Analysis of injuries and deaths from road traffic accidents in Iran: bivariate regression approach

Abstract

Backgrounds This study aims to estimate and compare the parameters of some univariate and bivariate count models to identify the factors affecting the number of mortality and the number of injured in road accidents. Methods The accident data used in this study are related to Kermanshah province in march2020 to march2021. Accidents areas were divided into 125 areas based on density characteristics. In a one-year period, 3090 accidents happened on the suburban roads of Kermanshah province, which resulted in 398 deaths and 4805 injuries. Accident information, including longitude and latitude of accident location, type of accident (fatal and injury), number of deaths, number of injuries, accident type, the reason of the accident, and the kind of accident were all included as population-level variables in the regression models. We investigated four frequently used bivariate count regression models for accident data in the literature. Results In bivariate analysis, except for the DNM model, there is a reasonable decrease in the AIC measures of the saturated model compared to the reduced model for the other three models. For the injury models, MSE is lowest, respectively for DIBP (137.87), BNB (289.46), BP (412.36) and DNM (3640.89) models. These results are also established for death models. But, in univariate analysis, only injury models almost present reasonable results. Conclusions Our findings show that the IDBP model is better suitable for evaluating accident datasets than other models. Motorcycle accidents, pedestrian accidents, left turn deviance, and dangerous speeding were all significant variables in the IDBP death model, and these parameters were linked to accident mortality.

Traffic injury as a medical and sanitary consequence of a man-made emergency in Ukraine. Report two: comparative characteristics of clinical and epidemiological signs (gender and age)

“…Наприклад, у Волинській області цього року порівняно з відповідним періодом 2021 року кількість ДТП збільшилася на 45 %. Також велика кількість ДТП зафіксована у Київській, Львівській, Дніпропетровській областях [2], хоча найбільша питома вага ДТП завжди спостерігалася у великих містах (мегаполісах) [3][4][5].…”

“…Також у моделі 1 є найменший показник постраждалих до 20 років, а у моделі 3 -старше 71 року. Такий характер розподілу більш притаманний країнам, що розвиваються [5,21,24]. Крім того, у результаті дослідження встановлено вплив вікової ознаки на перебіг травматичного процесу в усіх моделях дослідження.…”

Clinical epidemiological characteristics of traffic injuries in rur alareas

“…Дорожньо-транспортна травма залишається однією з основних причин травматизація у країнах, що розвиваються [1,2,3], хоча у розвинених країнах це питання також залишається болючим. Невирішеним питанням навіть у розвинених країнах досі є надання медичної допомоги постраждалим у результаті ДТП у сільській місцевості [3,4,5].…”

PERSIAN traffic safety and health cohort: a population-based precrash cohort study

“…According to the 2018 Global Status Report on Road Safety, the estimated rate of road traffic deaths in Iran was 20.5 per 100 0000 people. Also, the number of deaths by RTCs in the world is three people for every ten thousand cars, while it is 33 people for every ten thousand vehicles in Iran 6. RTIs are the eighth leading cause of death worldwide, but in Iran, they are the second leading cause of death and are accounted for 80 000 major traumas yearly 7 8.…”

“…Also, the number of deaths by RTCs in the world is three people for every ten thousand cars, while it is 33 people for every ten thousand vehicles in Iran. 6 RTIs are the eighth leading cause of death worldwide, but in Iran, they are the second leading cause of death and are accounted for 80 000 major traumas yearly. 7 8 RTCs and RTIs cause serious economic loss approximately 1%–2% of Gross National Product in high-income countries and 6%–7% in Iran.…”