International Journal of Injury Control and Safety Promotion最新文献

As the popularity of electric bicycles (e-bikes) continues to surge, the number of accidents involving them has commensurately increased. A significant factor contributing to the high fatality rate in these accidents is the low usage of helmets among e-bike riders. Helmets have been proven to reduce the severity of injuries, yet their usage remains unexpectedly low. This issue is particularly pronounced among college students, the primary buyer group for e-bikes. Regrettably, there is a lack of research exploring their intentions to wear helmets. Understanding determinants of their intentions to wear helmets is crucial in promoting safe e-bike travel. Therefore, the present study aims to develop an integrated theoretical model that combines the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM) to examine the factors influencing e-bike riders' helmet-wearing intentions among college students. Additionally, two variables-descriptive norms and law enforcement-are incorporated. The results indicate that the integrated model accounts for 76% of the variance in helmet-wearing intention, surpassing single-theory models. Specifically, the TPB accounts for 65%, while the HBM explains 53%. Notably, law enforcement emerges as the most influential factor, highlighting the crucial role of enforcing regulations and promoting awareness. Other significant factors include subjective and descriptive norms, attitudes, perceived benefits, perceived susceptibility, perceived barriers, and perceived severity. These findings provide valuable insights for policy development and targeted interventions aimed at improving helmet wear rates among e-bike riders, especially among the college student population.

This study aims to classify motorcycle (MC) following distance based on trajectory traffic data and identify the risks associated with MC following distances to prevent rear-end collisions. A total of 8,223 events of a MC following a vehicle were investigated in Pathum Thani, Thailand, and 41 cases of MC rear-end crashes were analyzed between 2017 and 2021. Time headway (TH), safe stopping distance (SSD) and time to collision (TTC) were applied to the proposed concept to determine safe following distance (SFD). Speed and following distance for actual rear-end crashes were applied to validate SFD. Results showed that the proposed SFD model identified the causes of MC rear-end collision events as mostly due to longitudinal critical area (38 cases, 92.68%), implying insufficient MC rider reaction and decision time for evasive action. The longitudinal warning area had relatively few chances for rear-end collisions to occur, with only 3 cases recorded. VDO clip extracts from MC rear-end crashes illustrated 11 cases (26.83%) of rider fatality. The study findings revealed that the SFD concept can help to prevent MC rear-end collision events by developing reminder systems when the rider reached the following distances of both warning and critical areas.

This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.

Taxis play a crucial role in urban public transportation, but the traffic safety situation of taxi drivers is far from optimistic, especially considering the introduction of ride-hailing services into the taxi industry. This study conducted a comparative analysis of risk factors in crashes between traditional taxi drivers and ride-hailing taxi drivers in China, including their demographic characteristics, working conditions, and risky driving behaviors. The data was collected from 2,039 traditional taxi drivers and 2,182 ride-hailing taxi drivers via self-reported questionnaires. Four XGBoost models were established, taking into account different types of taxi drivers and crash types. All models showed acceptable performance, and SHAP explainer was used to analyze the model results. The results showed that for both taxi drivers, risk factors related to risky driving behaviors are more important in predicting property damage (PD) crashes, while risk factors related to working conditions are more important in predicting person injury (PI) crashes. However, the relative importance of each risk factor varied depending on the type of crashes and the type of taxi drivers involved. Furthermore, the results also validated certain interactions among the risk factors, indicating that the combination of certain factors generated a greater impact on crashes compared to individual factors alone. These findings can provide valuable insights for formulating appropriate measures to enhance road safety for taxi driver.

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections.

Motorized two-wheeler users account for 45% of road fatalities in 2021 in India. Correct helmet use decreases the risk of fatalities, but information about the prevalence of correct helmet use is limited in the Indian context. This study aims to assess the prevalence of helmet use and factors associated with correct helmet use among motorized two-wheeler users in Bengaluru city, India. This observational cross-sectional assessment was conducted in a random-representative sample of 98021 motorized two-wheelers sampled from 15 intersections. Helmet use and other information were collected by trained Field Data Collectors via hand-held tablet devices and the KoBo collect application, following a globally used checklist. The prevalence of helmet use (all types) was 88% among riders and pillion together and 92.4% among riders. However, the prevalence of correct helmet use is 38% among riders, and non-standard helmet use is 27%. This study reveals a correct helmet use gap in Bengaluru city. The study recommends the need to shift enforcement, messaging, and intervention focus towards correct helmet use and the implementation of routine population-based surveillance for helmet use in the city.

Childhood burns pose a significant public health concern, particularly in low- and middle-income nations, where the burden is pronounced. Mongolia stands out for its elevated rates of burn-related child mortality, especially among 1 to 5-year-olds. Despite this, there is a lack of research on the current status of childhood burns in Ulaanbaatar. This study utilized Cognitive Edge's Sensemaker^® to collect micronarratives from parents in the ger districts of Ulaanbaatar, aiming to understand the present context of childhood burn injuries. Thematic analysis revealed four key themes, including features in home and outdoor environments, lack of safety measures, and limitations in supervision. The findings emphasize the need for a comprehensive approach to burn injury prevention tailored to the Mongolian context. Implementing suitable safety measures in ger districts based on these results could be instrumental. Additionally, extending this approach to other low- and middle-income countries warrants further investigation for effective preventive measures.

Distracted driving is a threat to traffic safety that can result in more traffic crashes. Although previous studies have been conducted to explore the relationship between driver distraction and hazardous driving actions, few studies are available to identify the causation between them. Thus, the study intended to evaluate the causal effects of distraction on hazardous driving actions at intersections based on the crash data extracted from the Crash Report Sampling System (2021-2022). The multinomial logit model was employed to reveal the factors contributing to driver distraction. Then, propensity score weighting was adopted to balance the factor distributions between distraction and non-distraction cases to identify the causal effects on hazardous actions. Results indicated that 1) the propensity of distraction is relevant to factors such as the driver's age, gender, vehicle type, speed limit, area, weather, and light condition, 2) driver distraction can significantly increase the probability of risky actions including speeding, running red lights, failing to obey stop signs, failing to yield, following too closely, and 3) the causal effects show great diversity for different distraction types. The findings serve to understand the influence mechanism of distraction on specific crash risks and develop countermeasures to reduce distraction and hazardous driving actions.

The study determined the prevalence and factors associated with unintentional injuries among under- fives in Kgalagadi South. A descriptive cross- sectional study was conducted between November 2020 and February 2021 using a multi- stage sampling technique to recruit 389 guardians with children aged five years and below. Self-administered questionnaires were used for the literate population, while interviewer-administered questionnaires were used for the population with limited literacy to elicit information from the guardians. Chi- square, univariate and multivariate logistic regression were used for inferential analysis. The prevalence of unintentional injuries was 48.8%. There was a significant association between injury occurrence and guardians' employment status, alcohol consumption and non- medicinal drug use by the guardian, cigarette use by the guardian, household income level, gender and age of the child who got injured. The guardians age and cigarette use by the guardian were identified as independent risk factors for injury occurrence among under- five children. The findings highlight the need for policymakers and practitioners to develop policies that support comprehensive health education for caregivers on childhood injury prevention and creating a safe environment for children.

There is a lack of research that investigates the risk factors of cycling in low- and middle-income countries. We present descriptive epidemiology of crashes that involved at least one fatal cyclist, over a three-year period (2016-2018) in Delhi, India. Next, we used an unmatched case-control approach to identify road design risk factors of cycle fatalities. Cases were road segments with at least one cycle fatality, and controls were those with none. We developed logistic regression models with cases and controls as binary outcomes to estimate the odds ratio of site characteristics. There were 167 crashes involving at least one cyclist fatality over the study period. Fatal cyclists were almost all males. They were less likely to be children or young adults and less likely to be residents of high-income localities, compared to the general population. One in ten crashes included more than one occupant on a cycle and 5% of fatal victims were pillion riders. Seventy percent crashes occurred at midblock, and majority were backend collisions. Regression shows that road width, traffic speed, and volume of heavy vehicles have strong positive effects on the fatality risk of cyclists. These results can inform strategies to design interventions for safety of cyclists.