Implications of reinfection for Hepatitis C elimination among HIV-infected and uninfected people who inject drugs

项目总结/摘要 丙型肝炎病毒（HCV）感染和HCV-HIV合并感染是导致发病和死亡的重要原因 注射毒品的人（PWID）在高负担环境中，PWID中的HCV患病率可超过90%， 随着药物使用障碍流行病学的变化，美国的发病率仍然很高。高度 HCV的有效、治愈性治疗是可用的，并且已经呼吁扩大治疗， 预防和减少危害计划，以在全球范围内消除HCV。虽然模型研究表明 到2030年，中度到高度的治疗覆盖率可能会在PWID中实现消除目标，这种模式通常 不能完全解释HCV风险的个体异质性和纵向模式。即使HCV感染率很低 再次感染可导致病原体持续存在，这一现象在考虑 在感染艾滋病毒的PWID中进行微消除。然而，测量HCV风险的传统方法导致 当较长的检测间隔导致频繁的未观察到的HCV暴露事件时， 清除-研究访视之间发生的再感染事件，归类为单一慢性感染）。 在这里，我们将利用来自一个30年的HIV感染者队列的丰富的流行病学和分子数据， 美国马里兰州巴尔的摩的未感染PWID（ALIVE研究），以探索在高水平的 配料我们将使用隐马尔可夫模型来估计未观察到的暴露 个人注射职业的风险。我们将使用下一代基因测序数据来量化 已知已接受直接作用的抗病毒治疗的个体中的再感染和治疗失败， 返回治疗后病毒血症。在这两个目标中，我们将考虑艾滋病毒合并感染作为一种修饰剂的作用， HCV风险。总之，这些目标将允许在注射职业中识别个人和时间段 其中预期HCV再感染风险高。随着对个人和时间的理解的提高， HCV再感染风险的差异，我们将对各种政策和策略进行基于证据的评估 使用动态传播模型提供HCV治疗和减少危害，包括有针对性的 艾滋病病毒感染者中的艾滋病策略。该项目将为决策者和公众提供重要指导， 在艾滋病毒感染者中设计和实施有效的HCV消除策略 和未感染的PWID

PROJECT SUMMARY / ABSTRACT Hepatitis C virus (HCV) infection and HCV-HIV coinfections are significant causes of morbidity and mortality among people who inject drugs (PWID). HCV prevalence among PWID can exceed 90% in high burden settings, and incidence remains high in the US following the changing epidemiology of substance use disorders. Highly effective, curative treatment for HCV is available, and calls have been made for the expansion of treatment-as- prevention and harm reduction programs to eliminate HCV globally. While modeling studies have indicated moderate to high treatment coverage may achieve elimination targets among PWID by 2030, such models often do not fully account for individual heterogeneity and longitudinal patterns in HCV risk. Even low rates of HCV reinfection can lead to pathogen persistence, a phenomenon that may be particularly important when considering micro-elimination among PWID living with HIV. However, traditional methods to measure HCV risk result in substantial misclassification when long testing intervals lead to frequent unobserved HCV exposure events (e.g., clearance-reinfection events occurring between study visits classified as a single chronic infection). Here, we will leverage rich epidemiologic and molecular data from a 30-year cohort of HIV-infected and uninfected PWID in Baltimore, MD USA (the ALIVE study) to explore the feasibility of HCV elimination in a high burden setting. We will use hidden Markov models which account for unobserved exposures to estimate HCV risk across individuals' injection careers. We will use next-generation genetic sequencing data to quantify risk of reinfection and treatment failure among individuals known to have received directly acting antiviral therapy and returned with post-treatment viremia. In both aims, we will consider the role of HIV coinfection as a modifier of HCV risk. Together, these aims will allow for identification of individuals and time periods within injection careers in which expected HCV reinfection risk is high. With this improved understanding of the individual and temporal variance in HCV reinfection risk, we will generate evidence-based assessments of various policies and strategies for delivering HCV treatment and harm reduction using dynamic transmission models, including targeted strategies among HIV-infected PWID. This project will provide critical guidance for policymakers and public health officials on the design and implementation of effective HCV elimination strategies among HIV-infected and uninfected PWID.