MODELING COMBINATION BIOMEDICAL SUBSTANCE USE INTERVENTIONS TO INFORM A GETTING TO ZERO IMPLEMENTATION FOR BLACK MEN WHO HAVE SEX WITH MEN

项目概要/摘要 GTZ和其他消除艾滋病毒倡议在美国获得了势头 根据2010年艾滋病规划署战略计划，德国技术合作公司。这些举措通常侧重于消除艾滋病毒 在地方管辖范围内，由于国内艾滋病毒发病率趋于稳定，加上越来越多地使用 生物医学预防方式，如开始抗逆转录病毒治疗，以治疗预防， 暴露前预防（PrEP）虽然伊利诺伊州（以及整个美国）的艾滋病毒总发病率具有 在过去十年中，这种下降已趋于平稳，各亚人口的下降幅度并不相同。 年轻（18-34岁）黑人同性恋、双性恋和其他男男性接触者（YBMSM）经历了相对稳定的 发病率。这些观察结果支持制定旨在降低艾滋病毒发病率的干预措施 在YBMSM中。ART和PrEP在BMSM中的规模扩大受到限制，部分原因是 这些障碍阻碍了它们在YBMSM中的广泛使用。成瘾、心理健康状况、刑事司法 参与，以及从拘留中释放后的失业是一些不成比例的障碍， 影响YBMSM。药物使用就是这样一个障碍，它与次优的ART有关 坚持和错过的PrEP剂量MSM之间。药物使用干预措施已被发现， 增加艾滋病毒感染风险的行为，参与持续的ART护理和PrEP 潴留因此，联合生物医学物质使用干预措施可能提供一种途径， 艾滋病毒发病率可能比目前考虑的假设情况更容易实现。然而，这是困难的， 设计以证据为基础的干预措施，以最佳方式实现德国技术合作公司的目标。计算模型可以 提供计算机模拟实验室，以研究如何最大限度地发挥这种综合干预措施的影响。 应严格审查如何优化这些综合干预措施的影响， 在开展干预措施之前通报最佳做法。基于代理的网络模型（ABNM）提供了一个 计算严格，但灵活的平台，允许包括各种微观层次的个人 行为，中观层次的网络因素，以及它们与宏观层次的人口结构的反馈，这使得 在多个尺度上严格检查过程及其参数化。因此，ABNM可以 在推出多层面干预措施之前，促进制定这些措施。在建议的研究中，我们会：（1） 制定一套全面的参数，描述兴奋剂使用对ART和PrEP护理的影响 继续在YBMSM在伊利诺伊州;（2）重新校准现有的ABNM的YBMSM之间的艾滋病毒传播 使用上述收集的参数开发模块，说明兴奋剂使用对 参与ART和PrEP连续体;（3）估计组合生物医学的潜在疗效 物质使用干预。

Project Summary/Abstract Getting to Zero (GTZ) and other HIV elimination initiatives have gained momentum in the United States following the UNAIDS strategic plan GTZ in 2010. These initiatives have typically focused on HIV elimination within local jurisdictions and been driven by a plateauing domestic HIV incidence combined with growing use of biomedical prevention modalities such as antiretroviral treatment (ART) initiation for treatment-as-prevention, and pre-exposure prophylaxis (PrEP). While overall HIV incidence in Illinois (and across the U.S.) has plateaued over the past decade, these declines have not been experienced equally by sub-populations. Younger (18-34 years) Black gay, bisexual and other MSM (YBMSM) have experienced relatively stable incidence rates. These observations support development of interventions designed to reduce HIV incidence among YBMSM. The scale-up of ART and PrEP among BMSM is constrained in part because of the many barriers that prevent their wider use among YBMSM. Addiction, mental health conditions, criminal justice involvement, and unemployment upon release from custody are some of the barriers that disproportionately impact YBMSM. Substance use is one such barrier, and it has been associated with suboptimal ART adherence and missed PrEP doses among MSM. Substance use interventions have been found to reduce behaviors that elevate risk for HIV acquisition, engagement in the continuums of ART care, and PrEP retention. Combination biomedical substance use interventions may therefore provide a pathway to decrease HIV incidence that may be more implementable than the current scenarios considered. It is difficult, however, to design evidence-based interventions that will optimally implement GTZ goals. Computational models can provide an in-silico laboratory to examine how the impact of such combination interventions can be maximized. Rigorous examination of how the impact of such combination interventions can be optimized should be used to inform best practices prior to intervention roll-out. Agent-based network models (ABNMs) provide a computationally rigorous, yet flexible, platform that allow for inclusion of a variety of micro-level individual behaviors, meso-level network factors, and their feedbacks with macro-level population structures, which allow for rigorous examination of processes and their parameterization at multiple scales. ABNMs can therefore promote development of multilevel interventions prior to their roll-out. In the proposed study, we will: (1) develop a comprehensive suite of parameters that describe the impact of stimulant use on ART and PrEP care continuums among YBMSM in Illinois; (2) recalibrate an existing ABNM of HIV transmission among YBMSM using the parameters collected above to develop modules that account for the impact of stimulant use on engagement in the ART and PrEP continuums; (3) estimate the potential efficacy of combination biomedical substance use interventions.