INCARCERATION’S SYNDEMIC EFFECTS: TOBACCO SMOKING, ALCOHOL USE & CARDIOVASCULAR DISEASE

美国仍然是世界上监禁率最高的国家。监禁引起的慢性压力与心血管疾病（CVD）的危险因素和现有健康障碍的恶化有关。此外，涉及司法的人员吸烟和饮酒的比率高于一般人口；在监禁期间对这两种行为进行干预的效果有限。此外，与监禁相关的影响是异质性的；黑人男性的入狱率远远高于白人或拉丁裔男性，这给他们和他们的社区带来了更大的压力。因此，我们在黑人男性及其社区中看到监禁、吸烟和饮酒以及心血管疾病（CVD）的综合现象，迫切需要公共卫生对策。然而，需要更好地了解这些因素如何共同作用，在司法相关人员社区中影响心血管疾病，这反过来可以为估计政策改革如何对这一综合征产生积极影响提供信息。这些改革的影响很难用纯粹的经验方法来评估。基于代理的模型是一种动态系统建模技术，它可以灵活地将个人及其社区网络中的成员建模为“代理”，并将连接他们的社会网络结构建模为“纽带”。代理人和网络的共同进化为卫生政策实施的影响提供了实际见解。在这个项目中，将开发一个基于代理的网络模型，以芝加哥黑人群体数据测量的监禁和再犯模式为参数。吸烟、饮酒和慢性压力的危险因素将使用项目导师提供的现有临床实验室数据进行估计。被监禁的黑人男性的社会网络参数将从正在进行的NIDA研究中获得（PI Schneider, Co-I Khanna）。在虚拟计算实验室中生成的合成人口将使用布朗大学强大的计算超级计算资源进行模拟，以调查以下目标：(1)量化监禁对黑人男性及其社会网络中的人吸烟、饮酒和心血管疾病的影响；(2)预测各种脱碳政策可能对该综合征的影响。本研究产生的科学和政策见解将提供PL数据和机会，为R01应用程序开发开源建模工具，该应用程序考虑司法相关人员社区的干预措施，并开发量身定制的干预措施，以改善他们的健康结果。

The United States continues to have the highest incarceration rate in the world. Chronic stress induced by incarceration is associated with risk factors for cardiovascular disease (CVD) and exacerbation of existing health disorders. Additionally, justice-involved persons smoke tobacco and use alcohol at higher rates than the general population; interventions to address either behavior during incarceration have demonstrated limited efficacy. Incarceration-associated impacts, moreover, are heterogeneous; Black men are incarcerated at far greater rates than their White or Latino counterparts, leading to greater stressors among them and their communities. Consequently, we are seeing a syndemic of incarceration, tobacco and alcohol use, and cardiovascular disease (CVD) in Black men and their communities that urgently requires a public health response. However, greater understanding of how these factors act together to impact CVD in communities of justice-involved persons is needed, which can in turn inform estimates of how policy reform may positively impact this syndemic. The impact of such reforms is difficult to assess using purely empirical methods. Agent-based models, a dynamic systems modeling technique, provides flexibility in modeling individual persons and members of their community networks as "agents", and the social network structures that connect them as "ties". The co-evolution of agents and networks provides practical insight on the impacts of health policy implementations. In this project, an agent-based network model, parameterized with incarceration and recidivism patterns as measured from cohort data for Black men in Chicago, will be developed. Risk factors for tobacco smoking, alcohol use, and chronic stress will be estimated using existing clinical laboratory data from project mentors. Social network parameters on incarcerated Black men will be obtained from an ongoing NIDA study (PI Schneider, Co-I Khanna). A synthetic population, generated in a virtual computational laboratory, will be simulated using powerful computing supercomputing resources at Brown University to investigate the following aims: (1) Quantify the impact of incarceration on smoking, alcohol use and cardiovascular disease among Black men and persons in their social networks; (2) Predict how various decarceration policies are likely to impact this syndemic. Scientific and policy insights generated from this study will provide the PL data and opportunity to develop open source modeling tools for an R01 application that considers interventions in communities of justice-involved persons and develop interventions tailored to improve health outcomes among them.