Center for Prevention Implementation Methodology for Drug Abuse and HIV (Ce-PIM), Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
Northwestern Institute for Complex Systems (NICO), Northwestern University, Evanston, IL, United States of America.
PLoS One. 2022 Oct 17;17(10):e0274288. doi: 10.1371/journal.pone.0274288. eCollection 2022.
Our objective is to improve local decision-making for strategies to end the HIV epidemic using the newly developed Levers of HIV agent-based model (ABM). Agent-based models use computer simulations that incorporate heterogeneity in individual behaviors and interactions, allow emergence of systemic behaviors, and extrapolate into the future. The Levers of HIV model (LHM) uses Chicago neighborhood demographics, data on sex-risk behaviors and sexual networks, and data on the prevention and care cascades, to model local dynamics. It models the impact of changes in local preexposure prophylaxis (PrEP) and antiretroviral treatment (ART) (ie, levers) for meeting Illinois' goal of "Getting to Zero" (GTZ) -reducing by 90% new HIV infections among men who have sex with men (MSM) by 2030. We simulate a 15-year period (2016-2030) for 2304 distinct scenarios based on 6 levers related to HIV treatment and prevention: (1) linkage to PrEP for those testing negative, (2) linkage to ART for those living with HIV, (3) adherence to PrEP, (4) viral suppression by means of ART, (5) PrEP retention, and (6) ART retention. Using tree-based methods, we identify the best scenarios at achieving a 90% HIV infection reduction by 2030. The optimal scenario consisted of the highest levels of ART retention and PrEP adherence, next to highest levels of PrEP retention, and moderate levels of PrEP linkage, achieved 90% reduction by 2030 in 58% of simulations. We used Bayesian posterior predictive distributions based on our simulated results to determine the likelihood of attaining 90% HIV infection reduction using the most recent Chicago Department of Public Health surveillance data and found that projections of the current rate of decline (2016-2019) would not achieve the 90% (p = 0.0006) reduction target for 2030. Our results suggest that increases are needed at all steps of the PrEP cascade, combined with increases in retention in HIV care, to approach 90% reduction in new HIV diagnoses by 2030. These findings show how simulation modeling with local data can guide policy makers to identify and invest in efficient care models to achieve long-term local goals of ending the HIV epidemic.
我们的目标是利用新开发的 HIV 基于代理的模型（ABM）杠杆，改善当地的决策制定，以制定终结 HIV 流行的策略。基于代理的模型使用计算机模拟，将个体行为和相互作用的异质性纳入其中，允许系统行为出现，并推断未来。HIV 模型的杠杆（LHM）使用芝加哥社区的人口统计学数据、性风险行为和性网络数据以及预防和护理级联数据来模拟当地动态。它模拟了改变当地暴露前预防（PrEP）和抗逆转录病毒治疗（ART）（即杠杆）的影响，以实现伊利诺伊州的“零目标”（GTZ）——到 2030 年将男男性行为者（MSM）中新感染 HIV 的人数减少 90%。我们根据与 HIV 治疗和预防相关的 6 个杠杆，对 2304 个不同场景进行了 15 年（2016-2030 年）的模拟：（1）对检测结果为阴性的人进行 PrEP 衔接，（2）对携带 HIV 的人进行 ART 衔接，（3）PrEP 依从性，（4）通过 ART 实现病毒抑制，（5）PrEP 保留，以及（6）ART 保留。使用基于树的方法，我们确定了在 2030 年之前实现 HIV 感染减少 90%的最佳场景。最佳场景包括最高水平的 ART 保留和 PrEP 依从性，其次是最高水平的 PrEP 保留，以及中等水平的 PrEP 衔接，在 58%的模拟中，到 2030 年可实现 90%的 HIV 感染减少。我们使用基于我们模拟结果的贝叶斯后验预测分布，根据最近的芝加哥公共卫生部监测数据确定实现 90% HIV 感染减少的可能性，并发现当前下降速度（2016-2019 年）的预测结果不会实现 2030 年 90%（p = 0.0006）的减少目标。我们的结果表明，需要在 PrEP 级联的所有步骤中增加干预措施，同时增加 HIV 护理的保留率，才能在 2030 年之前达到新 HIV 诊断减少 90%的目标。这些发现表明，如何利用本地数据进行模拟建模可以为决策者提供指导，以确定和投资有效的护理模式，实现终结 HIV 流行的长期本地目标。
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