摘要
目的 对道路交通伤害(road traffic injury,RTI)的发生与气象因素之间的相关关系进行探索,为完善气象预报预警信息内容、制定天气—响应交通管理策略、减少气象因素对交通伤害的不利影响提供科学参考.方法 从汕头市某三甲综合性医院收集2002-2012年近11年间道路交通伤害病例,以每日发生例数进行统计;从中国气象科学数据共享服务网收集相同时间段的汕头市气象监测数据,按日平均温度(℃)、降雨量(mm)、相对湿度(％)、气压(kPa)、风速(m/s)和日照时数(h)进行整理;使用SPSS 19.0软件,根据各指标数据的正态性检验结果,采用Pearson或Spearman相关系数(rp或rs),对每日RTI病例与气象因素的相关关系进行分析,并通过变量的时间序列趋势图对各变量随时间的变化趋势进行描述.结果 每日RTI发生例数与日均温度、光照时数呈正相关关系(rs=0.042,P=0.012和rs=0.038,P=0.024);与日降雨量和相对湿度呈负相关关系(rp=-0.034,P=0.044和rp=-0.036,P=0.030).与其他气象因素之间的相关关系无统计学意义.结论 温度对道路交通伤害具有负面影响,而降雨量对RTI的发生可能具有一定的保护效应.
Objective The study aimed to explore the relationshiop between metorological factors and occurrences of road traffic injury (RTI ),and to provide scientific information for improving weather forecasting and early warning ,system,developing weathe-responsive traffic management strategies,and reducing adverse effects of meteorologicalfactors on RTI,Methods Data of daily RTI cases during 2002-2012 were collected from a tertiary hospital in Shantou,Guangdong.Local meteorological data in the same time period were obtained from the Chinese Meteorological Data Sharing Service System,including daily average temperatur(℃),rainfall(mm),relative humidity(%),pressure (kPa),wind speed (m/s) and sunshine hours (h),Besed on results of normality tests,Pearson or Spearman correlation coefficient (r,or,r)was determined to estimate the associations between meteorological factors and the number of daily RTI cases,and the long-term trends of relevant variales were described by plotting scstter of time series of the variables,All analyses were conducted using SPSS19.0 software,Results Daily average temperature and sunshine hours were significantly and positively correlated with the number of daily RTI cases,while rainfall and relative humidity were significantly but negatively correlated with daily RTI.Conelusion Temperature has an advcrsc impact on RTI ,whereas rainfall shows a protective effecton RTI.
出处
《伤害医学（电子版）》
2013年第2期26-31,共6页
Injury Medicine(Electronic Edition)
关键词
交通伤害
气候因素
相关分析
变化趋势
Road traffic injury
Climatic factor
Correlation analysis
Long-term trends
