Localized economic modeling to optimize public health strategies for HIV treatment and prevention

 产品说明：艾滋病毒治疗方面的科学进展表明，立即获得抗逆转录病毒综合疗法既有利于个人健康，也有利于公共健康。如果充分接受抗逆转录病毒疗法，就可以在人口一级大幅度减少艾滋病毒相关的发病率、死亡率和传播。因此，全球艾滋病毒控制战略的重点已经转向努力“寻找，测试，治疗和保留”艾滋病毒感染者（PLHIV）在ART。新兴的研究艾滋病毒护理的级联，一个简单的指标跟踪感染，诊断，对ART和病毒抑制的个人数量，已经明确指出，艾滋病毒流行的控制取决于卫生保健系统在向艾滋病毒感染者提供护理方面的表现。然而，作为一种监督工具，它为如何分配资金的决定提供了一个不完整和可能误导的基础。 美国的艾滋病流行是一种多样化的微流行病，主要分布在具有不同潜在流行病学和结构条件的大城市中心。这些不同的基本条件需要本地化的战略，以优化艾滋病毒护理级联。随着时间的推移，这些微流行病以及如何改进艾滋病毒检测和护理的证据发生了变化，这对我们做出知情和及时决定的能力提出了挑战，这些决定将使投入治疗和预防艾滋病毒的资源产生最大的健康效益。 我们提出了一个新的经济建模框架，以彻底改变艾滋病毒监测。这一框架将确保稀缺资源集中用于在特定微流行病中能够提供最大资金价值的干预措施。我们将建立一个经过验证的经济模型，以产生快速，自动化的证据合成，重点是最低限度的数据要求，以更新模型，并将公共卫生干预措施的新成果的发现纳入实时疾病监测系统。 关于改善艾滋病毒检测和抗逆转录病毒疗法参与的干预措施的实验性和观察性研究表明，这些措施在一般人群和目标人群中有效，但在实践中却远远没有得到充分利用。针对注射吸毒者（PWID）的结构性干预措施，如针头交换和阿片类激动剂治疗（OAT），已被证明在这些服务障碍较低的司法管辖区具有令人难以置信的价值，导致艾滋病毒发病率和疾病负担的惊人下降。我们假设，这些干预措施的独特组合将在每种微流行病中提供最佳的资金价值，并且最佳组合将根据潜在的流行趋势和艾滋病毒干预研究的科学进步状况而随着时间的推移而变化。 我们将展示我们的方法，在美国六个不同的城市环境与不同的结构条件，物质使用模式和艾滋病毒流行。该项目是一项创新的转化研究举措，将加强监测工作，增加艾滋病毒和药物使用障碍干预研究的影响。

 DESCRIPTION: Scientific advances in HIV treatment have demonstrated that immediate access to combination antiretroviral therapy (ART) provides both individual and public health benefits. With sufficient uptake of ART, HIV-related morbidity, mortality and transmission can be drastically reduced at the population-level. As a result, the focus of global HIV control strategie have turned towards efforts to 'seek, test, treat and retain' people living with HIV (PLHIV) in ART. Emerging research on the cascade of HIV care, a simple metric tracking numbers of individuals infected, diagnosed, on ART and virally suppressed, has made clear that the control of the HIV epidemic hinges on the performance of health care systems in reaching and delivering care to PLHIV. Yet, as a surveillance tool, it provides an incomplete and possibly misleading basis for decisions on how to allocate funding. The US HIV epidemic is a diverse set of microepidemics, dispersed primarily across large urban centers with different underlying epidemiological and structural conditions. These disparate underlying conditions require localized strategies to optimize the HIV care cascade. Changes over time in these microepidemics, as well as in evidence on how to improve HIV testing and care, challenges our ability to make informed and timely decisions that result in the greatest health benefits for the resources invested in treating and preventing HIV. We propose a novel economic modeling framework to revolutionize HIV surveillance. This framework would ensure scarce resources are focused on interventions that can provide the greatest value for money in a given microepidemic. We will build upon a validated economic model to produce rapid, automated evidence synthesis, focusing on minimal data requirements to update the model, and integrate findings from emerging results of public health interventions into real-time disease surveillance systems. Experimental and observational studies of interventions to improve HIV testing and ART engagement have demonstrated their effectiveness in generalized and targeted populations, yet they are vastly underused in practice. Structural interventions for people who inject drugs (PWID), such as needle exchange and opioid agonist therapy (OAT), have proven incredibly valuable in jurisdictions with low barriers to these services, resulting in staggering declines in HIV incidence and disease burden. We hypothesize that a unique mix of these interventions will provide the best value for money in each micro epidemic, and that the optimal combinations will change over time according to underlying epidemic trends and the state of scientific advancement in HIV intervention research. We will demonstrate our approach for six distinct urban settings in the US with disparate structural conditions, substance use patterns, and HIV epidemics. This project is an innovative translational research initiative that will enhance surveillance efforts and increase the impact of interventional research in HIV and substance use disorders.