International Journal of Injury Control and Safety Promotion最新文献

{"title":"Estimating the differences in police and hospital records of people injured in traffic crashes in Dire Dawa City administration, Ethiopia.","authors":"Getu Segni Tulu, Mark King, Helen Bekri","doi":"10.1080/17457300.2024.2443979","DOIUrl":"10.1080/17457300.2024.2443979","url":null,"abstract":"<p><p>The management of road safety relies on data from road traffic crashes to identify priorities, monitor trends and evaluate interventions. Both police and hospital records are important sources of information on crashes that result in injury; however, both are known to be incomplete, with the quality and completeness of data being lower in low- and middle-income countries. The aim of this study is to estimate the magnitude of the underreporting of crashes in Dire Dawa City, Ethiopia, as a case study that may be applicable elsewhere. In addition, it gives an opportunity to understand the discrepancies between police and hospital records in Dire Dawa City and how the data systems work in the city. This research compared data on traffic collisions resulting in injury from July 2014 to February 2019 across police and hospital databases and used the capture-recapture technique to estimate the actual numbers of crashes and the degree of under-recording in both sources. It was found that there was substantial under-recording in both sources, with the degree of under-recording varying by urban/rural area, gender, age, road user category and injury severity, as well as by source within these variables. The police figures were lower than the hospital figures, and in all cases but three (rural areas, passengers and serious injury crashes), both sources had more unmatched than matched cases. In addition, the analysis discovered undocumented deaths and injuries in both databases. To summarize, police capture more death instances, but hospitals capture more serious injury cases. The capture-recapture strategy predicted a greater number of instances than currently recorded by police and hospitals. This demonstrates a major under-reporting of crash data from both sources. This level of under-recording can lead to less effective road safety management and evaluation. Replication of this research in other parts of Ethiopia could provide information on local practices that are more or less successful in reducing the level of under-recording, and such results may have implications for other countries with similar problems.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"52-60"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bicycle crash frequency modeling across different crash severities using a random-forest-based Shapley Additive explanations approach.","authors":"Tao Li, Ruiqi Wang, Hongliang Ding, Tiantian Chen, Hyungchul Chung","doi":"10.1080/17457300.2025.2485040","DOIUrl":"10.1080/17457300.2025.2485040","url":null,"abstract":"<p><p>Statistical modeling and data-driven studies on bicycle accidents are widespread, however, explanations of the underlying mechanisms remain limited, particularly regarding the impact of key risk factors on the bicycle crash frequency across different crash severities. This study aims to examine the effects of various risk factors on the frequency of bicycle crashes using Random Forest and Shapley Additive Explanations (RF-SHAP), taking into account the different crash severity levels. Data from three years of London crash data (2017 to 2019) is utilized. Population demographics, land use, road infrastructure, and traffic flows, are collected in Greater London. In addition to providing superior predictive accuracy, our proposed method identified critical risk factors at different levels of severity associated with bicycle crashes. The distinct contribution of this study is the identification of the primary factors influencing the severity of bicycle collisions in London through the use of RF-SHAP. The study quantifies both the main and interactive effects of various severity risk factors on bicycle collisions. Results suggest that the proportion of building areas and population density are most critical to bicycle crash numbers in different severity levels. Also, the interaction effects of the risk factors on bicycle crashes are revealed. Specifically, results reveal a negative correlation between traffic flow and overall bicycle crash frequency when the average road network connectivity is below 2.25. After controlling the population density, the proportion of residential areas shows a three-stage pattern of influence on the slight injury crash frequency. Furthermore, a boundary value of 6.3 is identified for the safety impact of road density on fatal and severely-injured bicycle crashes. Study findings should provide insights into cost-effective safety countermeasures for bicycle infrastructures, traffic controls, and safety education. Bicycle safety can be improved through these measures over the long term.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"87-100"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the injury severity of public bus-taxi crashes: a random parameters logistic model with heterogeneity in means approach.","authors":"Qiang Zeng, Zikang Li, Qianfang Wong, S C Wong, Pengpeng Xu","doi":"10.1080/17457300.2024.2440939","DOIUrl":"10.1080/17457300.2024.2440939","url":null,"abstract":"<p><p>Public buses and taxis play crucial roles in urban transportation. Ensuring their safety is of paramount importance to develop sustainable communities. This study investigated the significant factors contributing to the injury severity of bus-taxi crashes, using the crash data recorded by the police in Hong Kong from 2009 to 2019. To account for the unobserved heterogeneity, the random parameters logistic model with heterogeneity in means was elaborately developed. The results revealed that taxi driver age, bus age, traffic congestion, and taxi driver behavior had significantly heterogeneous effects on the injury severity of bus-taxi crashes and that the mean value of the random parameter for severe traffic congestion was likely to increase if the taxi's age was <5 years. Taxi driver gender, rainfall, time of day, crash location, bus driver behavior, and collision type were found to significantly affect the bus-taxi crash severity. Specifically, female taxi drivers, old taxis, rainfall, midnight, improper manipulation of bus and taxi drivers, head-on and sideswipe collision types, and non-intersections were associated with a higher likelihood of fatal and severe crashes. Based on our findings, targeted countermeasures were proposed to mitigate the injury severity of bus-taxi crashes.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"12-24"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring safety effects on urban expressway diverging areas: crash risk estimation considering extreme conflict types.","authors":"Jiaqiang Wen, Nengchao Lyu, Lai Zheng","doi":"10.1080/17457300.2024.2440940","DOIUrl":"10.1080/17457300.2024.2440940","url":null,"abstract":"<p><p>Previous research solely employed a single type of conflict extremes for crash estimation, without considering the joint impact of multiple types of conflict extremes on crash risk. Therefore, two analysis frameworks based on conflict extremes were proposed: separate modeling and cooperative modeling. Based on the trajectories from five diverging areas, longitudinal and lateral conflicts were extracted. Then, a Bayesian hierarchical model for joint multi-location conflict extremes was constructed. Next, the threshold for conflict extremes was determined using automatic mean residual life plots, and a link function was established between the logarithmic scale parameter and dynamic and static variables. Finally, model parameters were estimated using the Markov Chain Monte Carlo simulation method, and a comparative analysis of crash probabilities and overall risks for diverging areas in the two frameworks was conducted by the fitted distributions. The results show that density differences, speed differences, and the ratio of large vehicles are important covariates explaining the non-stationarity of conflict extremes. In terms of crash probability, significant covariates exhibit stronger explanatory power for longitudinal conflicts compared to lateral conflicts. At the overall risk level, the accuracy of the separate modeling is higher compared to the cooperative modeling.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"25-39"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing machine learning and geographic analysis to improve Post-crash traffic injury management and emergency response systems.","authors":"Boonsak Hanterdsith","doi":"10.1080/17457300.2025.2487632","DOIUrl":"https://doi.org/10.1080/17457300.2025.2487632","url":null,"abstract":"<p><p>Traffic injuries are a major public health concern globally. This study uses machine learning (ML) and geographic analysis to analyse road traffic fatalities and improve traffic safety in Nakhon Ratchasima Province, Thailand. Data on road traffic fatalities were collected from forensic and hospital records. K-means clustering grouped death locations and identified cluster centres. The Ball Tree algorithm and Google Directions API were used to find the nearest trauma centre hospital from the injury locations. Statistical tests, including chi-square and Kruskal-Wallis, examined relationships between clusters and demographic variables. The analysis identified 181 cases, mostly males (83.43%), with a median age of 37 years. Clustering the death locations into four high-risk areas resulted in a Silhouette Score of 0.94, indicating suitable EMS locations. While no significant correlation was found with demographic variables, distinct patterns were observed in road user types. Testing the prediction performance for the nearest hospital using forty new locations yielded an accuracy, precision, recall, and F1 score of 0.90. These findings emphasize the importance of targeted interventions and resource allocation in traffic injury prevention and emergency response planning, showcasing the potential of ML and geographic analysis in enhancing traffic injury management and emergency response systems.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"32 1","pages":"108-117"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of the effectiveness of addition of road humps as a road safety intervention.","authors":"Walid Abdullah Al Bargi, Joel Kironde","doi":"10.1080/17457300.2025.2485033","DOIUrl":"10.1080/17457300.2025.2485033","url":null,"abstract":"<p><p>Evaluating the effectiveness of road humps is very essential in traffic safety and transportation planning. In Uganda, no study has assessed the effectiveness of road humps. This study evaluated the effectiveness of the addition of road humps as a safety intervention in Uganda. Before and after data of the injuries and death that occurred along Kansanga-Gabba and Mukwano road were obtained from Uganda Police Forces (UPF) and used during the analysis. Scikit-Learn library in python 3.7 was used to calculate descriptive statistics and Empirical Bayes (EB) method was used to estimate the effectiveness of the addition of road humps on the road. The results show that the addition of road humps led to a reduction of the road crash death by 38%, 63%, 21%, 31% and 93% for pedestrians, bicyclists, motorcyclists, Light-Duty Vehicles (LDVs), and Heavy-Duty Vehicles (HDVs) respectively. In addition, road crash injuries decreased by 56%, 17%, 13%, 32%, and 74% for pedestrians, bicyclists, motorcyclists, LDVs and HDVs respectively. The inferences from these results will be useful to reduce the continued road crash injuries and death on the road in Uganda.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"76-86"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing road traffic injury measures in the WANA region towards road safety specific SDGs.","authors":"Hamid Soori, Alireza Razzaghi","doi":"10.1080/17457300.2024.2440936","DOIUrl":"10.1080/17457300.2024.2440936","url":null,"abstract":"<p><p>The study of road traffic injuries (RTIs) is crucial for understanding the unique challenges faced by West Asia and North Africa (WANA) states. This research evaluates road safety practices in the WANA region, comparing them to global standards, and employs secondary data analysis from sources such as the Global Road Safety Status Report, Global Road Safety Facility, and the World Health Organization. The analysis examines epidemiological data, preventive measures like seatbelt and child-restraint use, and policy development, including national action plans, to estimate road traffic death rates per 10,000 vehicles and per 100,000 population. Data from 23 countries are analyzed, focusing on road traffic injury rates by user type, road safety laws, and global safety targets. Overall, WANA states account for 10.5% of global RTI fatalities, exceeding both world and European averages. Most pedestrian fatalities occur in Ethiopia (40.0%) and Afghanistan (34.0%). This indicates that low enforcement scores (averaging 5 out of 10) in most WANA countries contribute to the insufficient effectiveness of road safety laws in reducing injuries and deaths. Achieving the Sustainable Development Goal (SDG) to reduce global road traffic deaths by 50% by 2030 requires commitment and cooperation from governments, communities, and stakeholders in the WANA region.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"3-11"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating lives saved and serious injuries reduced by bicycle helmet use in Colorado, 2006-2014.","authors":"Nicholas N Ferenchak, Bruce N Janson, Wesley E Marshall","doi":"10.1080/17457300.2024.2441501","DOIUrl":"10.1080/17457300.2024.2441501","url":null,"abstract":"<p><p>Using the methodology developed by the National Highway Traffic Safety Administration (NHTSA) for motorcyclists, this paper estimates bicycle helmet effectiveness factors (HEFs), defined as the percentage greater chance that a helmeted bicyclist will avoid a fatality or serious injury relative to a non-wearer. We analyse reported motor vehicle-bicycle collisions in Colorado between 2006 and 2014. We conclude that NHTSA's motorcycle HEF methodology did not provide reasonable results given underreporting of low-severity collisions of helmeted bicyclists. The HEF methodology may be applied to bicycles in future research if more complete bicyclist collision reporting can be obtained. To account for underreporting, we calibrated our bicycle HEFs to past research and found that approximately one of every two bicyclists killed may have survived (HEF = 0.50) and one of every three seriously injured bicyclists may have been less seriously injured (HEF = 0.33) if wearing a helmet at the time of the collision.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"40-51"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of motorcyclist injury severities in motorcyclist violation crash on suburban roads of China: accommodating temporal instability and the unobserved heterogeneity in means and variances.","authors":"Yuntao Ye, Jie He, Xintong Yan","doi":"10.1080/17457300.2025.2487649","DOIUrl":"10.1080/17457300.2025.2487649","url":null,"abstract":"<p><p>This study analysed motorcyclist violation (MV) crashes on suburban roads of China to investigate how determinants affect MV crash injury severity and explore the temporal stability of determinants. Crash data from Xi'an, China (2015-2018) were utilized to investigate three MV crash injury categories: no injury, minor injury and severe injury. Motorcyclist-related, crash-related, roadway-related, environment-related and time-related characteristics were analysed utilizing a group of random parameters multinomial logit models with heterogeneity in means and variances. The temporal instability was measured by performing likelihood ratio tests. Marginal effects were calculated to further illustrate the temporal variations of these factors. The study found an overall temporal instability, with some violations like alcohol-impaired riding, speeding, and unlicensed riding having significant effects on MV crash injury severity. Additionally, the study revealed a significant risk compensation mechanism of riders under adverse riding conditions. The findings provided insights and recommendations for suburban motorcycle crash prevention strategies.</p>","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":" ","pages":"145-159"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Renew global partnerships for addressing the risk to vulnerable road users and strengthening research institutions.","authors":"Geetam Tiwari","doi":"10.1080/17457300.2025.2494915","DOIUrl":"https://doi.org/10.1080/17457300.2025.2494915","url":null,"abstract":"","PeriodicalId":47014,"journal":{"name":"International Journal of Injury Control and Safety Promotion","volume":"32 1","pages":"1-2"},"PeriodicalIF":2.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}