Improving HIV and HCV outcomes in U.S. AIDS Drug Assistance Programs

描述（由申请人提供）：抗逆转录病毒疗法（ART）提高了HIV/AIDS治疗的临床效果和成本。因此，确保获得抗逆转录病毒治疗的美国医疗保健项目没有足够的资源向所有可能受益的人提供治疗。美国艾滋病药物援助计划（ADAPs）向无法获得其他药物的人提供抗逆转录病毒治疗。适应中心的服务对象主要是低收入、无保险、少数族裔，为历史上服务不足的人群提供医疗服务。随着艾滋病毒感染者存活率的提高，适应性适应方案正处于危机之中。各项目在ART资源的优先排序方面面临着艰难的决定。在ADAP等候名单上的患者中有显著的、可预防的发病率。此外，随着丙型肝炎病毒治疗变得更加有效，向艾滋病毒/丙型肝炎病毒合并感染患者提供丙型肝炎病毒治疗的压力将增加，威胁到许多人获得抗逆转录病毒治疗的稳定性。这种情况突出了艾滋病毒护理系统对结果影响的问题。在这个K01指导的职业奖励申请中，我建议使用离散事件模拟，一种创新的数学建模方法，来估计资源优先策略对HIV和HIV/HCV发病率、死亡率和获得护理的影响。三个具体目标是：1。在美国艾滋病药物援助计划的背景下，建立并验证HIV感染的离散事件模拟模型。使用经过验证的模型来估计与艾滋病药物援助计划资源优先策略相关的结果，包括改变资格规则、重组药物处方和改进成本预测。3. 将模型扩展到包括HIV/HCV合并感染，并使用它来比较HCV资源分配的潜在策略范围下的结果。为了补充这些研究工作，我将攻读高级生物统计学、运筹学和制度结构对卫生公平的影响方面的课程。这一研究和教学经验将为R01基金研究大规模规划资源分配决策对HIV和HCV临床结果的影响奠定基础。这一K01奖将导致新的、独立的研究轨迹，并将为美国的艾滋病毒和丙型肝炎病毒护理和政策提供信息。

DESCRIPTION (provided by applicant): Antiretroviral therapy (ART) increases both the clinical effectiveness, and cost of HIV/AIDS treatment. As a result, U.S. health care programs ensuring access to ART do not have sufficient resources to provide therapy to all who might benefit. U.S. AIDS Drug Assistance Programs (ADAPs) provide ART to those with no other access to medications. ADAPs serve primarily low-income, uninsured, minorities, providing access to care to historically underserved populations. As survival with HIV increases, ADAPs are in crisis. Programs face difficult decisions about prioritizing ART resources. There is significant, preventable morbidity among patients on ADAP waitlists. Further, as HCV treatment becomes more effective, pressure will build on ADAPs to provide HCV therapy to HIV/HCV co-infected patients, threatening the stability of ART access for many. This situation highlights questions about the impact of the HIV-care system on outcomes. In this K01 mentored career award application, I propose using discrete event simulation, an innovative mathematical modeling method, to estimate the impact of resource prioritization strategies on HIV and HIV/HCV morbidity, mortality, and access to care. The three specific aims are: 1. To build and validate a discrete event simulation model of HIV infection in the context of U.S. AIDS Drug Assistance Programs. 2. To use the validated model to estimate outcomes associated with several AIDS Drug Assistance Program resource prioritization strategies including altering eligibility rules, restructuring drug formularies, and Improving cost projections. 3. To expand the model to include HIV/HCV co-infection and use it to compare outcomes under spectrum of potential strategies for allocating HCV resources. To complement these research efforts, I will pursue course-work in advanced biostatistics, operations research, and the impact of institutional structures on health equity. This research and didactic experience will lay the foundation for R01 funding to investigate the implications of large-scale, programmatic resource allocation decisions on clinical outcomes in HIV and HCV. This K01 award will lead to a novel, independent trajectory of research, and will inform HIV and HCV care and policy in the United States.