Fatemeh Jahanjoo; Homayoun Sadeghi-Bazargani; Seyyed Teymoor Hosseini; Mina Goletsani; Mahdi Rezaei; Kavous Shahsavari; Hamid Soori; Mohammad asghari Jafarabadi
Volume 11, Issue 3 , July 2023, , Pages 125-131
Abstract
Objective: To determine the causal relationship between aging and nighttime driving and the odds of injuryamong elderly drivers.Methods: In this cross-sectional study, 5460 car accidents were investigated from 2015 to 2016. The data wereextracted from the Iranian Integrated Road Traffic Injury Registry ...
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Objective: To determine the causal relationship between aging and nighttime driving and the odds of injuryamong elderly drivers.Methods: In this cross-sectional study, 5460 car accidents were investigated from 2015 to 2016. The data wereextracted from the Iranian Integrated Road Traffic Injury Registry System. Pedestrian accidents, motorcyclecrashes, and fatalities were excluded from the study. To account for major confounders, Bayesian-LASSO, andtreatment-effect cutting-edge approaches were used.Results: Overall, 801 injuries (14.67%) were evaluated. The results of the univariable analysis indicated thataging and nighttime had adverse effects on the odds of road traffic injuries (RTIs), even after adjusting forthe effect of other variables, these effects remained statistically significant. According to a newly developedapproach, the overall effects of aging and nighttime were significantly and directly correlated with the odds ofbeing injured for older adults (both p<0.001). Our findings indicated that drivers over 75 years old experienced23% higher injury odds (OR=1.23, 95% CI:1.11 to 1.39; p<0.001), while driving at night increased the odds by1.78 times (OR=1.78, 95% CI:1.51 to 1.83; p<0.001).Conclusion: Aging and nighttime driving are significant risk factors for RTIs among elderly drivers. Thishighlights the importance of implementing targeted interventions to enhance road safety for this vulnerablepopulation. Furthermore, the use of advanced Bayesian-LASSO and treatment-effect statistical methodshighlights the importance of utilizing sophisticated methodologies in epidemiological research to effectivelycapture and adjust for potential confounding factors.
Siyamak Tahmasebi; Seyyed Mohammad Hosein Javadi; Tahereh Azari Arghun; Forough Edrisi; Alireza Tajlili
Volume 8, Issue 1 , January 2020, , Pages 19-26
Abstract
Objective: To identify the human factors contributing to traffic accidents with a special focus on psychosocial factors amongst young girls of Tehran, Iran.Methods: In a descriptive study conducted in Tehran, Iran in 2013, 108 girls aged 18-24 were enrolled by using a stratified cluster sampling ...
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Objective: To identify the human factors contributing to traffic accidents with a special focus on psychosocial factors amongst young girls of Tehran, Iran.Methods: In a descriptive study conducted in Tehran, Iran in 2013, 108 girls aged 18-24 were enrolled by using a stratified cluster sampling method. Participants filled a wide range of validated questionnaires about traffic psychology.Results: The developed psychological model about behaviors of drivers’ factors as well as agreeable and aggressive personality trait with B coefficient of 0.25% and 0.37% were able to predict violation, driving style, perception of police laws, and off hook scheme and the mistrust with B coefficient of 0.33%, 0.23% and 0.28% in the level of 0.1 were able to predict violations and lapses of sample group, respectively. Extroversion with B coefficient of 0.27% also predicted unintentional violations of girls. B coefficient for perception of police laws was 0.22%. This was 0.25% for openness to experiences. Concerning driving accidents, the perception ofpolice rules has the highest predictability.Conclusion: According to the results of the current research amongst girls in Tehran, a gender-sensitive interventional model can be designed for reduction of traffic accidents for this population group.
Payam Roshanfekr; Mohammad-Reza Khodaie-Ardakani; Hossein Malek Afzali Ardakani; Homeira Sajjadi
Volume 7, Issue 1 , January 2019, , Pages 60-66
Abstract
Objective: To determine the prevalence and socio-economic disparity among victims with disabilities caused by RTAs in Iran as country with a high rate of accidents.Method: The source of data was the Iranian Multiple Indicator Demographic and Health Survey, a nationwide cross-sectional study. The ...
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Objective: To determine the prevalence and socio-economic disparity among victims with disabilities caused by RTAs in Iran as country with a high rate of accidents.Method: The source of data was the Iranian Multiple Indicator Demographic and Health Survey, a nationwide cross-sectional study. The sampling framework was based on the population and housing census for Iran in 2006. Provincial samples ranged from 400 to 6,400 households. The target sample was 3,096 clusters consisting of 2,187 urban and 909 rural clusters. In the present study, all but a few indicators are reported at provincial levels. Mortality indicators, accident and disability rates, low birth weight rate and young age at marriage rates are presented at the national level only. Logistic regression was performed to investigate the individual and family factors influencing RTAs that lead to disability in Iran.Results: The period prevalence (12 months) of road traffic accident disabilities (RTADs) in the total population of 111415 was 30.52 (95% CI: 21.13.41.64) per 100,000 individuals. Among those who had been injured during the year leading up to the study, the proportion of disabilities caused by RTAs was 31.67 (95% CI; 8.51.54.97) per 1000 pedestrians, 20.99 (95% CI: 13.37.30.75) per 1000 motorcyclists, 18.64 (95% CI: 7.71.29.57) per 1000 vehicle drivers. Multivariate logistic regression analysis showed that the risk of RTADs differed significantly in relation to age (AOR 50-59 vs. 0-9=10. 78, p-value:0.05); activity status (AOR unemployed vs. employed=4.72, p-value:0.001) and family income (AOR q2 vs. q1=0.37, p-value:0.048) of the victim.Conclusion: In addition to the risks associated with socio-economic groups, particularly vulnerable groups, RTADs have consequences which can lead to further marginalization of individuals, can affect their quality of life and damage the community as a whole.