Purpose:Road traffic accidents pose a global challenge with substantial human and economic costs.Iranexperiences a high incidence of road traffic injuries,leading to a significant burden on society.This studyaims to p...Purpose:Road traffic accidents pose a global challenge with substantial human and economic costs.Iranexperiences a high incidence of road traffic injuries,leading to a significant burden on society.This studyaims to predict the future burden of road traffic injuries in Iran until 2030,providing valuable insights forpolicy-making and interventions to improve road safety and reduce the associated human and economiccosts.Methods:This analytical study utilized time series models,specifically autoregressive integrated movingaverage(ARIMA)and artificial neural networks(ANNs),to predict the burden of road traffic accidents byanalyzing past data to identify patterns and trends in Iran until 2030.The required data related toprevalence,death,and disability-adjusted life years(DALYs)rates were collected from the Institute forHealth Metrics and Evaluation database and analyzed using R software and relevant modeling andstatistical analysis packages.Results:Both prediction models,ARIMA and ANNs indicate that the prevalence rates(per 100,000)of allroad traffic injuries,except for motorcyclist road injuries which have an almost flat trend,remaining ataround 430,increase by 2030.Based on estimations of both models,the rates of death and DALYs due tomotor vehicle and pedestrian road traffic injuries decrease.For motor vehicle road injuries,estimatedtrends decrease to approximately 520 DALYs and 10 deaths.Also,for pedestrian road injuries these ratesreached approximately 300 DALYs and 6 deaths,according to the models.For cyclists and other roadtraffic injuries,the predicted DALY rates by the ANN model increase to almost 50 and 8,while predictionsconducted by the ARIMA model show a static trend,remaining at 40 and approximately 6.5.Moreover,these rates for the prediction of death rate by the ANN model increased to 0.6 and 0.1,while predictionsconducted by the ARIMA model show a static trend,remaining at 0.43 and 0.07.According to the ANNmodel,the predicted rates of DALY and death for motorcyclists decrease to 100 and approximately 2.7,respectively.On the other hand,predictions made by the ARIMA model show a static trend,with ratesremaining at 200 and approximately 3.2,respectively.Conclusion:The prevalence of road traffic injuries is predicted to increase,while the death and DALYrates of road traffic injuries show different patterns.Effective intervention programs and safety measuresare necessary to prevent and reduce road traffic accidents.Different interventions should be designedand implemented specifically for different groups of pedestrians,cyclists,motorcyclists,and motorvehicle drivers.展开更多
基金This paper was extracted from a research project at Shiraz University of Medical Sciences with grant number 16369. The funder had no role in the study design, data collection, statistical analysis, interpretation of findings, and writing of the manuscript.
文摘Purpose:Road traffic accidents pose a global challenge with substantial human and economic costs.Iranexperiences a high incidence of road traffic injuries,leading to a significant burden on society.This studyaims to predict the future burden of road traffic injuries in Iran until 2030,providing valuable insights forpolicy-making and interventions to improve road safety and reduce the associated human and economiccosts.Methods:This analytical study utilized time series models,specifically autoregressive integrated movingaverage(ARIMA)and artificial neural networks(ANNs),to predict the burden of road traffic accidents byanalyzing past data to identify patterns and trends in Iran until 2030.The required data related toprevalence,death,and disability-adjusted life years(DALYs)rates were collected from the Institute forHealth Metrics and Evaluation database and analyzed using R software and relevant modeling andstatistical analysis packages.Results:Both prediction models,ARIMA and ANNs indicate that the prevalence rates(per 100,000)of allroad traffic injuries,except for motorcyclist road injuries which have an almost flat trend,remaining ataround 430,increase by 2030.Based on estimations of both models,the rates of death and DALYs due tomotor vehicle and pedestrian road traffic injuries decrease.For motor vehicle road injuries,estimatedtrends decrease to approximately 520 DALYs and 10 deaths.Also,for pedestrian road injuries these ratesreached approximately 300 DALYs and 6 deaths,according to the models.For cyclists and other roadtraffic injuries,the predicted DALY rates by the ANN model increase to almost 50 and 8,while predictionsconducted by the ARIMA model show a static trend,remaining at 40 and approximately 6.5.Moreover,these rates for the prediction of death rate by the ANN model increased to 0.6 and 0.1,while predictionsconducted by the ARIMA model show a static trend,remaining at 0.43 and 0.07.According to the ANNmodel,the predicted rates of DALY and death for motorcyclists decrease to 100 and approximately 2.7,respectively.On the other hand,predictions made by the ARIMA model show a static trend,with ratesremaining at 200 and approximately 3.2,respectively.Conclusion:The prevalence of road traffic injuries is predicted to increase,while the death and DALYrates of road traffic injuries show different patterns.Effective intervention programs and safety measuresare necessary to prevent and reduce road traffic accidents.Different interventions should be designedand implemented specifically for different groups of pedestrians,cyclists,motorcyclists,and motorvehicle drivers.
