Abstract WMP51: Patients’ Social Networks Influence Timing of Hospital Arrival After Acute Ischemic Stroke: a Mixed Methods Study

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Amar Dhand ◽  
Douglas Luke ◽  
Michael Tsiaklides ◽  
Catherine Lang ◽  
Jin-Moo Lee
Keyword(s):  
Risk Factors ◽  
Social Networks ◽  
Risk Factor ◽  
Ischemic Stroke ◽  
Social Network ◽  
Network Structure ◽  
Independent Risk Factor ◽  
Hospital Arrival ◽  
Prehospital Delay ◽  
Social Network Structure

Introduction: Delay in hospital arrival is a major reason for stroke patients’ exclusion from acute therapy. Risk factors for delay include older age, minor symptoms, and living alone. Personal social networks, consisting of the structure and content of relationships around a patient, are important and modifiable factors to health behavior. This study examined the role and mechanisms of patients’ social networks in prehospital delay. Hypothesis: Social network structure is an independent risk factor of prehospital delay through social influence mechanisms. Methods: Seventy consecutive patients with mild acute ischemic stroke were interviewed in the hospital. An established social network analysis instrument was used to assess personal network structure and composition. This was followed by semi-structured interviews in 14 patients focused on the arrival process. Fast arrival was defined as before 6 hours, and slow was after 6 hours. Results: There were 32 slow and 38 fast arrivers. The mean age (63) and NIHSS (3) did not differ between groups. Subcortical stroke location (53% versus 26%) and being unmarried (75% versus 44%) were more common in slow compared to fast arrivers (p<0.05). After controlling for known risk factors, social network structure was significantly associated with arrival time. As shown in figure 1, patients (A) who had networks with high constraint (e.g., strong ties among all network members) were slower to arrive than patients (B) with low constraint (e.g., weak or no ties among network members). Constraint had an adjusted OR=1.08 (95% CI 1.03-1.13, p<0.005) for slow arrival. Mechanisms revealed from qualitative analysis were social capital benefits in fast arrivers, and family members’ perceptual bias to minimize symptoms in slow arrivers. Conclusions: Patients’ social network structure is an independent risk factor for prehospital delay. These results may be used to develop network-tailored stroke education.

scholarly journals Inequality is rising where social network segregation interacts with urban topology

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gergő Tóth ◽  
Johannes Wachs ◽  
Riccardo Di Clemente ◽  
Ákos Jakobi ◽  
Bence Ságvári ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Urban Geography ◽  
Online Social Network ◽  
Stage Model ◽  
Significant Relationships ◽  
Social Segregation ◽  
Social Network Structure ◽  
Physical Barriers

AbstractSocial networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

scholarly journals Balancing User Profile and Social Network Structure for Anchor Link Inferring Across Multiple Online Social Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12031-12040 ◽  
Author(s):  
Jiangtao Ma ◽  
Yaqiong Qiao ◽  
Guangwu Hu ◽  
Yongzhong Huang ◽  
Meng Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Online Social Networks ◽  
User Profile ◽  
Social Network Structure

scholarly journals Head-to-Head Comparison of Social Network Assessments in Stroke Survivors

2020 ◽  
Vol 11 (1) ◽  
pp. 18-24
Author(s):  
Morgan Prust ◽  
Abby Halm ◽  
Simona Nedelcu ◽  
Amber Nieves ◽  
Amar Dhand
Keyword(s):  
Social Support ◽  
Social Networks ◽  
Social Network ◽  
Social Isolation ◽  
Network Structure ◽  
Network Size ◽  
Personal Network ◽  
Mapping Tool ◽  
Social Network Structure ◽  
Network Mapping

Background and Purpose: Social networks influence human health and disease through direct biological and indirect psychosocial mechanisms. They have particular importance in neurologic disease because of support, information, and healthy behavior adoption that circulate in networks. Investigations into social networks as determinants of disease risk and health outcomes have historically relied on summary indices of social support, such as the Lubben Social Network Scale–Revised (LSNS-R) or the Stroke Social Network Scale (SSNS). We compared these 2 survey tools to personal network (PERSNET) mapping tool, a novel social network survey that facilitates detailed mapping of social network structure, extraction of quantitative network structural parameters, and characterization of the demographic and health parameters of each network member. Methods: In a cohort of inpatient and outpatient stroke survivors, we administered LSNS-R, SSNS, and PERSNET in a randomized order to each patient. We used logistic regression to generate correlation matrices between LSNS-R scores, SSNS scores, and PERSNET’s network structure (eg, size and density) and composition metrics (eg, percent kin in network). We also examined the relationship between LSNS-R-derived risk of social isolation with PERSNET-derived network size. Results: We analyzed survey responses for 67 participants and found a significant correlation between LSNS-R, SSNS, and PERSNET-derived indices of network structure. We found no correlation between LSNS-R, SSNS, and PERSNET-derived metrics of network composition. Personal network mapping tool structural and compositional variables were also internally correlated. Social isolation defined by LSNS-R corresponded to a network size of <5. Conclusions: Personal network mapping tool is a valid index of social network structure, with a significant correlation to validated indices of perceived social support. Personal network mapping tool also captures a novel range of health behavioral data that have not been well characterized by previous network surveys. Therefore, PERSNET offers a comprehensive social network assessment with visualization capabilities that quantifies the social environment in a valid and unique manner.

scholarly journals Offspring social network structure predicts fitness in families

2012 ◽  
Vol 279 (1749) ◽  
pp. 4914-4922 ◽  
Author(s):  
Nick J. Royle ◽  
Thomas W. Pike ◽  
Philipp Heeb ◽  
Heinz Richner ◽  
Mathias Kölliker
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Conflicts Of Interest ◽  
Parus Major ◽  
Social Structures ◽  
Emergent Properties ◽  
Social Network Structure ◽  
The Social ◽  
The Individual

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

scholarly journals Social Network Structure Is Related to Functional Improvement From Home-Based Telerehabilitation After Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Archana Podury ◽  
Sophia M. Raefsky ◽  
Lucy Dodakian ◽  
Liam McCafferty ◽  
Vu Le ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Depressive Symptoms ◽  
Network Structure ◽  
Network Size ◽  
Network Density ◽  
Stroke Patients ◽  
Historical Cohort ◽  
Home Based ◽  
Social Network Structure

Objective: Telerehabilitation (TR) is now, in the context of COVID-19, more clinically relevant than ever as a major source of outpatient care. The social network of a patient is a critical yet understudied factor in the success of TR that may influence both engagement in therapy programs and post-stroke outcomes. We designed a 12-week home-based TR program for stroke patients and evaluated which social factors might be related to motor gains and reduced depressive symptoms.Methods: Stroke patients (n = 13) with arm motor deficits underwent supervised home-based TR for 12 weeks with routine assessments of motor function and mood. At the 6-week midpoint, we mapped each patient's personal social network and evaluated relationships between social network metrics and functional improvements from TR. Finally, we compared social networks of TR patients with a historical cohort of 176 stroke patients who did not receive any TR to identify social network differences.Results: Both network size and network density were related to walk time improvement (p = 0.025; p = 0.003). Social network density was related to arm motor gains (p = 0.003). Social network size was related to reduced depressive symptoms (p = 0.015). TR patient networks were larger (p = 0.012) and less dense (p = 0.046) than historical stroke control networks.Conclusions: Social network structure is positively related to improvement in motor status and mood from TR. TR patients had larger and more open social networks than stroke patients who did not receive TR. Understanding how social networks intersect with TR outcomes is crucial to maximize effects of virtual rehabilitation.

scholarly journals Social Network Structure as a Moderator of the Relationship between Psychological Capital and Job Satisfaction: Evidence from China

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Fan Gu ◽  
Yuanyuan Xiao
Keyword(s):  
Job Satisfaction ◽  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Psychological Capital ◽  
Longitudinal Design ◽  
Eastern China ◽  
Beijing City ◽  
Social Network Structure ◽  
The Relationship

Although networking is reported to be a job search strategy in the literature, research on the interaction between social networking and other personal resources and its effect on job satisfaction is scarce. In the perspective of social networks, the present study explored whether the social network structure, which consists of network size and tie strength, moderates the relationship between psychological capital and job satisfaction. By using a two-wave longitudinal design, we collected the quantitative data (survey of 344 undergraduate students who were about to graduate soon) from 19 universities in Beijing city, Shandong Province, and Jiangsu Province in Eastern China. Factor analysis and hierarchical regression analysis were adopted to analyze the data of the survey. We found that psychological capital has a positive impact on job seekers’ job satisfaction. Furthermore, smaller networks and weaker ties in social networks both render the positive effect of psychological capital on job satisfaction even stronger.

Networks and Media Influence

2019 ◽  
pp. 712-729
Author(s):  
David A. Siegel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Interest Groups ◽  
Network Structure ◽  
Media Influence ◽  
Social Network Structure ◽  
Political Persuasion

Citizens’ electoral choices are subject to persuasion from numerous sources, including their social networks, media outlets, candidates’ campaigns, and interest groups. Extensive literatures address the isolated effects of each source, with mechanisms as diverse as information, influence, and sanctioning driving these effects. Understanding these isolated effects is sufficient to the extent that each effect is independent of all others. However, this is not typically the case when social networks are involved, due to the feedback inherent in the propagation of persuasion across networks. This feedback implies that network structure conditions the effects of other sources of persuasion. Consequently, failure to consider social network structure in studies of political persuasion risks biased accounts of the effects of persuasion. This essay elaborates on this point and discusses its consequences for the study and practice of electoral persuasion.

Risk of Surgery and Anesthesia for Ischemic Stroke

2000 ◽  
Vol 92 (2) ◽  
pp. 425-425 ◽  
Author(s):  
Gilbert Y. Wong ◽  
David O. Warner ◽  
Darrell R. Schroeder ◽  
Kenneth P. Offord ◽  
Mark A. Warner ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Ischemic Stroke ◽  
High Risk ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Independent Risk Factor ◽  
Index Date ◽  
Stroke Index ◽  
Increased Risk

Background The goal of this study was to determine if the combination of surgery and anesthesia is an independent risk factor for the development of incident (first-time) ischemic stroke. Methods All residents of Rochester, MN, with incident ischemic stroke from 1960 through 1984 (1,455 cases and 1,455 age- and gender-matched controls) were used to identify risk factors associated with ischemic stroke. Cases and controls undergoing surgery involving general anesthesia or central neuroaxis blockade before their stroke/index date of diagnosis were identified. A conditional logistic regression model was used to estimate the odds ratio of surgery and anesthesia for ischemic stroke while adjusting for other known risk factors. Results There were 59 cases and 17 controls having surgery within 30 days before their stroke/index date. After adjusting for previously identified risk factors, surgery within 30 days before the stroke/index date (perioperative period) was found to be an independent risk factor for stroke (P&lt;0.001; odds ratio, 3.9; 95% confidence interval, 2.1-7.4). In an analysis that excluded matched pairs where the case and/or control underwent surgery considered "high risk" for stroke (cardiac, neurologic, or vascular procedures), "non-high-risk surgery" was also found to be an independent risk factor for perioperative stroke (P = 0.002; odds ratio, 2.9; 95% confidence interval, 1.5-5.7). Conclusion Our results suggest that there is an increased risk of ischemic stroke in the 30 days after surgery and anesthesia. This risk remains elevated even after excluding surgeries (cardiac, neurologic, and vascular surgeries) considered to be high risk for ischemic stroke.

scholarly journals Study on Homocysteinemia as a Risk Factor of Ischemic Stroke

2021 ◽  
Vol 34 (1) ◽  
pp. 33-39
Author(s):  
Md Amjad Hossain Pramanik ◽  
Achinta Kumar Mallick ◽  
Mukul Kumar Sarkar ◽  
SM Emdadul Haque ◽  
Md Raseul Kabir ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Ischemic Stroke ◽  
Independent Risk Factor ◽  
Homocysteine Level ◽  
Control Group ◽  
Age Group ◽  
Healthy Individuals ◽  
Serum Homocysteine ◽  
And Control

Despite recent advances, only two-third of all strokes can be attributed to known causal risk factors. Homocysteine (tHcy), a sulfur-containing amino acid, is now considered to be an important risk factor for vascular diseases, along with the established risk factors like hyperlipidemia, hypertension, diabetes mellitus, and smoking. Elevated homocysteine levels play a causal role in the pathogenesis of atherosclerosis, thromboembolism and vascular endothelial dysfunction with an increased incidence of ischemic stroke.  This study aimed to find out the association of hyperhomocysteinemia with ischemic stroke. A total of 100 subjects were included in this study, 50 were ischemic stroke patients enrolled as case, and 50 were normal healthy individuals enrolled as control. Serum homocysteine level was measured in both case and control groups. The comparison was made in both groups regarding other common risk factors like diabetes mellitus, hypertension, smoking, dyslipidemia, family history, etc.  Among 100 patients, 50 had ischemic stroke and 50 were healthy individuals. In this study, out of all patients, abnormal serum homocysteine level was found in 32% of cases and 12% of controls. The mean (±SD) serum homocysteine level was found 16.50±13.86 μmole/L in cases and 9.46±3.49 μmole /L in the control group. Significant (p<0.05) difference was found between the case and the control. The incidence of hyperhomocysteinemia is higher in ischemic stroke cases than that in age-sex-matched healthy controls. In our study, serum homocysteine was high in both younger age group patients (16.65±14.55 μmole/L vs. 9.52±3.19 μmole/L) and older age group patients (16.33±9.87 vs. 9.35±3.97 μmole/L,) in case and control group respectively. Significant (p<0.05) difference was found between the case and the control. Multiple logistic regression analysis showed that abnormal serum homocysteine is an independent risk factor of ischemic stroke. So we conclude that hyperhomocysteinemia is an important and independent risk factor for the development of ischemic stroke. Hypertension and smoking are important contributory to elevated serum homocysteine. TAJ 2021; 34: No-1: 33-39

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