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.
Saeide Aghamohamadi; Katayoun Jahangiri; Amir Kavousi; Ardeshir Sayah Mofazali
Volume 6, Issue 4 , October 2018, , Pages 341-348
Abstract
Objective: To predict the accident mortality trend in next two decades in Iran.Methods: The study population comprised all deaths recorded in the system of registration and classification of causes of death of Ministry of Health and Medical Education of Iran during the years 2006 to 2015. The information ...
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Objective: To predict the accident mortality trend in next two decades in Iran.Methods: The study population comprised all deaths recorded in the system of registration and classification of causes of death of Ministry of Health and Medical Education of Iran during the years 2006 to 2015. The information was collected via death certificate, burial permit, and reporting forms. To forecast the trends of causes-of-death, Lee Carter model was employed in a demographic package 18.1 of R software version 3.3.1.Results: Based on the results, the highest percentage of all causes of death from accidents (in unintentional accidents) goes to transport accidents, and most top intentional accidents belonged to intentional self-harm. The trends of unintentional accidents in the whole population and both sexes have reduced from 2006 to 2035, such that the rate has reduced from 62.2 in 2006 to 12.1 per 100 thousand populations in 2035. It is anticipated that the causes of death due to intentional accidents with the rate of 8.86 in 2006, will be 1.89 (per 100,000 population) in the year 2035.Conclusion: Accident mortalities have a significant role in the deaths of Iranian population; therefore, to reduce the impact of accident mortality on society, a precise approach is needed to monitor the trends as well as preventing measures and increasing the safety standards.
