Analysis of factors affecting fatality risk in road traffic injury
10.13213/j.cnki.jeom.2021.21146
- VernacularTitle:道路交通伤害死亡风险的影响因素分析及预测
- Author:
Mengshuang LIU
1
,
2
;
Kezhi JIN
1
,
2
;
Ya WANG
1
,
2
;
Jiali YING
3
;
Chen YANG
3
Author Information
1. School of Public Health/Key Laboratory of Public Health and Safety of Ministry of Education, Fudan University, Shanghai 200032, China
2. Division of Occupational Population Health, Pudong Institute of Preventive Medicine, Fudan University, Shanghai 200136, China.
3. Department of Cancer and Injury Prevention, Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai 200136, China.
- Publication Type:Investigation
- Keywords:
road traffic injury;
logistic regression;
nomogram;
risk prediction
- From:
Journal of Environmental and Occupational Medicine
2021;38(11):1224-1230
- CountryChina
- Language:Chinese
-
Abstract:
Background In recent years, road traffic injury (RTI) has become a serious public health problem in China, and the factors affecting deaths caused by RTI are also complicated. Objective This study is designed to identify factors of RTI fatality risk and establish a road user fatality risk prediction model. Methods The data of traffic accident casualties in Pudong New Area of Shanghai from 2010 to 2016 were collected retrospectively, and the related impact factors of RTI were collected. Logistic regression was used to screen the selected factors of RTI fatality risk. A nomogram of RTI fatality risk was established, the consistency and accuracy of the model was evaluated by C-index and bootstrap internal verification, and a sensitivity analysis was also conducted. Results A total of 3521 casualties in traffic accidents were included in the study. The logistic regression results showed that age of victims, medical rescue distance, road type, transport means, injured body part, time of accident, and weekday/weekend affected RTI death risk (P<0.05). The nomogram model for RTI death risk showed that the most affecting factors were injured body part (especially head and neck injury), followed by age, transportation means, medical rescue distance, road type, time of accident, and weekday/weekend. The C-index of the model was 0.790, indicating high prediction accuracy and good fitness. The nomogram model for RTI death risk of head and neck injury showed that the score scales of all included factors expanded, the most prominent (most affecting) one was age; the RTI fatality risk of different road types changed, where urban road became the most dangerous road type; in addition, walking was the transportation means with the greatest risk of RTI fatality from head and neck injury. The results of the sensitivity analysis on accidents with varied casualties confirmed the robustness of the model. Conclusion The road user fatality risk of RTI is affected by many factors. As a simple tool to predict fatality risk of RTI, the nomogram based on logistic regression has certain reference significance for road traffic safety.
