International Journal of Injury Control and Safety Promotion最新文献

The aim of this study was to assess space-time clustering of suicide in Argentina between 2017 and 2020. A spatio-temporal scan statistic using a space-time permutation model was employed to retrospectively detect clusters of total suicides by sex and by age group. From 2017 to 2020, six statistically significant spatio-temporal clusters were identified in Argentina with either more or less suicides than expected. Two clusters extended from May 2019 to July and September 2020, while the remaining four clusters were detected only during 2020. The start of the COVID-19 pandemic seems to have changed the spatio-temporal patterns of suicide in Argentina. Despite the national decrease in the number of suicides during 2020 compared to previous years, the spatio-temporal analysis revealed both areas with decreases and increases in suicide. This heterogeneous scenario highlights the need for the study of local effects behind the emergence of these spatio-temporal suicide clusters.

Urban-rural integration is the critical process of urbanization, while during this process there are various corresponding problems generated, safety is the most important one, especially at urban-rural fringe areas due to mixed traffic flow and complicated land use. This study intended to investigate the injury severity interaction between urban area and urban-rural fringe area, and explore the correlation within injury severity levels and heterogeneity attributed to unobserved factors. To address the correlation and heterogeneity issues, a grouped random parameters seemingly unrelated bivariate (SUB) probit model was proposed, in which the SUB probit model addressed the correlation of residuals, while the random parameters model accommodated the heterogeneity due to unobserved factors. By comparing the pooled with random parameters models, the results showed that random parameters SUB probit model performed better than the pooled one, and the dataset collected from 2013 to 2017 in Chengdu, China was adopted to illustrate the proposed model. It is found that crash location, speed limit and age of person injured are significant for injury severity in urban and urban-rural fringe areas, but crash form plays a significant role in urban area while number of persons involved should be paid more attention due to injury severity in urban-rural fringe area. Some empirical suggestions are presented to improve the safety in urban and urban-rural fringe areas.

Left-turn waiting area (LWA) is an innovative traffic design that is popularly applied to improve the traffic capacity of signalized intersections in China. The traffic safety impacts of the LWA, however, have not been fully discussed in previous studies. Thus, the study aims to evaluate the safety performance of the LWA by means of the traffic conflict technique. A field investigation was conducted to collect the post-encroachment time (PET) of conflicts and relevant variables at the signalized intersections in Jinhua, China. The Chi-square and two sample t-tests were adopted to examine the difference in conflict distribution between the intersections with and without LWA. The random parameter ordered logit model was employed to identify the factors contributing to the risks of vehicular collisions. Results indicate that (1) intersections with LWA are generally associated with more merging conflicts; (2) there are no significant discrepancies in the PET values between intersections with and without LWA; and (3) factors such as the number of left-turn lanes, number of receiving lanes, conflict type, vehicle type, driving direction, stopping outside LWA and overtaking behavior are identified to significantly impact the traffic conflicts. The findings serve to develop the countermeasures to ensure the safe operation of LWA.

Intimate partner violence (IPV) is considered a serious public health concern among couples, regardless of the sexual orientation. However, there is a dearth of data about the determining factors of IPV among couples with mixed-romantic orientations, and not much is known about the role that intra-psychic factors play in the relationship between psychological factors and IPV. Therefore, the study set out to examine the mediating role of emotional suppression in the relationship between psychological factors and IPV among couples with mixed-romantic orientations in Nigeria. The study adopted a correlational research design. A total of 241 respondents (61.4% identified as heterosexual and 38.6% as bisexual) in mixed-romantic orientation marriages, were engaged using respondents-driven sampling. Outcomes revealed that emotional suppression (indirectly) mediated the relationship between depressive symptoms [c'-path analysis; b = .029, t(240) = 108, p = <.01; bootstrap =.0573-1715], anxiety [c'-path analysis; b = .027, t(240) = -0.044, p = <.05; bootstrap = .108-.004], stress [c'-path analysis; b = 0.019, t(240) = 0.057, p = <.001; bootstrap = .0247-.0992] and IPV among couples with mixed-romantic orientations. It was concluded that emotional suppression directly and indirectly mediated the relationship between psychological factors and IPV. Recommendations and limitations are discussed.

Pedestrians are the most vulnerable road users in the transportation system. Understanding pedestrian behaviour and road safety culture is critical for improving traffic safety in developing countries. The primary goal of this 2-fold study is to provide an investigation of pedestrian behaviour in Egypt, a developing country. The first part of this study validated the applying of Pedestrian Behaviour Scale (PBS) to investigate pedestrian behaviour in Egypt. Confirmatory Factor Analysis (CFA) was applied to 533 participants to assess the pedestrian's behavioural factors based on four validated categories: transgressions, lapses, aggressive and positive behaviours. The second part investigated pedestrian crossing behaviour at five different mid-block locations using video recordings. Then, logistic regression models were conducted to investigate pedestrian crossing safety. Males reported more aggressive behaviour than females. Pedestrians previously involved in a collision committed more transgressions and lapses. The presence of forced pedestrian crossing facilities, such as raised pedestrian crossings or traffic signals, significantly reduced the number of unsafe crossings when compared to uncontrolled pedestrian crossings. The aforementioned findings can be used by policymakers to improve road safety programs, create effective traffic safety campaigns, and enact appropriate laws, which could reduce the number of pedestrian-related crashes.

The media plays a key role in shaping the public's perception of road safety. This study analyzes the newspaper coverage and framing of motor vehicle crashes (MVCs) and road safety in Argentina, South America. The content of 304 articles published by 15 newspapers in November 2020 was reviewed. The results show that episodically framed news stories (focused on a single event or incident) prevail over thematically framed articles. MVCs are presented primarily as 'police' events and tend to receive more coverage when fatalities are involved. There is limited information provided on contextual and risk factors, and road safety advice is rarely included. Speeding, infrastructure, alcohol and other human-related variables are the most cited risk factors. Very few articles mention the use of protective devices (seat-belt, helmet and child restraint system). Although motorcyclists represent 40% of RTC deaths in Argentina, only 20% of the news coverage was about them. News coverage was quite similar in national and regional newspapers. There is an opportunity for the media to help build a better road safety culture, but significant changes in news framing are required. Practical recommendations for editors, journalists and road safety practitioners are provided.

With the development of big data technology and the improvement of deep learning technology, data-driven and machine learning application have been widely employed. By adopting the data-driven machine learning method, with the help of clustering processing of data sets, a recurrent neural network (RNN) model based on Keras framework is proposed to predict the injury severity in urban areas. First, with crash data from 2014 to 2017 in Nevada, OPTICS clustering algorithm is employed to extract the crash injury in Las Vegas. Next, by virtue of Keras' high efficiency and strong scalability, the parameters of loss function, activation function and optimizer of the deep learning model are determined to realize the training of the model and the visualization of the training results, and the RNN model is constructed. Finally, on the basis of training and testing data, the model can predict the injury severity with high accuracy and high training speed. The results provide an alternative and some potential insights on the injury severity prediction.

Despite statistics indicating that China has the world's largest taxi industry, there exists limited research about the relationship between workplace health hazards and taxi driver occupational crashes. In this paper, a cross-sectional survey of taxi drivers in four typical Chinese cities was conducted, and data on their self-reported job stress, health status, and daily risky driving behaviours, together with crash involvement experience in the two years before the survey was collected. Three hypotheses were then developed, and they were verified via multivariate analysis of variance (MANOVA) that the seriousness of drivers' health problems and the frequency of their daily risky driving behaviours could be the accurate predictor of their crash risk of taxi drivers. These factors were subsequently substituted in a bivariate negative binomial (BNB) distribution model to determine the joint rate of at-fault taxi drivers' involvement in property-damage-only (PDO) and personal-injury (PI) crashes. The results offer some useful advice for policy development to decrease and prevent professional taxi drivers from causing severe traffic crashes.

This research examines the injury severity of single-vehicle large-truck crashes in Florida while exploring the role of heterogeneity. A random parameter ordered logit (RPOL) model was applied to 27,505 single-vehicle large-truck crashes from 2007 to 2016 in Florida, and the contributing factors were identified. Random parameters and interaction effects were introduced to the model to determine the heterogeneity and its potential sources. The results suggested that driving speed of 76-120 mph and defective tires were the most influential factors in crash injury severity, increasing the probability of severe crashes. Regarding truckers' attributes, asleep or fatigued conditions and driving under the influence were correlated with a higher possibility of severe crashes. Interestingly, the results showed that truckers from outside the state of Florida were less likely to cause severe single-vehicle large-truck crashes compared to their Floridian counterparts. Y-intersections were also found as a high-risk location for single-vehicle large-truck crashes, leading to more severe outcomes. Regarding heterogeneity, the results indicated that the impacts of driving speed (26-50 mph) and light condition (dark - not lighted) significantly varied among the observations, and these variations could be attributed to driver action, vision obstruction, driver distraction, roadway type and roadway alignment.