目标:道路交通事故在全球范围内带来巨大的人力和经济成本,是一项全球性挑战。伊朗道路交通伤害发生率很高,给社会带来沉重负担。这项研究旨在预测伊朗到2030年道路交通伤害的未来负担,为政策制定和干预措施提供有价值的见解,以改善道路安全并降低相关的人力和经济成本。
方法:这项分析研究利用了时间序列模型,特别是自回归积分移动平均(ARIMA)和人工神经网络(ANN),通过分析过去的数据来确定伊朗到2030年的模式和趋势,从而预测道路交通事故的负担。与患病率相关的所需数据,死亡,和残疾调整寿命年(DALYs)率从健康指标和评估研究所数据库中收集,并使用R软件和相关建模和统计分析软件包进行分析。
结果:两种预测模型,ARIMA和ANN表明,所有道路交通伤害的患病率(每100,000人),除了摩托车手的道路伤害趋势几乎持平,保持在430左右,到2030年增加。基于这两个模型的估计,机动车和行人道路交通伤害导致的死亡率和DALY下降。对于机动车道路伤害,估计趋势减少到大约520个DALYs和10个死亡。此外,对于行人道路伤害,这些比率达到约300DALYs和6例死亡,根据模型。对于骑自行车的人和其他道路交通伤害,ANN模型预测的DALY率增加到近50和8,而ARIMA模型进行的预测显示出静态趋势,保持在40和大约6.5。此外,通过ANN模型预测死亡率的这些比率增加到0.6和0.1,而通过ARIMA模型进行的预测显示出静态趋势,保持在0.43和0.07。根据ANN模型,摩托车手的预计DALY和死亡率分别降至100和约2.7。另一方面,ARIMA模型的预测显示出静态趋势,率分别保持在200和约3.2。
结论:道路交通伤害的患病率预计会增加,而道路交通伤害的死亡率和死亡率表现出不同的模式。有效的干预计划和安全措施对于预防和减少道路交通事故是必要的。应专门针对不同的行人群体设计和实施不同的干预措施,骑自行车的人,摩托车手,和机动车驾驶员。
OBJECTIVE: Road
traffic accidents pose a global challenge with substantial human and economic costs. 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.
CONCLUSIONS: 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.