International journal of general medicine · 2026
Multifactor Analysis of Electric Bicycle-Related Road Traffic Injuries in Wenzhou and Construction of a Prediction Model Based on Data from a Tertiary Trauma Center.
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Ye Ting, Jin Qinghe, Meng Weiyang, Shen Xiuxiu, Huang Xiu
Аннотация
OBJECTIVE: To investigate the multifactorial determinants of electric bicycle-related road traffic injuries in Wenzhou based on data from a tertiary hospital trauma center, and to develop a risk prediction model for severe injury.
METHODS: We retrospectively analyzed data from 249 electric bicycle riders treated at The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University between January 2020 and June 2023. Demographic characteristics, injury severity, safety-related factors, environmental factors, accident-related factors, and vehicle-related factors were collected. Univariate and multivariate logistic regression analyses were performed to identify independent variables associated with injury severity. A risk prediction model was constructed and visualized using a nomogram, and its discrimination and predictive performance were evaluated using the Hosmer-Lemeshow goodness-of-fit test and receiver operating characteristic (ROC) curve analysis.
RESULTS: Among the 249 enrolled riders (168 males, 81 females; mean age: 33.2 ± 12.2 years), 70 (28.1%) sustained severe injuries. Multivariate analysis revealed that junior high school education or above was protective (OR=0.513), while riding speed >25 km/h (OR=3.182), poor road lighting (OR=2.777), collision with a motor vehicle (OR=23.377), and accidents on unpaved roads (OR=5.888) were significant risk factors (all P<0.05). The model demonstrated good calibration (Hosmer-Lemeshow test P=0.879) and discrimination, with an ROC curve area of 0.873 (95% CI: 0.823-0.922), yielding 71.43% sensitivity and 87.71% specificity.
CONCLUSION: Rider education level, riding speed, road lighting, collision counterpart, and road type are significantly associated with the severity of electric bicycle-related road traffic injuries. The model constructed from these variables demonstrates good discriminative ability and has favorable performance for predicting severe injuries in electric bicycle riders.
METHODS: We retrospectively analyzed data from 249 electric bicycle riders treated at The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University between January 2020 and June 2023. Demographic characteristics, injury severity, safety-related factors, environmental factors, accident-related factors, and vehicle-related factors were collected. Univariate and multivariate logistic regression analyses were performed to identify independent variables associated with injury severity. A risk prediction model was constructed and visualized using a nomogram, and its discrimination and predictive performance were evaluated using the Hosmer-Lemeshow goodness-of-fit test and receiver operating characteristic (ROC) curve analysis.
RESULTS: Among the 249 enrolled riders (168 males, 81 females; mean age: 33.2 ± 12.2 years), 70 (28.1%) sustained severe injuries. Multivariate analysis revealed that junior high school education or above was protective (OR=0.513), while riding speed >25 km/h (OR=3.182), poor road lighting (OR=2.777), collision with a motor vehicle (OR=23.377), and accidents on unpaved roads (OR=5.888) were significant risk factors (all P<0.05). The model demonstrated good calibration (Hosmer-Lemeshow test P=0.879) and discrimination, with an ROC curve area of 0.873 (95% CI: 0.823-0.922), yielding 71.43% sensitivity and 87.71% specificity.
CONCLUSION: Rider education level, riding speed, road lighting, collision counterpart, and road type are significantly associated with the severity of electric bicycle-related road traffic injuries. The model constructed from these variables demonstrates good discriminative ability and has favorable performance for predicting severe injuries in electric bicycle riders.