title: Field data: a study on trend and prediction of fatal traffic injuries prevalence in Shanghai.
abstract: OBJECTIVE
  The main objectives of the study are to analyze fatal traffic-injury trends in 1987-2003 in Shanghai and predict its prevalence in near future and provide scientific data for the local governmental decision on developing practical working methods on traffic-injury prevention and control.
  
  
  METHODS
  In this study, epidemiological method and Grey dynamic model GM (1,1) were introduced to analyze and forecast traffic-injury mortality rates respectively.
  
  
  RESULTS
  There was an apparent increasing trend of traffic-related injuries in Shanghai from 1987 to 2003 with the rate of growth in motorization. The average rates of annual increase are 3.59% in fatalities (from 7.78 per 100,000 population to 14.18 per 100,000 population) during the period. Pedestrians were the most common type of victims (29.6%), followed by bicyclists (25.1%), and motorcyclists (24.1%). Males accounted for the majority of all victims, over 69%. The population of high-school and lower high-school education level represented 66.4% victims of total road-traffic injuries. And if no special factors effect its development, the traffic fatalities would be up to 17.84 per 100,000 population in 2010, when calculating from equations we found and validated Y(t) = 359.90 x e0.027(t-1)-352.13, (t = 1, 2, ..., N) for Shanghai.
  
  
  CONCLUSIONS
  Our data indicate the risk of fatal traffic injuries has increased in recent years and will go on growing in the near future in Shanghai. The findings showed that Grey dynamic model GM (1,1) is eligible on the prediction and can be a tool for injuries forecasting, implementing effective policies, programs, and interventions for reducing traffic injuries in the big cities.
authors.affiliation: Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China. huanyi@scdc.sh.cn
authors.forename: Yan-Hong
authors.initials: YH
authors.lastname: Li
authors.forename: Yousif
authors.initials: Y
authors.lastname: Rahim
authors.forename: Wei
authors.initials: W
authors.lastname: Lu
authors.forename: Gui-Xiang
authors.initials: GX
authors.lastname: Song
authors.forename: Yu
authors.initials: Y
authors.lastname: Yan
authors.forename: Ding Zhou
authors.initials: DZ
authors.lastname: De
authors.forename: Sheng-Nian
authors.initials: SN
authors.lastname: Zhang
authors.forename: Shun-Fu
authors.initials: SF
authors.lastname: Zhou
authors.forename: Shao-Ming
authors.initials: SM
authors.lastname: Chen
authors.forename: Bing-Jie
authors.initials: BJ
authors.lastname: Yang
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality* / Q000517:prevention & control / Q000706:statistics & numerical data; D002681:China / Q000453:epidemiology; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015995:Prevalence; D014505:Urban Population