Integrated Network Analysis of RADx-UP Data to Increase COVID-19 Testing and Vaccination Among Persons Involved with Criminal Legal Systems (PCLS)

RADx-UP 数据的综合网络分析可提高刑事法律系统 (PCLS) 相关人员的 COVID-19 检测和疫苗接种率

• 批准号: 10879972
• 负责人: Aditya Subhash Khanna
• 金额: $ 27.92万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10879972
• 关键词: Address; Affect; Attitude; Automobile Driving; Behavior; Biological Models; COVID-19; COVID-19 morbidity; COVID-19 mortality; COVID-19 outbreak; COVID-19 prevention; COVID-19 testing; COVID-19 vaccination; Common Data Element; Communities; Community Networks; Complex; Crowding; Data; Diffusion; Disparity; Drug Courts; Ensure; Environment; Family; Friends; General Population; Graph; Health; Health Information System; Health behavior; Imprisonment; Incidence; Individual; Inequity; Infection; Insurance; Intervention; Investigation; Jail; Legal; Legal system; Machine Learning; Medical; Minnesota; Misinformation; Modeling; Modernization; Morbidity - disease rate; Outcome; Pathway Analysis; Persons; Policies; Population; Positioning Attribute; Prevalence; Prevention strategy; Preventive; Prisons; RADx Underserved Populations; Resources; Rhode Island; SARS-CoV-2 infection; SARS-CoV-2 transmission; Safety; Sampling; Site; Social Network; Socioeconomic Status; Structural Racism; Structure; Study models; System; Techniques; Testing; US State; United States; Vaccinated; Vaccination; Vaccines; Vulnerable Populations; Washington; adverse outcome; barrier to care; community reentry; community setting; comparative; data integration; dynamic system; experience; future pandemic; health care availability; health disparity; improved; infection rate; innovation; insight; intervention effect; machine learning method; mortality; network models; parole; peer; people of color; probation; social health determinants; social structure; structural determinants; syndemic; uptake; vaccination outcome; vaccine acceptance; vaccine access; vaccine hesitancy

Project Summary Coronavirus Disease 2019 (COVID-19) continues to cause significant morbidity and mortality across the world. Characterized by crowded detention facilities and limited medical safety resources, US criminal legal settings (CLS) have experienced some of the largest COVID-19 outbreaks. Persons involved with CLS (PCLS) additionally experience significant barriers to health care upon release, and often return to environments impacted by syndemic factors rooted in structural racism: lower vaccine access, fewer testing facilities, medical mistrust, and higher COVID-19 prevalence. A complex interplay between individual and social network-level factors may be driving the adverse COVID-19 outcomes among PCLS. But, despite their importance, social network influences – and the effect of their interaction with individual ands structural factors on COVID-19 testing, vaccination and broader health behaviors – are not routinely examined. To systematically address this gap, we will leverage two existing RADx-UP studies across eight US states. The “Community Network Driven COVID-19 Testing Among Most Vulnerable Populations in the Central United States” (C3) study is unique in that it has collected longitudinal social network data on testing, vaccination and health behaviors among PCLS in five US states. Additionally, the “COVID-19 Testing and Prevention in Correctional Settings” (CTC) study has assessed COVID-19 testing, vaccination, and mitigation strategies for PCLS in three US states. We will integrate the common data elements collected through the CTC project with the network determinants estimated from the C3 data to develop an agent-based network model (ABNM) – a dynamic systems modeling technique that provides the ability to simulate emergent interaction between individual behaviors, social structures, policy implementation, and downstream assessment of population outcomes. The proposed modeling study will: (1) use machine learning to quantify the impact of network-level influences on COVID-19 testing, vaccination, and health behaviors within PCLS communities; (2) build an agent-based network modeling (ABNM) platform that integrates the individual common data elements (CDEs) of testing and vaccination collected from the CTC study and network determinants from the C3 study; (3) simulate the effects of interventions on COVID-19 vaccination, testing and broader health behaviors in PCLS and their communities. This approach will provide insight on the potential impacts of network-informed interventions using RADx-UP data, social network analysis, machine learning, and agent-based modeling to identify interventions to reduce COVID-19 morbidity and mortality among PCLS and their communities.

项目概要 2019 年冠状病毒病 (COVID-19) 继续在世界各地造成显着的发病率和死亡率。 美国刑事法律环境以拘留设施拥挤、医疗安全资源有限为特点 (CLS) 经历过一些最大规模的 COVID-19 疫情。 与 CLS 相关的人员 (PCLS)。 释放后还面临医疗保健方面的重大障碍，并经常返回环境 受到根源于结构性种族主义的综合症因素的影响：疫苗获取率较低、检测设施较少、医疗 不信任以及更高的 COVID-19 患病率。个人和社交网络层面之间存在复杂的相互作用。 但是，尽管这些因素很重要，但社会因素可能会导致 PCLS 中的 COVID-19 不良结果。 网络影响——以及它们与个体和结构因素的相互作用对 COVID-19 的影响 检测、疫苗接种和更广泛的健康行为——没有定期进行检查来系统地解决这个问题。 差距，我们将利用美国八个州现有的两项 RADx-UP 研究“社区网络驱动”。 “美国中部最脆弱人群中的 COVID-19 检测”(C3) 研究在 它收集了 PCLS 中有关检测、疫苗接种和健康行为的纵向社交网络数据 此外，“惩教机构中的 COVID-19 检测和预防”(CTC) 研究已在美国五个州开展。 我们将评估美国三个州的 PCLS 的 COVID-19 检测、疫苗接种和缓解策略。 将通过 CTC 项目收集的通用数据元素与网络决定因素相整合 根据 C3 数据进行估计，以开发基于代理的网络模型 (ABNM) – 动态系统建模 提供模拟个人行为、社会行为之间的紧急互动能力的技术 结构、实施政策和人口结果的下游评估。 建模研究将：(1) 使用机器学习来量化网络层面对 COVID-19 的影响 PCLS 社区内的检测、疫苗接种和健康行为；(2) 建立基于代理的网络； 建模（ABNM）平台，集成了测试和测试的各个通用数据元素（CDE） 从 CTC 研究收集的疫苗接种和从 C3 研究收集的网络决定因素 (3) 模拟效果； PCLS 中对 COVID-19 疫苗接种、检测和更广泛的健康行为的干预措施及其 这种方法将深入了解网络知情干预措施的潜在影响。 使用 RADx-UP 数据、社交网络分析、机器学习和基于代理的建模来识别 采取干预措施降低 PCLS 及其社区的 COVID-19 发病率和死亡率。