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: 10374360
• 负责人: Amar Dhand
• 金额: $ 77.43万
• 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/10374360
• 关键词: Acute; Address; Affect; African American; Age; Attenuated; Black race; Characteristics; Data; Databases; Decision Making; Dimensions; Ensure; Frequencies; Future; Goals; Health Services Accessibility; Hospitalization; Hospitals; Hour; Intervention; Knowledge; Lead; 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; base; 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.

中风延迟到达医院是公共卫生中一个未解决的主要问题， 卒中结局的持续种族和社会经济差异。这种拖延造成了健康差距 因为少数民族和社会经济上处于不利地位的患者比白色患者晚到达， 更少的治疗机会和更糟糕的结果。延迟的最常见原因是 病人和目击者一起决定观察和等待或去医院。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， 围绕特定患者的网络结构决定了导致采取行动的决定的信息流 快速或缓慢。早期到达的患者拥有庞大而松散的网络，而那些早期到达的患者则拥有庞大而松散的网络。 迟到的人都有小而紧密的网络。然而，仍然缺乏的是对 社会网络效应在更多样化的中风患者群体中的作用，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谨性和 未来社会网络干预的前提，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，减少中风期间的延迟，并确保公平获得 治疗因此，在这个项目中，我们使用双重经验和社会模拟的方法来表征 并对不同患者群体中的社交网络效应进行建模。在目标1中，我们将确定社交是否 网络影响中风后延迟到达医院的种族和社会经济地位的差异。我们将 采集500名种族和社会经济背景不同的患者的社交网络数据和到达时间， 他们的入院记录在目标2中，我们将模拟网络干预改善卒中延迟的潜力 在高危人群中。使用来自相同的500名患者及其网络中的人员的数据，我们将 参数化基于代理的模型，用于表示在社交网络中的动态决策， 中风然后，我们将评估网络干预对改善延迟和差异的潜在影响 在模型中。我们的中心假设是，社会网络指标将与医院的到来 时间，社交网络将缓和种族和SES差异的到达时间，并认为网络干预 例如，增加网络规模将改善社会模拟的结果和差异。我们有 组建了一个多学科团队，在中风、社交网络、基于代理的建模和健康方面具有专业知识 差异来执行这个项目。拟议的研究将提供急需的经验数据，社会 网络效应和网络干预的潜力，以解决中风延迟及其差异。这些 结果将产生积极的影响，直接为测试社交网络干预措施奠定基础， 中风临床试验，以改善到达时间和提高公平获得中风治疗。