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; 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.

项目摘要 2019年冠状病毒病（Covid-19）在全球范围内继续引起严重的发病率和死亡率。 美国刑事法律机构以拥挤的拘留所和有限的医疗安全资源为特征 （CLS）经历了一些最大的Covid-19爆发。参与CLS（PCL）的人 此外，释放时会遇到严重的医疗保健障碍，并经常返回环境 受结构种族主义源于结构性种族主义的合成因素的影响：较低的疫苗通道，较少的测试设施，医疗 不信任和较高的共vid-19患病率。个人和社交网络级别之间的复杂相互作用 因素可能是推动PCL中不良的19性结果。但是，dospite他们的重要性，社会 网络影响 - 以及它们与单个和结构因素的相互作用对Covid-19的影响 未经例行检查测试，疫苗接种和更广泛的健康行为。系统地解决这个问题 差距，我们将利用美国八个州的两项现有的RADX-UP研究。 “社区网络驱动的 美国中部大多数脆弱人群中的Covid-19测试”（C3）研究在 它已收集了PCLS的测试，疫苗接种和健康行为的纵向社交网络数据 在美国五个州。此外，“矫正环境中的COVID-19测试和预防”（CTC）的研究具有 评估了美国三个州PCL的COVID-19测试，疫苗接种和缓解策略。我们将 通过网络确定器集成了通过CTC项目收集的常见数据元素 从C3数据估计以开发基于代理的网络模型（ABNM） - 动态系统建模 提供了模拟单个行为，社会行为之间紧急互动的能力的技术 人口成果的结构，政策实施和下游评估。提议 建模研究将：（1）使用机器学习来量化网络级影响对COVID-19的影响 PCLS社区内的测试，疫苗接种和健康行为； （2）建立基于代理的网络 建模（ABNM）平台，该平台整合了测试的单个常见数据元素（CD）和 从CTC研究和C3研究中的网络决定仪收集的疫苗接种； （3）模拟效果 PCLS及其疫苗接种，测试和更广泛的健康行为的干预措施及 社区。这种方法将洞悉网络信息干预的潜在影响 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 降低PCL及其社区之间的共同发病率和死亡率的干预措施。