Gui Xi Zhang1, Joe King Man Fan1,2, Fion Siu Yin Chan1,2, Gilberto Ka Kit Leung1,2, Chung Mao Lo1,2, Yi Min Yu3, Hong Zhang3, Susan I Brundage4, Jan O Jansen5. 1. Department of Surgery, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China. 2. Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. 3. Shenzhen "120" Emergency Medical Services Center, Shenzhen, China. 4. Centre for Trauma Sciences, Queen Mary University of London, London, UK. s.brundage@qmul.ac.uk. 5. Departments of Surgery and Intensive Care Medicine, Aberdeen Royal Infirmary and Health Services Research Unit, University of Aberdeen, Aberdeen, UK. jan.jansen@abdn.ac.uk.
Abstract
BACKGROUND: The city of Shenzhen, China, is planning to establish a trauma system. At present, there are few data on the geographical distribution of incidents, which is key to deciding on the location of trauma centres. The aim of this study was to perform a geographical analysis in order to inform the development of a trauma system in Shenzhen. METHODS: Retrospective analysis of trauma incidents attended by Shenzhen Emergency Medical Services (EMS) in 2014. Data were obtained from Shenzhen EMS. Incident distribution was explored using dot and kernel density estimate maps. Clustering was determined using the nearest neighbour index. The type of healthcare facilities which patients were taken to was compared against patients' needs, as assessed using the Field Triage Decision Scheme. RESULTS: There were 49,082 recorded incidents. A total of 3513 were classed as major trauma. Mapping demonstrates that incidents predominantly occurred in the western part of Shenzhen, with identifiable clusters. Nearest neighbour index was 0.048. Of patients deemed to have suffered major trauma, 8.5% were taken to a teaching hospital, 13.6% to a regional hospital, 42.6% to a community hospital, and 35.3% to a private hospital. The proportions of Step 1 or 2 negative patients were almost identical. CONCLUSION: The majority of trauma patients, including trauma patients who are at greater likelihood of severe injury, are taken to regional and community hospitals. There are areas with identifiable concentrations of volume, which should be considered for the siting of high-level trauma centres, although further modelling is required to make firm recommendations.
BACKGROUND: The city of Shenzhen, China, is planning to establish a trauma system. At present, there are few data on the geographical distribution of incidents, which is key to deciding on the location of trauma centres. The aim of this study was to perform a geographical analysis in order to inform the development of a trauma system in Shenzhen. METHODS: Retrospective analysis of trauma incidents attended by Shenzhen Emergency Medical Services (EMS) in 2014. Data were obtained from Shenzhen EMS. Incident distribution was explored using dot and kernel density estimate maps. Clustering was determined using the nearest neighbour index. The type of healthcare facilities which patients were taken to was compared against patients' needs, as assessed using the Field Triage Decision Scheme. RESULTS: There were 49,082 recorded incidents. A total of 3513 were classed as major trauma. Mapping demonstrates that incidents predominantly occurred in the western part of Shenzhen, with identifiable clusters. Nearest neighbour index was 0.048. Of patients deemed to have suffered major trauma, 8.5% were taken to a teaching hospital, 13.6% to a regional hospital, 42.6% to a community hospital, and 35.3% to a private hospital. The proportions of Step 1 or 2 negative patients were almost identical. CONCLUSION: The majority of traumapatients, including traumapatients who are at greater likelihood of severe injury, are taken to regional and community hospitals. There are areas with identifiable concentrations of volume, which should be considered for the siting of high-level trauma centres, although further modelling is required to make firm recommendations.
Authors: Jan O Jansen; Jonathan J Morrison; Handing Wang; Shan He; Robin Lawrenson; Marion K Campbell; David R Green Journal: J Trauma Acute Care Surg Date: 2015-05 Impact factor: 3.313
Authors: Charles C Branas; Ellen J MacKenzie; Justin C Williams; C William Schwab; Harry M Teter; Marie C Flanigan; Alan J Blatt; Charles S ReVelle Journal: JAMA Date: 2005-06-01 Impact factor: 56.272
Authors: Christopher M Peach; Jonathan J Morrison; Amy N Apodaca; Gerry Egan; Henry G Watson; Jan O Jansen Journal: Surgeon Date: 2013-02-09 Impact factor: 2.392
Authors: Jan O Jansen; Jonathan J Morrison; Handing Wang; Robin Lawrenson; Gerry Egan; Shan He; Marion K Campbell Journal: J Trauma Acute Care Surg Date: 2014-04 Impact factor: 3.313
Authors: Gui-Xi Zhang; Gilberto Ka Kit Leung; Chung Mau Lo; Richard Kwong-Yin Lo; John Wong; Ronald V Maier; Eileen M Bulger; Joe King Man Fan; Xiao-Bing Fu Journal: Mil Med Res Date: 2021-11-13