Parvareh Maryam, Karimi Asrin, Rezaei Satar, Woldemichael Abraha, Nili Sairan, Nouri Bijan, Nasab Nader Esmail
1Social Determinants of Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran.
2Research Center for Environmental Determinants of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Burns Trauma. 2018 Mar 9;6:9. doi: 10.1186/s41038-018-0111-6. eCollection 2018.
Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran.
A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package.
A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0), and SARIMA (1, 1, 1) (0, 0, 1), respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January).
Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.
道路交通事故是常见事件，会给受害者造成高强度伤害，并对社会成员产生直接影响。伊朗是道路交通事故发生率最高的国家之一。本研究的目的是对伊朗库尔德斯坦省导致受伤的道路交通事故模式进行建模。
进行了一项时间序列分析，以描述和预测库尔德斯坦省导致受伤的道路交通事故的频率。伤害被分为三个不同的组，分别与汽车乘客、摩托车手和行人道路交通事故伤害有关。使用Box-Jenkins时间序列分析对2009年3月至2015年2月的伤害观测数据应用自回归积分滑动平均（ARIMA）和季节性自回归积分滑动平均（SARIMA）进行建模，并预测长达24个月后的事故（2017年2月）。分析使用R-3.4.2统计软件包进行。
共观察到5199起行人事故、9015起摩托车手事故和28906起汽车乘客事故。观察到的汽车乘客、摩托车手和行人事故伤害的平均（标准差）数量分别为每年401.01（标准差32.78）、123.70（标准差30.18）和71.19（标准差17.92）。汽车乘客、摩托车手和行人伤害模式的最佳模型分别为ARIMA（1,0,0）、SARIMA（1,0,2）（1,0,0）和SARIMA（1,1,1）（0,0,1）。摩托车手和行人伤害呈现季节性模式，高峰在夏季（8月）。摩托车手和行人伤害的最低频率出现在深秋和初冬（12月和1月）。
我们的研究结果表明，观察到的摩托车手和行人伤害具有季节性模式，这可以通过气温随时间的变化来解释。这些发现呼吁在高风险时期密切监测事故，以控制和降低伤害发生率。
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