Predictors of fatal outcomes in pedestrian accidents in Tabriz Metropolis of Iran: Application of PLS-DA method

<b>Objectives:</b> Road traffic deaths in walking pedestrians are a global public health problem. Considering that in Iran pedestrians have a high proportion of deaths caused by traffic accidents, the objective of the present study was to investigate mortality rate and related factors of fatal injury in pedestrian crashes in Tabriz Metropolis of Iran as the largest and most populous city of the northwest of Iran. <b>Methods:</b> The design of this study is case-control based on police and Forensic Medicine Organization data. All registered fatal pedestrian crashes in Tabriz Metropolis from 2014 to 2015 (146 cases) were included in the study as the case group. Also, 292 pedestrians (the ratio of cases to controls was 1:2) with non-fatal crashes were considered as the control group. Due to high dimensional data and multicollinearity issue, Partial least squares discriminant analysis (PLS-DA) was used for data analysis. Importance of the variables was determined by the VIP (Variable Importance in the Projection) index. Performance of the model was assessed by using training and test set validation method. The area under the ROC curve (AUC) and classification error rates were calculated for the test set. R software version 3.5.1 (mixOmcs packages) was used for data analysis. <b>Results:</b> According to the results of PLS-DA, the most important variables related to fatal outcome in pedestrian crashes with VIP &gt; 1 were: pedestrian age (positive effect), type of vehicle (light machinery with a negative effect), kind of vehicle plate (private plate with a negative effect), season of accident occurrence (winter season with a positive effect), type of driver’s licenses (Class A with a positive effect), pedestrian gender (male with a positive effect) and Fault of Pedestrian (At-fault with a positive effect). The overall accuracy for the fitted model and AUC were 0.77 and 0.79, respectively. <b>Conclusions:</b> The results show that predictors of a fatal outcome in pedestrian accidents in Tabriz can be attributed to the pedestrian characteristics (which notably account for differences in vulnerability in case of an accident), the car and driver features, and weather (which may all notably influence the amount of energy involved in the collision, through the car mass, speed, and conditions delaying the braking response or reducing the braking effectiveness). Regarding the statistical method, the PLS-DA is a powerful method which can be used to analyze high dimension data with multicollinearity issue.

**研究目的**：道路交通事故中的行人死亡是全球性公共卫生问题。考虑到伊朗行人因交通事故致死的占比颇高，本研究旨在以伊朗西北部规模最大、人口最多的大不里士都会区为研究区域，探究该区域行人碰撞事故中的死亡率及致死性损伤相关影响因素。**研究方法**：本研究采用基于警方与法医组织数据的病例对照研究设计。纳入2014至2015年大不里士都会区所有登记在册的行人致死碰撞事故共146例作为病例组；同时选取292名遭遇非致命碰撞事故的行人作为对照组，病例与对照之比为1:2。鉴于数据存在高维度与多重共线性问题，本研究采用偏最小二乘判别分析（Partial Least Squares Discriminant Analysis，PLS-DA）进行数据分析。通过投影变量重要性（Variable Importance in the Projection，VIP）指标衡量各变量的重要性。模型性能采用训练集与测试集验证法进行评估，并计算测试集的ROC曲线下面积（Area Under the ROC Curve，AUC）与分类错误率。数据分析采用R软件版本3.5.1及mixOmcs数据包完成。**研究结果**：偏最小二乘判别分析结果显示，与行人碰撞事故致死结局相关且VIP值大于1的最重要变量包括：行人年龄（正向影响）、车辆类型（轻型机械类车辆呈负向影响）、车牌类型（私家车牌照呈负向影响）、事故发生季节（冬季呈正向影响）、驾驶员驾照类型（A类驾照呈正向影响）、行人性别（男性呈正向影响）以及行人过错方（有过错方呈正向影响）。拟合模型的整体准确率与ROC曲线下面积分别为0.77与0.79。**研究结论**：研究结果表明，大不里士地区行人交通事故致死结局的预测因素可归结为三类：行人特征（其显著影响事故发生时的伤亡易感性差异）、车辆与驾驶员特征，以及气象因素。上述因素均可通过车辆质量、行驶速度、制动响应延迟时长或制动效能降低等途径，显著影响碰撞过程中的能量释放水平。就统计方法而言，偏最小二乘判别分析是一种适用于存在多重共线性问题的高维度数据分析的高效分析方法。