Modeling different strategies to reduce the public health impact of the HCV/HIV e

描述（由申请人提供）：注射吸毒者（IDU）中丙型肝炎（HCV）和人类免疫缺陷（HIV）病毒的负担仍然是一个主要的公共卫生挑战。不幸的是，目前既没有预防丙肝病毒的疫苗，也没有预防艾滋病毒的疫苗或治愈方法。目前，慢性HCV感染只能用聚乙二醇干扰素和利巴韦林治疗，大多数患者无法实现持续的病毒应答。在丙型肝炎病毒和艾滋病毒合并感染的情况下，丙型肝炎病毒的预后可能更差，因为艾滋病毒已被证明会加速丙型肝炎病毒疾病的进展并降低治疗反应。今天，预防丙型肝炎病毒和艾滋病毒仍然至关重要，特别是在注射吸毒者中，因为终末期肝病已成为合并感染者死亡的主要原因。然而，仍有大量的人不知道他们感染了或感染了

DESCRIPTION (provided by applicant): The burden of the hepatitis C (HCV) and the human immunodeficiency (HIV) viruses among individuals who inject drugs (IDU) remains a major public health challenge. Unfortunately, there is neither a preventive HCV vaccine, nor a preventive vaccine or a cure for HIV. Currently, chronic HCV infection can only be treated with peginterferon alfa and ribavirin and sustained viral response is not achieved by the majority of patients. In a setting of HCV and HIV co-infection, the prognosis of HCV can be even worse, since HIV has been shown to accelerate HCV disease progression and decrease treatment response. Today, prevention of HCV and HIV remains vital, especially among IDU, since end-stage liver disease has become the leading cause of death among co-infected individuals. However, there is still a large number of individuals unaware that they are infected with either or both conditions, and a large number of individuals with no or limited access to any of the treatments. Based on the success of the "HIV Treatment as Prevention" strategy, we propose to evaluate a parallel "HCV Treatment as Prevention" strategy. The specific aims of this study are: 1. To develop a micro-simulation mathematical model to assess the impact of increasing coverage of HCV treatment, among HCV mono- and HCV/HIV co-infected IDU, as means of prevention of both HCV and HIV transmission; 2. To explore different modeling risk structures of "Who Acquire Infection from Whom", based on injecting risk behaviors, to inform the micro-simulation model regarding the nature of the transmission dynamics and use this result to predict the impact of interventions. The impact of this proposed intervention will be measured by predicting future incidence, prevalence, morbidity and mortality rates. Our modeling efforts will consider the potential impact of this strategy under various scenarios based on differing levels of HCV treatment coverage. This study will be based on published efficacy and effectiveness data on pegIFN-RBV. Fortunately, the treatment for HCV is currently undergoing significant evolution, and several of these new anti-HCV drugs have shown promising results. Thus, the proposed mathematical model will be flexible, and therefore, it will be able to accommodate emerging results from ongoing efficacy and effectiveness trials of these new HCV drugs. RELEVANCE: There is no time to wait for the "perfect regimen" or a vaccine that will prevent HCV. Many lives will be saved if we act expeditiously taking advantage of the current and newer drugs. Failure to do so will lead to avoidable healthcare costs and further stress to healthcare systems already struggling to cope with the existing demand. This proposed study has the potential to advance the operations research field, since we will be developing a micro-simulation model of disease progression and transmission which includes both HCV mono-infected and HCV/HIV co-infected IDU.