Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and 79,664 Accidents

Abstract

Predictive factors for fatal traffic accidents have been determined, but not addressed collectively through a predictive model to help determine the probability of mortality and thereby ascertain key points for intervening and decreasing that probability. Data on all road traffic accidents with victims involving a private car or van occurring in Spain in 2015 (164,790 subjects and 79,664 accidents) were analyzed, evaluating 30-day mortality following the accident. As candidate predictors of mortality, variables associated with the accident (weekend, time, number of vehicles, road, brightness, and weather) associated with the vehicle (type and age of vehicle, and other types of vehicles in the accident) and associated with individuals (gender, age, seat belt, and position in the vehicle) were examined. The sample was divided into two groups. In one group, a logistic regression model adapted to a points system was constructed and internally validated, and in the other group the model was externally validated. The points system obtained good discrimination and calibration in both the internal and the external validation. Consequently, a simple tool is available to determine the risk of mortality following a traffic accident, which could be validated in other countries.

Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study

“…Studies [7,8,11,13] on road accidents in Spain, India, and the United States of America (Washington) showed different approaches and results. Palazón-Bru [10] studied Spain road accident data from 2015, and considered variables associated with the accident, the vehicle, and individuals. This information could be used by both police authorities and health services to make predictions and determine where to undertake possible interventions to reduce death risk.…”

“…Identify patterns to take appropriate measures to reduce the risk of loss of lives and occurrence of accidents on roads [10] Multivariable analysis with variables associated with accidents (weekend, time, number of vehicles, road, brightness, and weather), vehicle (type and age of vehicle, and other types of vehicles in the accident), and individuals (gender, age, seat belt, and position in the vehicle)…”

“…A large number of road accidents occur due to various factors that directly or indirectly affect conditions on the road for drivers, passengers, and pedestrians [9]. Factors such as gender and age, or environmental factors such as low brightness (dawn or dusk) and adverse weather conditions [10], influence the world's global number of road accidents.…”