Related Risk Factors for Injury Severity of E-bike and Bicycle Crashes in Hefei

Abstract

Objective: To explore the related risk factors of injuries caused by e-bike and bicycle crashes in Hefei, Anhui.

Methods: Between June 2009 and June 2011, the records of injuries were triggered by e-bike and bicycle crashes in Hefei maintained by 105th Hospital of PLA. A form was designed to document patient age, gender, road user category (driver, passenger, pedestrian), safety factors (safety devices present, speed, traffic violations), environmental factors (time of trauma, light conditions, road surface), crash mode, impact type, and vehicle type.

Results: Of the 205 cases, 108 were female and 97 were male. One hundred forty-six patients suffered injuries due to e-bike accidents and 59 due to bicycle accident. The chi-squared test compared distribution of categorical variables suggested that age (P =.0250), road user category (P =.0278), traffic rule violations (P =.0132), crash mode (P =.0027), impact type (P =.0019), and vehicle type (P =.0219) are related to the severity of injuries caused by e-bike/bicycle crashes in Hefei. The multiple-factor nonconditional logistic regression analysis showed that injury severity is the most commonly sustained within the vehicle type (odds ratio [OR] = 14.418; 95% confidence interval [CI], 4.680–44.418), followed by crash mode (OR = 11.556; 95% CI, 4.430–30.142), traffic rule violations (OR = 4.735; 95% CI, 1.934–11.594), and age (OR = 2.910; 95% CI, 1.213–6.979).

Conclusions: With the study of e-bike/bicycle crashes in Hefei, primary identification of the risk factors for the traffic injuries is obtained. These findings are important in decision making regarding preventive measures.

Keywords:

• traffic accidents
• regression analysis
• e-bike
• bicycle

Acknowledgments

Date analysis and preparation of the article were sponsored by the major projects of the Chinese People's Liberation Army “125 program” No. AWSIIJ008 and technology program of 2011 Anhui No. 10020503085. We gratefully acknowledge Jian Fang, Jie Zhu, Donglai Lv, and local police officers for their data collection efforts.