Background and aims: Road traffic crash and its deaths and injuries are one of the main public health problems in all over the world, especially in Low and Middle-Income Countries (LMICs). Road traffic crashes resulting in deaths, physical and psychological problems, and economic costs damages families and communities. The number of road traffic deaths exceeded 1. 3 million in 2016. Road traffic injury is the eight leading cause of death for all ages, largely neglected though. There are several effective factors in road traffic crashes. The Socio-Economic Status (SES) are known as important factors related to health status, although its influence is not fully understood on different aspects of health. People who live in low socioeconomic status suffer from the disease and injuries two times more than others. SES is known as important factors related to health status. The results of the studies show that fatal and non-fatal injuries have an inverse relationship with SES. However, there is a lack of enough information about the effects of SES on road traffic crash patients and the related consequences. The incidence rate of road traffic injuries and its related deaths have a relationship with SES. The SES is the main predictor factor in different aspects of health. However, its effect on many aspects of health is not fully understood. Identifying the SES factors related to the consequences of road traffic crashes can provide a good opportunity for policymakers and managers to use preventive interventions in high-risk groups. Methods: This study uses an analytical cross-sectional design. The studied samples were road traffic crash patients referred to Pour-Sina hospital in the city of Rasht, Guilan province. The data were collected by the two researchers. The instrument consisted of two tools. The first was a checklist to collect the demographic information and the location of the injury, the type of injury, the severity of injury (based on Injury Severity Score), and the clinical outcome of the patients were obtained from the patients’ records. The second tool was a standardized questionnaire to examine socio-economic status. In case the injured people had died or were unable to interview due to the severity of the injuries, the interview was conducted with one of the close relatives (father, mother, brother or sister) after obtaining informed consent. In order to determine the SES, the principal components analysis was used. To extract the factor/factors from the variables, the Varimax rotation method was used. In this study, the Eigenvalue greater than 1 was chosen. After determining the factor, the variables present in each factor were identified. To obtain the main socio-economic status factors the method of principal component analysis was used. To assess the interest of the implementation of the principal component analysis on data, Bartlett’ s sphericity test and the KMO index were used. The main SES factors were determined and in order to assess the relationship between these factors and death and severity of injuries related to road traffic crashes, the logistic regression with the Backward-LR method was used. The analysis was adjusted on the variable of age and sex of patients. For the severity of the injury, the ISS scale was grouped (ISS> 15, ISS = <15) and considered as the dependent variable in the Logistic Regression model. The ISS scale above 15 is considered to be a severe injury. All analyses were performed using the SPSS software version 20. The significance level of the tests in this study was considered 0. 05. Results: In this study, 300 traumatic patients were recruited, of whom 234 patients (78%) were male. The mean age of injured patients was 34. 25 years old (19. 07). The ISS scale was grouped (ISS> 15, ISS = <15). The severe injury (ISS > 15) was observed among 245 (81. 7%) patients. Bartlett’ s sphericity test and the KMO index showed that there is a good correlation between the studied variables and thus using principal component analysis is feasible. The p-value for the Bartlett test was significant and the KMO index was greater than 0. 5. Some socio-economic status factors had a relationship with the consequences of death and the severity of the injury of patients. There were three factors which affected the consequence of road traffic crashes. The first factor includes the following variables; household cost, the education level of an injured person, and the education level of the mother. The second factor includes the variables of job, owning the mobile and being motorcyclist. The third factor includes the variables of income and father’ s job. The results of logistic regression analysis showed that factor 3 (family income and father’ s job) had a significant relationship with the outcome of traumatic death. For this factor, the odds ratios of 0. 45 (CI 95%; 0. 042-0. 83) for deaths and 0. 65 (CI 95%; 0. 45-0. 90) for the severity of injuries were obtained. The highest SES had the lowest deaths and injuries. There was a relationship between economic factors and the severity of the trauma, the economic-social third factor including variables of family income and father's occupation that was identified as an effective factor in the severity of trauma. The odds ratio for the third factor (household income and father's occupation) was 0. 68 (95% confidence interval: 0. 452-0. 908). Conclusion: The results of this study showed that social economic factors affect both the deaths and the severity of injuries. The results of this study showed that the third factor (family income and father's job) had a significant relationship with traumatic death. In other words, the mortality rate of road traffic crashes is high among families with a low level of socio-economic status. Considering the high rates of deaths and severe injuries caused by traffic accidents in Iran compared to other countries, it is necessary to consider economic and social factors will be considered as effective factors on deaths and injuries in road traffic policy-making and planning.