Prediction for Severe Traffic Accident Injury in Korea: a Nationwide Database Study

Research Objectives: To develop a prediction model for severe traffic injuries in the entire population using a nationwide database for traffic accident in Korea

Design: A retrospective cohort study from 2017 to 2021, based on the Traffic Accident Analysis System in Korea, which contains 35 categories of data that characterize traffic accident obtained from various organizations including police and insurance companies.

Setting: General community

Participants: We collected detailed information on each traffic accident for drivers over the age of 40 that occurred between 2017 and 2021 (n=741,794).

Interventions: not applicable

Main Outcome Measures: We aimed to predict the occurrence of severe traffic injury where victims experience either death or severe injuries. Severe injuries were defined as requiring hospitalization for more than three weeks as a result of a traffic accident. We developed prediction models for the total population.

Results: The proportion of severe traffic injury was 22.1% (n=163,816) out of entire traffic accidents. The multivariate logistic model with stepwise elimination reveals that independent risk factors for severe traffic accident as follows; driver’s sex, years since obtaining the driver's license, type of driving license, year of the accident, region of traffic accident, weather condition, type of road, type of offender’s vehicle, type of victim’s vehicle, presence of violence with regulations. We used a nomogram to develop a risk prediction model. The areas under the curves of the model was 0.736.

Conclusions: The result of this study can serve as a baseline data for predicting severe traffic injury occurrence in individuals with diseases or disabilities. The prediction model in this study can contribute to the prevention of severe traffic accidents by mitigating and reducing risk factors.

Author(s) Disclosures: This study was supported by a grant from the Ministry of Land, Infrastructure and Transport (MOLIT) Research Fund (NTRH RF-2023001) and the Korean National Police Agency (PR09-02-000-22).