Epidemiological pattern of motorcycle injuries with focus on riding purpose: Experience from a middle-income country

Abedi, Leili; Khorasani‐Zavareh, Davoud; Sadeghi‐Bazargani, Homayoun

doi:10.15171/jarcm.2015.024

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…The dominancy of men indicates higher number of male car occupants as drivers/passengers [10-12, 14, 37, 41-43], possibly due to their frequent commuting by cars or their interest in excitement of speedy driving, [44,45]. Particularly in countries like Iran though women share a smaller role in transportation [45], they comply with traffic rules and regulations, with great respect for human rights [46].…”

Section: Discussionmentioning

confidence: 99%

Epidemiology of Road Traffic Injury Fatalities among Car Users; A Study Based on Forensic Medicine Data in East Azerbaijan of Iran

Sadeghi‐Bazargani

Samadirad

Shahedifar

et al. 2018

BEAT

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Objective: To study the epidemiology of car user road traffic fatalities (CURTFs) during eight years, in East Azerbaijan, Iran. Methods: A total of 3051 CURTFs registered in East Azerbaijan forensic medicine organization database, Iran, during 2006-2014, were analyzed using Stata 13 statistical software package. Descriptive statistics (p<0.05) and inferential statistical methods such as Chi-squared test and multivariate logistic regression with p<0.1 were applied. Results: Of the 7818 road traffic injury (RTI) deaths, 3051 (39%) were car users of whom 71% were male (mean age of 36.7±18.5 years). The majority of accident mechanisms were vehicle-vehicle crashes (63.95%), followed by rollover (26.24%). Crash causing vehicle fall increased the pre-hospital death likelihood by 2.34 times. The prominent trauma causing death was head trauma (in 62.5%). In assessing the role of type of counterpart vehicle on pre-hospital mortality, considering the other cars to be the reference group for comparison, deceased victims were 1.83 times more likely to die before hospital when the counterpart vehicle was a truck and 1.66 times more for buses. Conclusion: Decreasing the car users' fatalities using appropriate strategies such as separating the roads for heavy and light vehicles and improving the injury related facilitation may be effective. Male drivers with low education could be prioritized for being trained.

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Section: Discussionmentioning

confidence: 99%

Epidemiology of Road Traffic Injury Fatalities among Car Users; A Study Based on Forensic Medicine Data in East Azerbaijan of Iran

Sadeghi‐Bazargani

Samadirad

Shahedifar

et al. 2018

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“…The World Health Organization (WHO) stated that upwards of 1.2 million people die each year on the world’s roads, nearly half of which are those with the least protection: motorcyclists, cyclists, and pedestrians [1]. Fast economic growth in low- and middle-income nations has been associated with a surge in motorization and road traffic injuries [2]. In Ghana, studies have shown that one of the leading causes of death and injury is road traffic crashes, most of which occur in rural areas [3].…”

Section: Introductionmentioning

confidence: 99%

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

Wahab

Jiang

2019

PLoS ONE

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Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.

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“…This is especially of importance for novice riders due to their lower age and experience which are considered as the risk factors of motorcycle traffic injuries [ 27 – 29 ]. We highly recommend the researchers using SMRBQ to collect some additional information potentially related with riding behaviors or accident risks such as having a valid riding license according to the local legislations, riding experience, purpose of riding, age and gender [ 13 , 29 – 31 ].…”

Section: Discussionmentioning

confidence: 99%

“…In order to investigate motorcycle riding behavior it should be taken into account that motorcycle riders comprise a specific subpopulation of vehicle users with various purposes for using motorbikes other than just transportation. These might include such purposes as: recreation, sporting, delivery of goods, carrying passengers and even illegal activities [ 8 – 13 ]. Using lengthy questionnaires, regardless of their benefits, has its own limitations such as being costly and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

The short Persian version of motorcycle riding behavior questionnaire and its interchangeability with the full version

et al. 2018

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The aim of current study was to develop a valid and reliable short motorcycle riding behavior questionnaire (SMRBQ) and assess its interchangeability with the original 48-item tool. Through a psychometric study in Bukan district of Iran in 2015, the most recent available Persian version of the motorcycle riding behavior questionnaire (MRBQ) was used as a reference to develop its short version, the SMRBQ. The preliminary version was prepared through expert reviews, and its dimension was further reduced through principal component analysis (PCA). An exploratory factor analysis (EFA) was then applied on the remaining items and the final version was developed with 23 items. The validity, consistency, agreement and interchangeability of the SMRBQ were assessed in parallel with the MRBQ using several statistical methods including Kendall’s Tau, intra-class correlation coefficient (ICC), Bland-Altman plot and receiver operating characteristics (ROC) curve analysis. All the 340 participants were males. The mean age of the participants was 30.2 (SD = 9.1). SMRBQ was developed including 23 items. The mean normalized score for the full version was 30.5 (SD = 11.2) and it was 30 (SD = 13.9) for the short version. There was a high correlation between the normalized scores of MRBQ and SMRBQ (Kendall’s Tau = 0.82). The ICC of the interchangeability of the full version and short version scales was as high as 0.92 (95% CI: 90.2–93.5). The scale had adequate internal consistency based on the calculated Cronbach’s alpha which was 0.85 for the scale. Bland-Altman and ROC curve analysis confirmed the interchangeability and criterion validity of the SMRBQ. The Persian version of SMRBQ was found to be a valid, reliable and feasible tool for assessing motorcycle riding behavior in the studied population.

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Epidemiological pattern of motorcycle injuries with focus on riding purpose: Experience from a middle-income country

Cited by 6 publications

References 19 publications

Epidemiology of Road Traffic Injury Fatalities among Car Users; A Study Based on Forensic Medicine Data in East Azerbaijan of Iran

Epidemiology of Road Traffic Injury Fatalities among Car Users; A Study Based on Forensic Medicine Data in East Azerbaijan of Iran

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity

The short Persian version of motorcycle riding behavior questionnaire and its interchangeability with the full version

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