Participatory System Dynamics Modeling to Simulate HIV Test-and-Treat Improvements

摘要 美国疾病控制与预防中心越来越多地将检测和治疗(T&T)作为一种有希望的方法来在 通过减少社区病毒载量(CVL)或社区中所有病毒的总和，达到人口水平。然而， T&T服务系统中的程序不充分或中断会导致治疗级联。之前的努力 对级联进行建模不能充分解决CVL和城市层面艾滋病毒的复杂和动态驱动 在整个T&T连续体中的服务效率。社会、行为和公共卫生科学家 越来越需要系统科学，如系统动力学(SD)建模，来理解和解决 动态的、复杂的问题，如艾滋病毒治疗的级联。这项为期两年的模型开发研究名为 “模拟HIV检测和治疗改进的参与式系统动力学建模”旨在建立 使用SD建模方法的全艾滋病毒服务系统的计算和仿真模型。 社区可以受益于与研究人员一起建立当地T&T服务的这样一个模型 系统，它可以阐明系统结构内的时间延迟和反馈回路，从而导致 系统行为中的非线性模式或意外后果。这一模型和由此产生的 模拟工具将允许不同的利益相关者将T&T连续体理解为整体的、动态的艾滋病毒 护理系统，以找出该系统的弱点和负面驱动因素，并预测和比较未来 在不需要大量资源的情况下通过模拟模拟不同系统级别干预选项的影响 投资。我们将使用组模型构建(GMB)和多个辅助数据源，由我们的 最近获得资助的康涅狄格州哈特福德当地电信服务系统(R01-MH103176)的深入研究，以建立 该系统SD计算/仿真模型。使用GMB，不同的社区利益相关者将与 研究人员开发、验证和测试该模型。目标是：(1)让一群社区参与进来 利益相关者(N=20)参与迭代的参与性GMB过程，以开发、完善和批评计算 哈特福德的HIV T&T服务系统的SD模型，旨在模拟CVL随时间的变化，以揭示 系统性能问题；(2)结合GMB估计、二次数据分析(来自R01和 地方/州/国家流行病学/服务利用数据)和相关文献以校准/参数化 并在计算SD模型中制定变量并开发参考模式(流行趋势)以 验证整个GMB过程中的仿真模型；(3)演示SD计算的使用 模型和基于Web的模拟器应用程序，作为试点分析和实用工具，允许利益相关者预测 并比较不同干预方案在城市/人口层面减少HIV CVL的影响。可持续发展部 计算模型和模拟工具可能可以转移到其他寻求通过以下方式减少HIV CVL的城市 使用本地数据和利益相关者输入进行修改。本研究为R01的实施奠定了基础 旨在测试社区利益相关者使用该工具设计长期结构性干预措施以减少CVL的研究。

ABSTRACT The CDC has increasingly promoted Test & Treat (T&T) as a promising approach to prevent HIV at the population level by reducing community viral load (CVL), or the sum of all virus in a community. However, inadequate or interrupted procedures in the T&T service system generate a treatment cascade. Prior efforts to model the cascade have inadequately addressed complex and dynamic drives of CVL and city level HIV service effectiveness across the entire T&T continuum. Social, behavioral, and public health scientists increasingly call for systems science, such as system dynamics (SD) modeling, to understand and solve dynamic, complex problems like the HIV treatment cascade. This 2-year model development study, called “Participatory System Dynamics Modeling to Simulate HIV Test-and-Treat Improvements,” is designed to build a computational and simulation model of the full HIV service system using a SD modeling approach. Communities could benefit from participating with researchers to build such a model of their local T&T service system, which can elucidate time delays and feedback loops within the structure of the system that lead to non-linear patterns or unintended consequences in the behavior of the system. This model and the resulting simulation tool would allow diverse stakeholders to understand the T&T continuum as a holistic, dynamic HIV care system, to identify weaknesses and negative drivers of the system, and to project and compare future impact of different systems level intervention options through simulation without significant resource investment. We will use group model building (GMB) and multiple secondary data sources, supported by our recently funded in-depth study of the local T&T service system in Hartford, CT (R01-MH103176), to build the SD computational/simulation model of that system. Using GMB, diverse community stakeholders will work with researchers to develop, validate, and test the model. The aims are: (1) to engage a group of community stakeholders (N=20) in an iterative, participatory GMB process to develop, refine, and critique a computational SD model of Hartford's HIV T&T service system designed to simulate CVL over time in order to uncover system performance problems; (2) to combine GMB estimates, secondary data analysis (from the R01 and local/state/national epidemiological/ service utilization data), and relevant literature to calibrate/parameterize and formulate variables in the computational SD model and develop reference modes (epidemic trends) to validate the simulation model throughout the GMB process; (3) to demonstrate use of the SD computational model and web-based simulator app as a pilot analytical and practical tool that allows stakeholders to predict and compare impacts of different intervention options to reduce HIV CVL at the city/population level. The SD computational model and simulator tool may be transferrable to other cities seeking to reduce HIV CVL with modifications using local data and stakeholder input. This study lays the foundation for an R01 implementation study to test community stakeholder use of the tool to design long-term structural interventions to reduce CVL.