上海市居民交通伤害流行特征及相关因素分析

目的 分析上海市交通伤害的流行特征,探讨受教育程度、经济收入及年龄等对交通伤害的影响。方法 采用分层多阶段概率比例整群抽样方法抽取2011年上海市常住居民61 786人进行入户调查;收集1992-2012年上海市生命统计年鉴居民损伤数据及其相关社会人口资料。结果 上海市交通伤害死亡率从1992年的10.73/10万下降到2012年的9.32/10万。总受伤率为101.16/万。家庭月人均收入<1 000元的受伤率约为≥5 000元的4.37倍。在受教育程度为小学及以下的人群中,交通伤害的受伤率和死亡率是146.88/万和27.05/10万,为大专以上文化程度者的6.41倍和2.42倍。5~9岁年龄组死亡率在所有儿童中居于首位,30~34岁和55~59岁劳动力人口的受伤率增加幅度最大。多因素分析表明经济条件越差、受教育程度越低和年龄越大交通伤害越容易发生。结论 干预重点人群应为低收入、低文化程度人群及老年人。亟需实施有针对性的防控措施,提供均等化的公共服务产品,并建立伤害大数据的共享应用平台。

Epidemiologic features and relevant factors of traffic injuries in Shanghai,China

Objective To investigate the traffic injuries epidemiologic features and its relevant factors including income, education level and age in Shanghai, China. Methods The multi-stage stratified probability proportion to size cluster sampling method was conducted to collect the traffic injuries incidence data of Shanghai in 2011 and carrying out indoor investigation on 61 786 residents. Mortality and related socio-demographic data from 1992 to 2012 were obtained from local vital registration system and Shanghai municipal statistics bureau. Multivariate logistic model was used to analyse the independent effect of various variables. Results The incidence of traffic injury was 101.16/10 000. The mortality of traffic injury decreased from 10.73/100 000 in 1992 to 9.32/100 000 in 2012. The incidence of traffic injury among the people with an average monthly income no more than 1 000 CNY was 4.37 times than those no less than 5 000 CNY. Those educational level under the primary school people's mortality and incidence were 27.05/100 000 and 146.88/10 000 respectively, which were 6.41 times and 2.42 times compared to those with college degree or above. The mortality of 5-9 years old age group was the highest among 0-14 years old children. The age group of 30-34 and 55-59 had the highest traffic injury incidence increment. Multivariate regression analysis showed that lower income, lower education and elder age could lead to higher risk of traffic injuries. Conclusions Lower income, lower education level and older in age are main risk factors of traffic-related injuries and those people should be the target intervention populations. It is urgent to establish a big shared injury data application platform for better implement of targeted prevention and control measures and equalized public health services supplying.