Prediction of the burden of road traffic injuries in Iran by 2030: Prevalence, death, and disability-adjusted life years

Mozhgan Seif; Sedigheh Edalat; Ali Majidpour Azad Shirazi; Somayeh Alipouri; Mohsen Bayati

doi:10.1016/j.cjtee.2024.02.004

Prediction of the burden of road traffic injuries in Iran by 2030: Prevalence, death, and disability-adjusted life years

Chin J Traumatol. 2024 Feb 24:S1008-1275(24)00010-5. doi: 10.1016/j.cjtee.2024.02.004. Online ahead of print.

Authors

Mozhgan Seif¹, Sedigheh Edalat², Ali Majidpour Azad Shirazi², Somayeh Alipouri³, Mohsen Bayati⁴

Affiliations

¹ Non-communicable Diseases Research Center, Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
² Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
³ Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
⁴ Health Human Resources Research Center, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: bayatim66@gmail.com.

PMID: 38503589
DOI: 10.1016/j.cjtee.2024.02.004

Abstract

Purpose: Road traffic accidents pose a global challenge with substantial human and economic costs globally. Iran experiences a high incidence of road traffic injuries, leading to a significant burden on society. This study aims to predict the future burden of road traffic injuries in Iran until 2030, providing valuable insights for policy-making and interventions to improve road safety and reduce the associated human and economic costs.

Methods: This analytical study utilized time series models, specifically autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs), to predict the burden of road traffic accidents by analyzing past data to identify patterns and trends in Iran until 2030. The required data related to prevalence, death, and disability-adjusted life years (DALYs) rates were collected from the Institute for Health Metrics and Evaluation database and analyzed using R software and relevant modeling and statistical analysis packages.

Results: Both prediction models, ARIMA and ANNs indicate that the prevalence rates (per 100,000) of all road traffic injuries, except for motorcyclist road injuries which have an almost flat trend, remaining at around 430, increase by 2030. Based on estimations of both models, the rates of death and DALYs due to motor vehicle and pedestrian road traffic injuries decrease. For motor vehicle road injuries, estimated trends decrease to approximately 520 DALYs and 10 deaths. Also, for pedestrian road injuries these rates reached approximately 300 DALYs and 6 deaths, according to the models. For cyclists and other road traffic injuries, the predicted DALY rates by the ANN model increase to almost 50 and 8, while predictions conducted 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 predictions conducted by the ARIMA model show a static trend, remaining at 0.43 and 0.07. According to the ANN model, 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 rates remaining at 200 and approximately 3.2, respectively.

Conclusion: The prevalence of road traffic injuries is predicted to increase, while the death and DALY rates of road traffic injuries show different patterns. Effective intervention programs and safety measures are necessary to prevent and reduce road traffic accidents. Different interventions should be designed and implemented specifically for different groups of pedestrians, cyclists, motorcyclists, and motor vehicle drivers.

Keywords: Accident prevention; Accidents; Forecasting; Motorcycles; Road injury; Traffic.