title: Road traffic injury in China: a review of national data sources.
abstract: OBJECTIVE
  Road traffic injury (RTI) has become one of the leading causes of deaths in China, yet numbers on road traffic deaths are often inconsistent. This study sought to systematically review 4 national-level data sources that can be used to estimate burdens of RTI, including mortality, injury, and crashes in China.
  
  
  METHODS
  We conducted structured literature reviews in PubMed, using combined key words of injury or fatality or injury surveillance and traffic and China in order to identify relevant studies (in both English and Chinese) and data sources. We also conducted interviews and hosted seminars with key researchers from the Chinese Center for Disease Control and Prevention (Chinese CDC) to identify potential useful data sources for injury surveillance. We then extracted key information from publicly available reports of each data source.
  
  
  RESULTS
  Four national-level data sources were reviewed and compared: Ministry of Health-Vital Registration (MOH-VR) System, Chinese CDC-Disease Surveillance Points (DSP), Chinese CDC-National Injury Surveillance System (NISS), and police reports. Together they provide a complementary yet somewhat contradictory epidemiological profile of RTIs in China. Estimates on road traffic fatalities obtained from MOH-VR and police reports are often used by researchers and policymakers, whereas DSP and NISS, both with great merits, have virtually not been used for RTI research. Despite the well-documented problems of underreported deaths with both MOH-VR and DSP, estimated road traffic deaths from both systems were 3 times those reported by the police.
  
  
  CONCLUSIONS
  As the foundation of injury prevention, national-level data sources and surveillance systems were reviewed in the study. Existing data infrastructures present the Chinese government a great opportunity to strengthen and integrate existing surveillance systems to better track road traffic injury and fatality and identify the population at risk.
authors.affiliation: Johns Hopkins International Injury Research Unit, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA. sma@jhsph.edu
authors.forename: Sai
authors.initials: S
authors.lastname: Ma
authors.forename: Qingfeng
authors.initials: Q
authors.lastname: Li
authors.forename: Maigeng
authors.initials: M
authors.lastname: Zhou
authors.forename: Leilei
authors.initials: L
authors.lastname: Duan
authors.forename: David
authors.initials: D
authors.lastname: Bishai
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality; D002681:China / Q000453:epidemiology; D003625:Data Collection; D006801:Humans; D011159:Population Surveillance / Q000379:methods*; D012307:Risk Factors; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000401:mortality