Modeling the Co-evolution of Substance Use behavior and peer Networks of risk/support (CoSUN)

多年来，美国药物过量死亡人数持续增加，死亡人数超过70，000人 2019年物质使用和滥用相关犯罪、医疗保健和工作损失的经济成本 美国的生产力每年超过6000亿美元。为了帮助个人和减少 为了提高相关成本效益，我们必须将适当的资源分配给最大程度地 新出现的需求（例如，未来的高使用率）。然而，目前还没有数据驱动的 允许利益相关者（随机）预测个人物质使用的工具。电流 方法仅依赖于趋势分析，建立风险因素之间的相关性（例如，朋友 使用药物）和物质使用。我们在模拟未来物质使用时面临的一个关键挑战 在于行为的共同进化（即，毒品使用）和风险/支助同侪网络，这些网络可 随着时间的推移而变化，并且可能相互依赖。为了解决这一科学障碍，我们考虑一个 创新方法，体现了药物使用和同伴风险/支持的共同演变过程 网络（目标1A）首先建模如何个别属性（例如，吸毒和不良童年 历史）沿着它们的对等网络（例如，同伴和知己吸毒的程度）影响 个人的物质使用行为和（目标1B），然后建模如何个人的同伴风险/支持 网络链路响应于端点属性的相似性或差异而形成或断开（例如， 吸毒行为）。因此，本项目首次寻求发展随机预测 未来物质使用模型（FSU）和未来长期同伴风险/支持网络（FPN） 月内（目标1-2）和短时间尺度的FSU（目标3），使用协变量数据 个人属性和对等网络特征。我们将使用成功的机器学习方法， 建立这些模型，并严格评估模型的可推广性/预测性能（目标1-3）， 利用从模型构建过程中保留的数据。目标将提供一个基础 用于未来创新的NIH R 01，开发现实SUD相关行为的随机模拟 在合理的动态网络中传播，为资源分配和应急计划提供信息。这 该项目还为及时干预奠定了基础，以发现FSU即将增加， 使用这些风险预测来触发社会和行为干预措施的交付。

Drug overdose deaths in the U.S. have continued to increase over the years, with over 70,000 deaths in 2019. The economic cost of substance use and abuse related crimes, healthcare, and loss in work productivity in the U.S. exceeds $600 billion each year. In order to aid individuals and reduce the associated cost effectively, we must allocate appropriate resources to individuals in the greatest emerging need (e.g., those of high future substance use). However, there are currently no data-driven tools that allow stakeholders to (stochastically) forecast an individual's substance use. Current methodologies only rely on trend analyses, establishing correlations between risk factors (e.g., friends that use drugs) and substance use. A key challenge we are facing when modelling future substance use lies in the co-evolution of behaviors (i.e., drug use) and peer networks of risk/support, which can change over time and may depend on each other. To address this scientific obstacle, we consider an innovative approach that decouples the co-evolution process of substance use and peer risk/support networks by (Aim 1A) first modelling how individual attributes (e.g., drug use and adverse childhood history) along with their peer networks (e.g., the extent of peer and confidant drug use) impact the individual’s substance use behavior and (Aim 1B) then modeling how individuals’ peer risk/support network links form or break in response to similarities or differences in the endpoints’ attributes (e.g., their drug use behaviors). Thus, for the first time, this project seeks to develop stochastic forecasting models for future substance use (FSU) and future peer risk/support networks (FPN) at long timescales within months (Aims 1-2) and for FSU at short timescales within days (Aim 3) using data on covariates of individual attributes and peer network features. We will use successful machine-learning methods to build these models and rigorously assess model generalizability/prediction performance (Aims 1-3) by making use of data that is held-out from the model building process. The Aims will provide a foundation for a future innovative NIH R01 that develops stochastic simulations of realistic SUD-related behavioral contagion in plausible dynamic networks, to inform resource allocation and contingency planning. This project also lays the groundwork for just-in-time interventions to detect imminent increases in FSU and use these risk forecasts to trigger the delivery of social and behavioral interventions.