率对原发性感染时间的影响，以及自发清除感染的节点对感染发病率的作用。在治疗层面，我们分析了九种基于网络的治疗策略对慢性感染患病率、原发性感染与再感染发病率的影响。研究结果显示，接触数量与注射频率均对缩短原发性感染时间起到关键作用。从“低频率”到“高频率”注射者的转变，其效果大致等同于新增一个网络接触节点。自发清除HCV感染的节点会对局部感染风险产生影响，而此类节点的总数量（而非其分布位置）会在全网络层面影响原发性感染与再感染的发病率。再感染在治疗干预的有效性中占据重要地位。定向选择注射毒品人群并治疗其所有接触者（类似于环状疫苗接种）的策略，在降低再感染与合并感染发病率方面效果最佳；而定向针对接触数量最多的感染注射毒品人群的策略（类似于靶向疫苗接种）则效果最差。