Modeling And Simulation Research On The Influence Of Social Network On Prehospital Delay In Patients With Acute Ischemic Stroke

Objectives1.To explore the current situation of prehospital delay in patients with acute ischemic stroke,and to analyze the influence of social network structure on prehospital delay in patients with acute ischemic stroke by social network analysis.2.The hybrid simulation model of prehospital delay of people with acute ischemic stroke was constructed,and the effects of different social network characteristics on prehospital delay were quantitatively evaluated by designing simulation experiments.Methods1.First of all,by using the method of cross-sectional survey and convenient sampling,patients with acute ischemic stroke in the neurological wards of two third-class hospitals in Qingdao from October 2020 to May 2021 were investigated by individual social network questionnaire.Ucinet was used to obtain the quantitative characteristics of social network,and SPSS software was used for logistic regression analysis,so as to determine the influence of the characteristics of social network structure on prehospital delay.2.The hybrid simulation model of system dynamics and multi-agent is constructed by using Anylogic simulation platform,and based on the cross-sectional survey data,the simulation is carried out with the help of the model to verify the effectiveness of the model.Finally,six groups of simulation experiments are designed according to the results of social network analysis,so as to further intuitively and quantitatively observe the impact of social network characteristics on prehospital delay.Results1.A total of 422 patients with acute ischemic stroke and 2471 social network nominees were investigated,36.0% of the patients arrived at the hospital within 3 hours after onset,and 64.0% of the patients had prehospital delay.The corrected binary logic regression results showed that six characteristics of social network structure could affect the prehospital delay in patients with ischemic stroke.Effective size(OR=0.384,95% CI:0.179-0.673,P = 0.002),percentage kin(OR= 0.135,95% CI: 0.031-0.586,P = 0.008)and people around immediately seek medical help when the patient has symptoms(OR=0.191,95% CI: 0.076-0.485,P < 0.001)were the protective factors of prehospital delay.Betweeness centrality(OR= 1.176,95% CI: 1.027-1.346,P = 0.019),network constraint(OR= 1.019,95% CI: 1.001-1.038,P = 0.038)and network efficiency(OR= 1.041,95%CI: 1.010-1.072,P = 0.008)are the risk factors of prehospital delay.2.The relative error between the simulation data and the real data is less than 10%,the RMS value is 3%,and the model fits well.From the simulation results,it can be seen that the six parameters are important social network characteristics that affect the pre-hospital delay of stroke patients,including: betweeness centrality,effective size,network constraint,network efficiency,people around immediately seek medical help when the patient has symptoms.According to the characteristics of the social network,the order of the influence on patients’ medical treatment is as follows: effective size,network efficiency,network constraint,percentage kin,people around immediately seek medical help when the patient has symptoms,and betweeness centrality.Conclusion1.Six social network characteristics,such as betweeness centrality,effective size,network constraint,network efficiency,percentage kin and people around immediately seek medical help when the patient has symptoms,are all important social network characteristics that affect the pre-hospital delay of stroke patients.The results show that effective size,percentage kin and people around immediately seek medical help when the patient has symptoms are protective factors of prehospital delay.Betweeness centrality,network constraint and network efficiency are the risk factors of prehospital delay.2.Through the simulation quantitative results,according to the characteristics of the social network,the order of the influence on patients’ medical treatment is as follows:effective size,network efficiency,network constraint,percentage kin,people around immediately seek medical help when the patient has symptoms,and Betweeness centrality.