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.

项目总结