Estimating the avoidable burden and population attributable fraction of human risk factors of road traffic injuries in iran: application of penalization, bias reduction and sparse data analysis

Bakhtiyari, Mahmood; Mehmandar, Mohammad Reza; Khezeli, Mehdi; Latifi, Arman; Jouybari, Touraj Ahmadi; Mansournia, Mohammad Ali

doi:10.1080/17457300.2019.1660374

Cited by 9 publications

(12 citation statements)

References 31 publications

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“…The greatest avoidable burden was related to fatigue and drowsiness, over-speeding, and not fastening seatbelt. The authors concluded that by considering the high contribution of human risk factors in occurrence of fatal traffic injuries, appropriate legislation and prevention programmes for these risk factors would decrease half of such deaths (Bakhtiyari et al, 2019). The low usage of seat belts was seen in this study as were other important risk factors for motor vehicle traffic crashes such as speeding and driving while fatigued and drowsy which also need to be addressed.…”

Section: Addressing the Human Toll From Motor Vehicle Traffic Crashesmentioning

confidence: 72%

“…In addition to the demographic characteristics of age, gender, and socioeconomic status, many other risk factors related to the environments and the vehicles as well as the behaviours of pedestrians, drivers, occupants, cyclists, and motorcyclists have been implicated as contributing to this carnage on our roads. Several articles in the current issue of the Journal focus on motor vehicle traffic crashes, stressing on strategies and approaches aimed at reducing the human toll from motor vehicle traffic crashes (Arafa, El-Setouhy, & Hirshon, 2019;Bakhtiyari et al, 2019;Fletcher, McDowell, Thompson, & James, 2019;Zabihi, Davoodi, & Nordfjaern, 2019).…”

Section: Addressing the Human Toll From Motor Vehicle Traffic Crashesmentioning

confidence: 99%

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Addressing the human toll from motor vehicle traffic crashes

2019

International Journal of Injury Control and Safety Promotion

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Section: Addressing the Human Toll From Motor Vehicle Traffic Crashesmentioning

confidence: 72%

Section: Addressing the Human Toll From Motor Vehicle Traffic Crashesmentioning

confidence: 99%

Addressing the human toll from motor vehicle traffic crashes

2019

International Journal of Injury Control and Safety Promotion

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“…They included all road crash data of five main suburban roads from August to February 2015. Over speeding, not warning a seat belt, reckless overtaking, fatigue and drowsiness, and exceeding the speed limit were determined to be the most important human factors affecting traffic-related deaths [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

2022

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Background Prevention of road traffic injuries (RTIs) as a critical public health issue requires coordinated efforts. We aimed to model influential factors related to traffic safety. Methods In this cross-sectional study, the information from 384,614 observations recorded in Integrated Road Traffic Injury Registry System (IRTIRS) in a one-year period (March 2015—March 2016) was analyzed. All registered crashes from Tehran, Isfan, Fras, Razavi Khorasan, Khuzestan, and East Azerbaijan provinces, the six most populated provinces in Iran, were included in this study. The variables significantly associated with road traffic fatality in the uni-variate analysis were included in the multiple logistic regression. Results According to the multiple logistic regression, thirty-two out of seventy-one different variables were identified to be significantly associated with road traffic fatality. The results showed that the crash scene significantly related factors were passenger presence(OR = 4.95, 95%CI = (4.54–5.40)), pedestrians presence(OR = 2.60, 95%CI = (1.75–3.86)), night-time crashes (OR = 1.64, 95%CI = (1.52–1.76)), rainy weather (OR = 1.32, 95%CI = (1.06–1.64)), no intersection control (OR = 1.40, 95%CI = (1.29–1.51)), double solid line(OR = 2.21, 95%CI = (1.31–3.74)), asphalt roads(OR = 1.95, 95%CI = (1.39–2.73)), nonresidential areas(OR = 2.15, 95%CI = (1.93–2.40)), vulnerable-user presence(OR = 1.70, 95%CI = (1.50–1.92)), human factor (OR = 1.13, 95%CI = (1.03–1.23)), multiple first causes (OR = 2.81, 95%CI = (2.04–3.87)), fatigue as prior cause(OR = 1.48, 95%CI = (1.27–1.72)), irregulation as direct cause(OR = 1.35, 95%CI = (1.20–1.51)), head-on collision(OR = 3.35, 95%CI = (2.85–3.93)), tourist destination(OR = 1.95, 95%CI = (1.69–2.24)), suburban areas(OR = 3.26, 95%CI = (2.65–4.01)), expressway(OR = 1.84, 95%CI = (1.59–2.13)), unpaved shoulders(OR = 1.84, 95%CI = (1.63–2.07)), unseparated roads (OR = 1.40, 95%CI = (1.26–1.56)), multiple road defects(OR = 2.00, 95%CI = (1.67–2.39)). In addition, the vehicle-connected factors were heavy vehicle (OR = 1.40, 95%CI = (1.26–1.56)), dark color (OR = 1.26, 95%CI = (1.17–1.35)), old vehicle(OR = 1.46, 95%CI = (1.27–1.67)), not personal-regional plaques(OR = 2.73, 95%CI = (2.42–3.08)), illegal maneuver(OR = 3.84, 95%CI = (2.72–5.43)). And, driver related factors were non-academic education (OR = 1.58, 95%CI = (1.33–1.88)), low income(OR = 2.48, 95%CI = (1.95–3.15)), old age (OR = 1.67, 95%CI = (1.44–1.94)), unlicensed driving(OR = 3.93, 95%CI = (2.51–6.15)), not-wearing seat belt (OR = 1.55, 95%CI = (1.44–1.67)), unconsciousness (OR = 1.67, 95%CI = (1.44–1.94)), driver misconduct(OR = 2.51, 95%CI = (2.29–2.76)). Conclusion This study reveals that driving behavior, infrastructure design, and geometric road factors must be considered to avoid fatal crashes. Our results found that the above-mentioned factors had higher odds of a deadly outcome than their counterparts. Generally, addressing risk factors and considering the odds ratios would be beneficial for policy makers and road safety stakeholders to provide support for compulsory interventions to reduce the severity of RTIs.

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“…[1][2][3] It is clear that the implementation of a proper methodology in order to identify blackspots can avoid any dissipation of the limited fund allocated by authorities for safety measures. 4 Bakhtiyari et al 5 prioritized human risk factors and their contribution to fatal traffic injuries that consequently led to the enactment of some preventive measures to reduce car crashes. Chen et al 6 identified hotspots via diverse spatial analyzing methods in Songjiang new urban area, China, and the causes behind each accident.…”

Section: Introductionmentioning

confidence: 99%

A hybrid method based on P and P′ control chart for identifying hotspots

Jahan

Abbaspour

Safakhah

2021

Quality & Reliability Eng

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The terms “hotspots,” “blackspot,” or “high‐risk” are normally used for an area on a road where the number of car accidents are far beyond the normal level and, more importantly, the causes of the incident always link to one or many specific reasons. Identifying these spots is helpful and may assist people in authority to implement corrective actions. To do the aforementioned, some decision‐support tools could be useful since they enable us to monitor the indicators that may lead to a car accident, same as the procedure engineers use to control production lines in factories. In this regard, control charts are utilized to identify blackspots. Although the method is low cost, and provides quick detection and statistical reliability level and, more importantly, uses online applications; however, it advised that the rate quality control method could be used on roads with the same geometry and traffic conditions. This research develops a hybrid method, based on accident type, for improving rate quality control method to overcome the shortcomings. Furthermore, the suggested framework considers crash severity and traffic volume that have not been taken into account in the rate quality control method yet. The case study evaluates how this framework is successful to identify blackspots, and based on the result in the real environment, it reveals that the proposed method could detect and identify the hotspots.

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Estimating the avoidable burden and population attributable fraction of human risk factors of road traffic injuries in iran: application of penalization, bias reduction and sparse data analysis

Cited by 9 publications

References 31 publications

Addressing the human toll from motor vehicle traffic crashes

Addressing the human toll from motor vehicle traffic crashes

Modeling road traffic fatalities in Iran’s six most populous provinces, 2015–2016

A hybrid method based on P and P′ control chart for identifying hotspots

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