Medical Costs and Economic Burden Caused by Road Traffic Injuries in Iran

Behnood, Hamid Reza; Haddadi, Mashyaneh; Sirous, Shadrokh; Ainy, Elaheh; Rezaei, Reza

doi:10.5812/traumamon.35997

Cited by 11 publications

(5 citation statements)

References 19 publications

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“…It is estimated that road traffic crashes are responsible for approximately 3% of the loss of gross domestic product (GDP) in LMICs (WHO 2015). In Iran, it was estimated that the economic burden of road traffic injuries on only the health sector accounted for 2.18% of the country's total GDP in 2011 (Behnood et al 2017). There is therefore a need to replicate our findings in other settings, especially in LMICs, with different climates, traffic cultures, and fleet, while applying a more elaborated heat map.…”

Section: Discussionmentioning

confidence: 87%

Extreme environmental temperatures and motorcycle crashes: a time-series analysis

Zoeram

Yang

Mohammadi

et al. 2022

Environ Sci Pollut Res

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Extreme temperature could affect traffic crashes by influencing road safety, vehicle performance, and drivers’ behavior and abilities. Studies evaluating the impacts of extreme temperatures on the risk of traffic crashes have mainly overlooked the potential role of vehicle air conditioners. The aim of this study, therefore, was to evaluate the effect of exposure to extreme cold and hot temperatures on seeking medical attention due to motorcycle crashes. The study was conducted in Iran by using medical attendance for motorcycle crashes from March 2011 to June 2017. Data on daily minimum, mean and maximum temperature (°C), relative humidity (%), wind velocity (km/h), and precipitation (mm/day) were collected. We developed semi-parametric generalized additive models following a quasi-Poisson distribution with the distributed nonlinear lag model to estimate the immediate and lagged associations (reported as relative risk [RR], and 95% confidence interval [CI]). Between March 2011 and June 2017, 36,079 medical attendances due to motorcycle road traffic crashes were recorded (15.8 ± 5.92 victims per day). In this time period, the recorded temperature ranged from −11.2 to 45.4 °C (average: 25.5 ± 11.0 °C). We found an increased risk of medical attendance for motorcycle crashes (based on maximum daily temperature) at both extremely cold (1st percentile) and hot (99th percentile) temperatures and also hot (75th percentile) temperatures, mainly during lags 0 to 3 days (e.g., RR: 1.12 [95% CI: 1.05: 1.20]; RR: 1.08 [95% CI: 1.01: 1.16]; RR: 1.20 [95% CI: 1.09: 1.32] at lag0 for extremely cold, hot, and extremely hot conditions, respectively). The risk estimates for extremely hot temperatures were larger than hot and extremely cold temperatures. We estimated that 11.01% (95% CI: 7.77:14.06) of the medical attendance for motorcycle crashes is estimated to be attributable to non-optimal temperature (using mean temperature as exposure variable). Our findings have important public health messaging, given the considerable burden associated with road traffic injury, particularly in low- and middle-income countries.

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Section: Discussionmentioning

confidence: 87%

Extreme environmental temperatures and motorcycle crashes: a time-series analysis

Zoeram

Yang

Mohammadi

et al. 2022

Environ Sci Pollut Res

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“…The costs of traffic injuries in Iran in 2013 using the willingness to pay method constituted 6.46% of gross national income, much higher than the global average (Ainy, et al 2014). Consistent with this, the medical costs and economic burden of road traffic injuries were estimated in 2011 to be USD $4.44 billion (Behnood, 2017). Both these estimates do not account for the personal loss, suffering and trauma to Iranian families and the work environment.…”

Section: Introductionmentioning

confidence: 92%

Speed Management in Iran: A Review Process

Mooren¹,

Shuey²,

Hamelmann

et al. 2021

JRS

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The level of road trauma is high in the Eastern Mediterranean Region with the Islamic Republic of Iran having a particularly high fatality rate at 20.5 per 100,000 population. The Government, assisted by the World Health Organisation (WHO), committed to implementing demonstration projects in three provinces that will form the basis of road safety actions to be advanced by the WHO across the Region. In recognition that speed management is a pivotal factor in achieving a safer road and traffic system, and as a component of the project, a review was carried out in 2019 by a team of international experts in the field in collaboration with national consultants. This review was undertaken in consideration of the Safe System Approach and the Results-Based Management Approach. The findings of this review and their implications for future actions in Iran are discussed in this paper.

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“…According to the latest statistics of the Iran road maintenance and transportation organization (IRMTO) in 2020, from March 2019 to March 2020, 159,735 road traffic accidents (RTA) occurred in Iran, killing 16,947 and injuring 347,307 people [3]. The high rate of RTA in Iran, along with significant economic damage and loss of life [4,5], necessitates research into the problem.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

et al. 2021

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Drivers’ lack of alertness is one of the main reasons for fatal road traffic accidents (RTA) in Iran. Accident-risk mapping with machine learning algorithms in the geographic information system (GIS) platform is a suitable approach for investigating the occurrence risk of these accidents by analyzing the role of effective factors. This approach helps to identify the high-risk areas even in unnoticed and remote places and prioritizes accident-prone locations. This paper aimed to evaluate tuned machine learning algorithms of bagged decision trees (BDTs), extra trees (ETs), and random forest (RF) in accident-risk mapping caused by drivers’ lack of alertness (due to drowsiness, fatigue, and reduced attention) at a national scale of Iran roads. Accident points and eight effective criteria, namely distance to the city, distance to the gas station, land use/cover, road structure, road type, time of day, traffic direction, and slope, were applied in modeling, using GIS. The time factor was utilized to represent drivers’ varied alertness levels. The accident dataset included 4399 RTA records from March 2017 to March 2019. The performance of all models was cross-validated with five-folds and tree metrics of mean absolute error, mean squared error, and area under the curve of the receiver operating characteristic (ROC-AUC). The results of cross-validation showed that BDT and RF performance with an AUC of 0.846 were slightly more accurate than ET with an AUC of 0.827. The importance of modeling features was assessed by using the Gini index, and the results revealed that the road type, distance to the city, distance to the gas station, slope, and time of day were the most important, while land use/cover, traffic direction, and road structure were the least important. The proposed approach can be improved by applying the traffic volume in modeling and helps decision-makers take necessary actions by identifying important factors on road safety.

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Medical Costs and Economic Burden Caused by Road Traffic Injuries in Iran

Cited by 11 publications

References 19 publications

Extreme environmental temperatures and motorcycle crashes: a time-series analysis

Extreme environmental temperatures and motorcycle crashes: a time-series analysis

Speed Management in Iran: A Review Process

Evaluation of Tree-Based Machine Learning Algorithms for Accident Risk Mapping Caused by Driver Lack of Alertness at a National Scale

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