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

中风延迟到达医院是公共卫生中一个尚未解决的重大问题，导致史塔克和 中风预后中持续存在的种族和社会经济差异。这种延误造成了健康差距。 因为少数族裔和社会经济上处于不利地位的患者比白人患者来得晚 更少的治疗机会和更糟糕的结果。延迟的最常见原因是 病人和目击者共同决定是观望等待还是去医院。因此，我们建议 社会联系是延迟现象的一个主要决定因素。我们的团队已经证明了社交 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 迅速或缓慢地。早期到达的患者拥有庞大且连接松散的网络，而那些 晚到的人有小而紧密的网络。然而，仍然缺乏的是对疾病程度的了解 在更多样化的中风患者群体中的社会网络效应，其机制，并转化为 改善中风延迟和差异的干预措施。这一理解对于建立严谨和 这是未来旨在减少中风预后差异的社会网络干预的前提。我们的长期合作 目标是设计基于网络的干预措施，减少中风期间的延迟，并确保公平地获得 治疗。因此，在这个项目中，我们使用了双重的经验和社会模拟方法来表征 并在不同的患者群体中模拟社交网络效应。在目标1中，我们将确定社交 网络对中风后住院延迟的影响因种族和社会经济地位不同而不同。我们会 在500名不同种族和社会经济背景的患者中收集社交网络数据和到达时间 他们的入院情况。在目标2中，我们将模拟网络干预改善中风延迟的潜力 在高危人群中。使用来自相同500名患者和他们网络中的人的数据，我们将 将基于代理的模型参数化以表示社交网络内的动态决策 卒中。然后，我们将评估网络干预改善延迟和差异的潜在影响 在模型中。我们的中心假设是，社交网络指标将与医院到达率相关 时间上，社交网络将缓和种族和SES到达时间的差异，而网络干预 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，拥有中风、社交网络、基于代理的建模和健康方面的专业知识 差异来执行这个项目。这项拟议的研究将提供亟需的社会经验数据 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 结果将产生积极影响，直接为测试社交网络干预在急性 中风临床试验，以改善到达时间和增加中风治疗的公平机会。

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.