scholarly journals Does Whom Patients Sit Next to During Hemodialysis Affect Whether They Request a Living Donation?

Kidney360 ◽  
2021 ◽  
pp. 10.34067/KID.0006682020
Author(s):  
Avrum Gillespie ◽  
Edward L. Fink ◽  
Heather M. Gardiner ◽  
Crystal A. Gadegbeku ◽  
Peter P. Reese ◽  
...  
Keyword(s):  
Social Network ◽  
Living Donor ◽  
Graph Model ◽  
Social Contagion ◽  
Living Donation ◽  
Exponential Random Graph Model ◽  
Relationship Formation ◽  
Random Graph Model ◽  
Seating Arrangement ◽  
Exponential Random Graph

Background: The seating arrangement of in-center hemodialysis is conducive to patients forming a relationship and a social network. We examined how in-center hemodialysis clinic seating affected patients forming relationships, whether patients formed relationships with others who have similar transplant behaviors (homophily), and whether these relationships influenced patients (social contagion) to request a living donation from family and friends outside of the clinic. Methods: In this 30-month prospective cohort study, we observed the relationships of 46 hemodialysis patients in a hemodialysis clinic. Repeated participant surveys assessed in-center transplant discussions and living donor requests. A separable temporal exponential random graph model estimated how seating, demographics, in-center transplant discussions, and living donor requests affected relationship formation via sociality and homophily. We examined whether donation requests spread via social contagion using a susceptibility-infected model. Results: For every seat apart, the odds of participants forming a relationship decreased (OR 0.74, 95% confidence interval CI [0.61, 0.90], p = 0.002). Those who requested a living donation tended to form relationships more than those who did not (sociality, OR 1.6, CI 95% [1.02, 2.6]; p = 0.04). Participants who discussed transplantation in-center were more likely to form a relationship with another participant who discussed transplantation than with someone who did not discuss transplantation (homophily, OR 1.9, CI 95% [1.03, 3.5]; p = 0.04). Five of the 36 susceptible participants made a request after forming a relationship with another patient. Conclusions: Participants formed relationships with those whom they sat next to and had similar transplant behaviors. The observed increase in in-center transplant discussions and living donation requests by the hemodialysis clinic social network members was not because of social contagion. Instead, participants who requested a living donation were more social, formed more relationships within the clinic, and discussed transplantation with each other as a function of health-behavior homophily.

scholarly journals Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles

2016 ◽  
Vol 48 (1) ◽  
pp. 202-239 ◽  
Author(s):  
Per Block ◽  
Christoph Stadtfeld ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Statistical Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Model Choice ◽  
Basic Principles ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Transition Level

Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested.

scholarly journals Symptom and Age Homophilies in SARS-CoV-2 Transmission Networks during the Early Phase of the Pandemic in Japan

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 499
Author(s):  
Ali Andalibi ◽  
Naoru Koizumi ◽  
Meng-Hao Li ◽  
Abu Bakkar Siddique
Keyword(s):  
Early Stage ◽  
Graph Model ◽  
Initial Response ◽  
Contact Tracing ◽  
Pcr Analysis ◽  
Exponential Random Graph Model ◽  
Transmission Networks ◽  
Random Graph Model ◽  
Healthcare Settings ◽  
Exponential Random Graph

Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan’s initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 registry data from Kanagawa and Hokkaido (n = 4392). Exponential random graph model (ERGM) network analysis was performed to examine demographic and symptomological homophilies. Age, symptomatic, and asymptomatic status homophilies were seen in both prefectures. Symptom homophilies suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Environmental variables such as temperature and humidity may also play a role in the overall pathogenesis of the virus. A higher level of asymptomatic transmission was observed in Kanagawa. Moreover, patients who contracted the virus through secondary or tertiary contacts were shown to be asymptomatic more frequently than those who contracted it from primary cases. Additionally, most of the transmissions stopped at the primary and secondary levels. As expected, significant viral transmission was seen in healthcare settings.

scholarly journals The drivers of regulatory networking: policy learning between homophily and convergence

2018 ◽  
Vol 39 (3) ◽  
pp. 443-464 ◽  
Author(s):  
Francesca P. Vantaggiato
Keyword(s):  
Regulatory Networks ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Random Graph Model ◽  
Market Structures ◽  
Exponential Random Graph ◽  
Policy Frameworks ◽  
Regulatory Coordination ◽  
Regular Network ◽  
The Eu

AbstractThe literature on transnational regulatory networks identified interdependence as their main rationale, downplaying domestic factors. Typically, relevant contributions use the word “network” only metaphorically. Yet, informal ties between regulators constitute networked structures of collaboration, which can be measured and explained. Regulators choose their frequent, regular network partners. What explains those choices? This article develops an Exponential Random Graph Model of the network of European national energy regulators to identify the drivers of informal regulatory networking. The results show that regulators tend to network with peers who regulate similarly organised market structures. Geography and European policy frameworks also play a role. Overall, the British regulator is significantly more active and influential than its peers, and a divide emerges between regulators from EU-15 and others. Therefore, formal frameworks of cooperation (i.e. a European Agency) were probably necessary to foster regulatory coordination across the EU.

scholarly journals A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

2019 ◽  
Vol 7 (1) ◽  
pp. 20-51 ◽  
Author(s):  
Philip Leifeld ◽  
Skyler J. Cranmer
Keyword(s):  
Network Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Real Data ◽  
The Other ◽  
Exponential Random Graph Model ◽  
Empirical Comparison ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Longitudinal Network Analysis

AbstractThe temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.

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