Modeling Socio-economic Factors Affecting the Severity of Drivers’ Injuries in Traffic Accidents: Using the Logistic Regression

Introduction and purpose: The severity of accidents and mortality rate varies between countries with different socio-economic statuses. This study aimed to model the socio-economic factors affecting the severity of injuries in traffic accidents of drivers referred to Imam Khomeini Hospital in Urmia using logistic regression. Methods: This cross-sectional-analytical study included 250 drivers who had a traffic accident in 2020 and were referred to Imam Khomeini Medical Center in Urmia, Iran. For evaluating the relationship between socio-economic factors and the severity of injuries, the Injury Severity Score (ISS) scale was used to determine the severity of the injury, and logistic regression was used to model and study the relationship. The collected data were analyzed in SPSS software (version 16), and a p-value less than 0.05 was considered statistically significant. Results: The results showed a significant relationship between the high severity of the injury with, marital status (OR=0.43, P=0.029), level of education (OR=4.45, P=0.005), monthly household income (OR=5.40, P=0.037), car monetary value (OR=3.22, P=0.011), and the share of health care costs (OR=0.31, P=0.004). In contrast, there was no significant relationship between job and the high injury severity. Conclusion: The results showed a significant relationship between most of the socio-economic factors and the severity of the injury. It seems that paying attention to welfare and public health strategies, material and spiritual support for the recovery of patients, increasing the purchasing power of families, improving cultural views, specialized training, and upgrading the automotive industry may reduce accidents and the severity of injuries.