Social networks and risk of delayed arrival to the hospital during stroke

社交网络和中风期间延迟到达医院的风险

• 批准号: 10611852
• 负责人: Amar Dhand
• 金额: $ 73.95万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-20 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611852
• 关键词: Acute; Address; Affect; African American; Age; Attenuated; Black race; Characteristics; Data; Databases; Decision Making; Dimensions; Disparity; Ensure; Equity; Frequencies; Future; Goals; Health Services Accessibility; Hospitalization; Hospitals; Hour; Individual; Intervention; Knowledge; Measures; Modeling; Morbidity - disease rate; Network-based; Outcome; Patients; Persons; Population; Population Heterogeneity; Populations at Risk; Public Health; Race; Research; Risk; Risk Factors; Social Environment; Social Network; Socioeconomic Status; Stroke; Stroke Volume; Structure; Symptoms; Testing; Time; Translations; Work; acute stroke; density; design; disability; disparity reduction; health disparity; high risk; improved; improved outcome; low socioeconomic status; multidisciplinary; patient population; post stroke; racial disparity; racial minority; recruit; simulation; social; social contact; social structure; socioeconomic disadvantage; socioeconomic disparity; socioeconomics; stroke clinical trials; stroke model; stroke outcome; stroke patient; stroke symptom; stroke therapy

Delayed arrival to the hospital in stroke is a major unsolved problem in public health that leads to stark and persistent racial and socioeconomic disparities in stroke outcomes. The delay generates health disparities because racial minority and socioeconomically disadvantaged patients arrive later than White patients leading to less access to treatment and worse outcomes. The most common reason for delay is the time spent by the patient and witnesses who decide together to watch-and-wait or go to the hospital. Therefore, we propose that social connectedness is a major determinant of the delay phenomenon. Our team has demonstrated that social network structure around a specific patient determines the flow of information that leads to decisions to act rapidly or slowly. Patients who arrived early had large and loosely connected networks, while those who arrived late had small and close-knit networks. What remains lacking, however, is knowledge of the extent of the social network effect in a more diverse population of stroke patients, its mechanism, and translation into interventions to improve stroke delay and disparities. This understanding is critical to establishing rigor and premise for future social network interventions aimed at reducing disparities in stroke outcomes. Our long-term goal is to design network-based interventions that reduce delay during stroke and ensure equitable access to therapies. Therefore, in this project, we use a dual empirical and social simulation approach to characterize and model social network effects in a diverse patient population. In Aim 1, we will determine whether social networks affect delay in hospital arrival after stroke differentially by race and socioeconomic status. We will capture social network data and time to arrival in 500 racially and socioeconomically diverse patients during their hospital admission. In Aim 2, we will model the potential of network interventions to improve stroke delay in at-risk populations. Using data from the same 500 patients and persons in their network, we will parameterize an agent-based model to represent the dynamic decision-making within the social network during stroke. Then we will evaluate the potential effects of network interventions to improve delay and disparities within the model. Our central hypothesis is that social network metrics will be associated with hospital arrival time, social networks will moderate race and SES differences in arrival time, and that network interventions such as increasing network size will improve outcomes and disparities in social simulations. We have assembled a multidisciplinary team with expertise in stroke, social networks, agent-based modeling, and health disparities to execute this project. The proposed research will provide much needed empirical data on social network effects and the potential of network interventions to address stroke delay and its disparities. These results will have a positive impact by directly setting the stage for testing social network interventions in acute stroke clinical trials to improve arrival time and enhance equitable access to stroke therapies.

延迟到达医院的中风是公共卫生中的一个主要未解决问题，导致史塔克和 中风结果中的持续种族和社会经济差异。延迟会产生健康差异 因为种族少数群体和社会经济上的处境不利的患者比白人患者更晚。 减少获得治疗和更糟糕的结果。延迟的最常见原因是 决定共同等待或去医院的患者和目击者。因此，我们建议 社会联系是延迟现象的主要决定因素。我们的团队已经证明了社交 特定患者周围的网络结构决定了导致决定行动的信息流 迅速或缓慢。早点到达的患者具有较大且连接松散的网络，而那些 到达迟到的小型和紧密联系的网络。然而，仍然缺乏的是了解程度的知识 社交网络对中风患者的多样化人群的效应，其机制以及转化为 改善中风延迟和差异的干预措施。这种理解对于建立严格和 未来社交网络干预措施的前提，旨在减少中风结果的差异。我们的长期 目标是设计基于网络的干预措施，以减少中风期间的延迟并确保公平访问 疗法。因此，在这个项目中，我们使用双重经验和社会模拟方法来表征 并建模社交网络对多样化的患者人群的影响。在AIM 1中，我们将确定社交是否 网络会因种族和社会经济地位而差异化中风后医院的到来延迟。我们将 捕获社交网络数据以及在种族和社会经济上有500名到达的时间 他们的医院入院。在AIM 2中，我们将建模网络干预的潜力以改善中风延迟 在高危人群中。使用来自同一500名患者和网络中人员的数据，我们将 参数化基于代理的模型，以表示社交网络中的动态决策 中风。然后，我们将评估网络干预措施的潜在影响以改善延迟和差异 在模型中。我们的中心假设是社交网络指标将与医院到达有关 时间，社交网络将在到达时间和网络干预中适度种族和SES差异 例如增加网络规模将改善社交模拟的结果和差异。我们有 组建了一个具有中风，社交网络，基于代理的建模和健康方面的专业知识的多学科团队 执行此项目的差异。拟议的研究将为社会提供急需的经验数据 网络效应以及网络干预措施解决中风延迟及其差异的潜力。这些 结果将直接为急性测试社交网络干预奠定阶段，从而产生积极的影响 中风临床试验以改善到达时间并增强对中风疗法的公平通道。