International Journal of Injury Control and Safety Promotion最新文献

Lack of data on exposure for walking and cycling poses a significant barrier to understanding the injury risk of these road users. Though this data paucity is most prevalent across low-and-middle-income countries (LMICs), it remains a challenge in many high-income countries as well. A new and simple method has been proposed to estimate population-level cycling distance travelled, with New Delhi, India as a case study. I used two independent estimates to calculate this distance. First, a ratio of motorcycle volume counts to that of cycle volume counts across major roads, and second, the total annual distance travelled by motorcycles. I validate this method using data from London, where cycling distance estimates are available from city-wide traffic volume counts as well as household travel survey. Combining the distance estimates with annual fatalities of corresponding road users, I found that cyclists have about 2 times greater fatality risk per kilometre than motorcycle occupants and about 40 times greater risk than car occupants. To encourage greater use of cycling, there is an urgent need to narrow this gap between the safety of cyclists and that of car occupants. The proposed method can be used to monitor cycling usage and its risk for many settings where traffic surveillance systems do not exist.

This review aimed to evaluate and synthesize information on the effects of first-aid education in road traffic crashes on knowledge, attitudes, and skills among non-healthcare professionals. A qualitative study was designed according to the Prepared Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards to evaluate three outcomes, knowledge, skills, and attitude. The search strategy was performed in five databases (Science Direct, Scopus, CINAHL Plus, PubMed, and Google Scholar) to retrieve primary studies published between January 2011 and December 2021. In addition, the full texts of randomized controlled trials conducted on adults were included. Among the 2,399 articles retrieved from the databases, 2,388 were discarded, and only five studies met the inclusion criteria and were used in the final analysis and synthesis. Three of the five studies suggested that knowledge and skills are crucial in differentiating outcome factors between intervention and control groups regarding the effectiveness of first aid education. However, two studies on attitude and behaviour toward first-aid found no discernible change between the intervention and the control group. Therefore, our review revealed that only a well-structured first aid education is crucial to leverage the knowledge and skills of non-healthcare professionals before first aid provision, not attitude or behaviours.

Emergency departments (EDs) are often the first point of contact for individuals following self-harm. The majority of previous research relies on hospital-based data, yet only a minority of individuals who self-harm in the community present to healthcare services. The study design is cross-sectional survey design. Data from the National Institute for Health Research Applied Research Collaboration North West Coast (NIHR ARC NWC) Household Health Survey, a community-based public health survey in North West England, was collected using stratified random sampling. Three thousand four hundred twelve people were recruited in 2018 from relatively disadvantaged areas. The sample included 1490 men and 1922 women aged 18 to 100 years (M = 49.37, SD = 18.91). Logistic regression analysis was employed to examine demographic, health and socioeconomic predictors of self-harm and ED attendance for self-harm. Age (18-24 years), lower financial status, depression, anxiety and physical and mental health co-morbidity was associated with significantly higher levels of self-harm. People aged 18-24 years, with physical and mental health co-morbidity and lower levels of social support had significantly higher levels of attending EDs for self-harm. Improving people's financial situations, social connectivity, mental and physical health may help to reduce individual risk for self-harm and strain on health services.

The most cost-effective way to improve the performance of a new road is to integrate road safety into the early phase of the project design. Therefore, the information obtained from the design phase is simply used to get an overview of the project in place. This article proposes a simplified analytical tool to target road safety problems proactively, even before inspection visits. The study area contains 110 segments of 100 m long (inspection intervals) of a highway under construction, sited in Algeria in the locality of Ghazaouet, Wilaya of Tlemcen. The methodology adopted consists of combining the International Road Assessment Program (iRAP) and the multiple linear regression method to obtain a simplified analytical model, which allows the prediction of road risk for each section of 100 m. The results obtained showed a 98% correlation between the model values and the real values obtained by the iRAP approach. As a complement to the iRAP method, this approach facilitates the anticipatory assessment of road risks by road safety auditors. Eventually, this tool will support and guide auditors to be acquainted with the recent trends in the domain of road safety.

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors.

Various studies have investigated the relationship between speed and accidents using different definitions of speed variation. This research considers the speed in mixed traffic as heterogeneous based on the vehicle categories. This research aims to develop a traffic safety model with speed heterogeneity as expressed in accident modification factor (AMF) index. The data types include traffic data, road volumes and geometrics from 18 roads in 8 provinces in Indonesia: Central Sulawesi, Southeast Sulawesi, South Sulawesi, West Kalimantan, Central Kalimantan, NTB, NTT and Bali. The power model is adopted to model the relationship between speed changes and the number of accidents and victims. Change in paratransit speed is significant in predicting all types of AMFs, but the effects are lower than those of the other categories. Truck speed change has the highest impact of fatalities. A 10% decrease in truck speed results in a 29.9% decrease in the number of fatalities, whilst the same 10% decrease in paratransit decreases 17.4% of fatalities. The study resulted in AMF models based on the vehicle speed heterogeneity that could be used in road safety evaluation by looking at the effects of vehicle speed changes in specific categories.

Spousal physical violence (PV) against women is considered to be major health issue in developing countries. Lifetime physical violence is a composite outcome consists of hit, kick, beat, slap and threatened with weapon, perpetrated by the husband. The study aims to examine changes in prevalence and specific risk factors for PV from 1998 to 2016 in India. This study analyzed data from a cross sectional epidemiological survey in 1998-1999, NFHS-3 (2005-2006) and NFHS-4 (2015-2016) data. There was a significant decline of about 10% (95% CI: 8.8%-11.1%) in PV. Major risk factors for change in PV were husband's use of alcohol, illiteracy and socio-economic status of the household. The Protection of Women from Domestic Violence Act may have played a role in reducing the PV. Even though there was a decline in PV, actions have to be implemented from the root level to ensure women empowerment.

Investigating the contributing factors to traffic crash severity is a demanding topic in research focusing on traffic safety and policies. This research investigates the impact of 16 roadway condition features and vacations (along with the spatial and temporal factors and road geometry) on crash severity for major intra-city roads in Saudi Arabia. We used a crash dataset that covers four years (Oct. 2016 - Feb. 2021) with more than 59,000 crashes. Machine learning algorithms were utilized to predict the crash severity outcome (non-fatal/fatal) for three types of roads: single, multilane, and freeway. Furthermore, features that have a strong impact on crash severity were examined. Results show that only 4 out of 16 road condition variables were found to be contributing to crash severity, namely: paints, cat eyes, fence side, and metal cable. Additionally, vacation was found to be a contributing factor to crash severity, meaning crashes that occur on vacation are more severe than non-vacation days.

To achieve an effective emergency response and road safety, this study aims to assist a semi-automated dynamic system to analyze and predict the spatial distribution and temporal pattern of road crashes. Kasur, an intermediate city of Pakistan, was selected and data including location, time and reasons of accidents for five years (2014-2018) was utilized. Radar charts, Getis-Ord Gi* statistic, Moran's I spatial auto-correlation, and time series indices were engaged to present temporal, spatial and spatial-temporal variation of accidents, using python-based tools and jupyter notebook. A dynamic user interface was created using Github and Tableau to visualize a real-time zoom-able spatiotemporal variation of accidents. The results explain that out of 12 months, October faces the peak while April sees the least of road accidents. 7am is the peak hour for accidents and the weekends record a significantly higher number of road accidents as compared to weekdays. The city core witnesses the major hotspot areas with huge cluster of accidents. The findings contribute towards a well-informed decision support system, the knowledge of spatial analytics and its application in road safety science, and the preparedness of the rescue agencies for rapid response to reduce the impacts of road accidents.

Identifying black spots effectively and accurately is a pivotal and challenging task to improve road traffic safety. A novel black spot identification model is proposed by integrating the GIS-based processing with hierarchical density-based spatial clustering of applications with noise. Additionally, the optimal clustering parameters are determined based on an internal validation indicator called the density-based clustering validation index to minimize the impact of subjectivity in parameter selection. The model is validated by collecting 3536 accident data from 1 August to 31 October 2020 in Hangzhou, China, and eventually identifies 39 black spots. The results show that: (1) The number of accidents contained in black spots account for 75% of all accidents, while the length of network in the black spots only account for 23.26% of the total road network length. (2) Compared with the conventional density-based spatial clustering of applications with noise model and K-means model, the proposed model achieves the best performance with more accidents gathered per unit road length. (3) The sample survey with 6 onsite of the identified black spots indicates that the proposed model has high recognition accuracy and recommend these sites for further investigation.