title: Pre-hospital delay and its associated factors in first-ever stroke registered in communities from three cities in China.
abstract: This study aimed to explore pre-hospital delay and its associated factors in first-ever stroke registered in communities from three cities in China. The rates of delay greater than or equal to 2 hours were calculated and factors associated with delays were determined by non-conditional binary logistic regression, after adjusting for different explanatory factors. Among the 403 cases of stroke with an accurate documented time of prehospital delay, the median time (interquartile range) was 4.00 (1.50-14.00) hours. Among the 544 cases of stroke with an estimated time range of prehospital delay, 24.8% of patients were transferred to the emergency department or hospital within 2 hours, only 16.9% of patients with stroke were aware that the initial symptom represented a stroke, only 18.8% used the emergency medical service and one-third of the stroke cases were not identified by ambulance doctors. In the multivariate analyses, 8 variables or sub-variables were identified. In conclusion, prehospital delay of stroke was common in communities. Thus, intervention measures in communities should focus on education about the early identification of stroke and appropriate emergency medical service (EMS) use, as well as the development of organized stroke care.
authors.affiliation: Department of Neuroepidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.|Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Department of Neuroepidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.|Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Xiaojuan
authors.initials: X
authors.lastname: Ru
authors.affiliation: Department of Neuroepidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.|Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Haixin
authors.initials: H
authors.lastname: Sun
authors.affiliation: Department of Neuroepidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.|Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.|National Office for Cerebrovascular Diseases (CVD) Prevention and Control in China, Beijing, China.
authors.forename: Hongmei
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Neuroepidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.|Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
authors.forename: Dongling
authors.initials: D
authors.lastname: Sun
authors.affiliation: Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.
authors.forename: Yunhai
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Shanghai Institute of Cerebral Vascular Diseases Prevention and Cure, Shanghai, China.
authors.forename: Jiuyi
authors.initials: J
authors.lastname: Huang
authors.affiliation: Department of Neurology, West China Hospital, Sichuan University, Chengdu, China.
authors.forename: Li
authors.initials: L
authors.lastname: He
authors.affiliation: Department of Neuroepidemiology, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.|Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.|National Office for Cerebrovascular Diseases (CVD) Prevention and Control in China, Beijing, China.
authors.forename: Wenzhi
authors.initials: W
authors.lastname: Wang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002681:China; D002947:Cities; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006786:Hospitals, Urban; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D010343:Patient Admission / Q000706:statistics & numerical data*; D020521:Stroke / Q000175:diagnosis*; D013997:Time Factors