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Objectives Stroke is a leading cause for disability and morbidity associated with increased economic burden due to treatment and post-stroke care (PSC). The aim of our study is to provide information on resource consumption for PSC, to identify relevant cost drivers, and to discuss potential information gaps. Methods A systematic literature review on economic studies reporting PSC-associated data was performed in PubMed/MEDLINE, Scopus/Elsevier and Cochrane databases, Google Scholar and gray literature ranging from January 2000 to August 2016. Results for post-stroke interventions (treatment and care) were systematically extracted and summarized in evidence tables reporting study characteristics and economic outcomes. Economic results were converted to 2015 US Dollars, and the total cost of PSC per patient month (PM) was calculated. Results We included 42 studies. Overall PSC costs (inpatient/outpatient) were highest in the USA ($4850/PM) and lowest in Australia ($752/PM). Studies assessing only outpatient care reported the highest cost in the United Kingdom ($883/PM), and the lowest in Malaysia ($192/PM). Fifteen different segments of specific services utilization were described, in which rehabilitation and nursing care were identified as the major contributors. Conclusion The highest PSC costs were observed in the USA, with rehabilitation services being the main cost driver. Due to diversity in reporting, it was not possible to conduct a detailed cost analysis addressing different segments of services. Further approaches should benefit from the advantages of administrative and claims data, focusing on inpatient/outpatient PSC cost and its predictors, assuring appropriate resource allocation.

Resilience engineering has consistently argued that safety is more than the absence of failures. Since the first book was published in 2006, several book chapters and papers have demonstrated the advantage in going behind 'human error' and beyond the failure concept, just as a number of serious accidents have accentuated the need for it. But there has not yet been a comprehensive method for doing so; the Functional Resonance Analysis Method (FRAM) fulfils that need.

Unfälle in Straßentunnelanlagen können zu weitreichendenFolgen für Tunnelbenutzer, für die Infrastrukturund im Schadensfall auch für das Tunnelbauwerkselbst führen.Gefahrguteinsätze bergenneben den üblichen Gefahren an einer Einsatzstelleeinige Besonderheiten, die die Lage im Tunnelzusätzlich erheblich beeinflussen können.

Accident investigation and risk assessment have for decades focused on the human factor, particularly 'human error'. Countless books and papers have been written about how to identify, classify, eliminate, prevent and compensate for it. This bias towards the study of performance failures, leads to a neglect of normal or 'error-free' performance and the assumption that as failures and successes have different origins there is little to be gained from studying them together. Erik Hollnagel believes this assumption is false and that safety cannot be attained only by eliminating risks and failures.

This paper presents analyses of 21real-world pedestrian versus vehicle collisions that were video recorded from vehicle dash mounted cameras or surveillance cameras. These pedestrian collisions have in common an impact configuration where the pedestrian was at the side of the vehicle, or with a minimal overlap at the front corner of the vehicle (less than one foot overlap). These impacts would not be considered frontal impacts [1], and as a result determining the speed of the vehicle by existing methods that incorporate the pedestrian travel distance post impact, or by assessing vehicle damage, would not be applicable. This research examined the specific interaction of non-frontal, side-impact, and minimal overlap pedestrian impact configurations to assess the relationship between the speed of the vehicle at impact, the motion of the pedestrian before and after impact, and the associated post impact travel distances. The21analyzed events are categorized according to the type of impact configuration (side-impact or minimal corner overlap). The vehicle speed was also determined from the video, and the relationship between the vehicle speed and pedestrian distance traveled post impact are summarized.

Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. 'Big data' has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (NAT) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a 'data society'. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated.

Rear-end crashes account for more than one in five fatal crashes in the U.S. The rear-end crash scenario most commonly associated with fatal crashes involves a following vehicle traveling 40 to 70 mph closing on a lead vehicle at a rate greater than 30 mph. The current research compiled an analysis of the literature to identify the kinematic factors, environmental factors, traffic-related factors and individual differences that are likely to influence drivers’ responses when closing on a slower-moving or stopped lead vehicle [LV]. In Part 1, several primarily kinematic-based methods for modeling drivers’ responses to a LV were compared for high-speed closing events. Methods utilizing looming (angular growth rate) equations were shown to predict drivers’ responses and time-to-contact methods (Inverse Tau) were conditionally accurate when applied to specific crash scenarios. However, the ratio or nominal response time methods did not predict drivers’ responses in most crash scenarios. Overall, with few exceptions, drivers responded in ways that could best be described when using the looming equation. In Part 2, the influence of the environment, traffic in the area, and individual differences were evaluated. Drivers responded much earlier than the looming equation suggested when in congested traffic, when following closely behind the lead vehicle, when the LV was parked on the shoulder, and when the lead vehicle was sideways across the road. In reduced lighting conditions, drivers responded significantly later when the lead vehicle had narrowed taillights, when the distances between the taillights was narrowed from the normal width, and when the lights on the lead vehicle were dimmer than normal. Interchanges, work zones, and inclines (or elevated roadways) were identified as locations where rear-end crash rate increased significantly. Drivers commonly responded 0.2 to 0.35 second faster when the lead vehicle displayed emergency flashing lights. Lastly, many drivers, whether a crash occurred or not, were found to make mirror glances in the moments before reaching the looming threshold. Hence, unbeknownst to a following driver, he/she may be looking away from the forward roadway at a critical moment while closing at a dangerous speed.

Moral disengagement (MD) is the process by which individuals mitigate the consequences of their own violations of moral standards. Although MD is understood to be co-determined by culture norms, no study has yet explored the extent to which MD applied to safety at work (JS-MD) fosters safety violations (e.g., accident underreporting), nor the role of organizational culture as a predictor of JS-MD. The current study seeks to address this gap in the literature by examining individual-(MD) and organizational-level (culture) factors that explain why employees fail to report workplace accidents. We tested a latent variable structural model positing organizational culture typologies (autocratic, bureaucratic, clan-patronage, technocratic, and cooperative) as predictors of JS-MD, which in turn is expected to mediate the relationship with accident underreporting. Using data from 1033 employees in 28 Italian organizations, findings suggest that bureaucratic safety culture was related to lower levels of JS-MD, whereas technocratic safety culture was related to greater JS-MD. In turn, JS-MD positively predicted employee accident underreporting and fully mediated the relationship between culture and underreporting. Theoretical and practical implications are discussed in light of the increasing focus on underreporting as well as the adverse individual and organizational consequences of failing to report workplace accidents.

Intersections have a high risk of vehicle-to-vehicle conflicts because of the overlapping traffic flow from multiple roads. To understand the factors contributing to the crashes, this study examines the common characteristics in intersection-related crash and near- crash events, such as the existence of traffic control devices, the driver at fault, and occurrence of visual obstructions. The descriptive data of the crash and near-crash events recorded in the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) database is used in categorization and statistical analysis in this study. First, the events are divided into seven categories based on trajectories of the conflicting vehicles. The categorization provides the basis for in-depth analysis of crash-contributing factors in specific confliction patterns. Subsequently, descriptive statistics are used to portray each of the categories. The severity of the categories is determined based of the frequency of occurrences of the crashes and near-crashes. Factors contributing to each crash category are then examined based on their frequency of occurrence with the categories. This study reveals the severity of different intersection-related crash patterns, as well as the effects of some crash-contributing factors. The information presented in this paper has the potential to guide investigators interested in developing simulation and test track scenarios to evaluate vehicles equipped with automated driving systems (ADS).

For over five decades, there has been an ongoing legal debate concerning the assessment of economic damages in personal injury cases. Determining economic damages that may be recoverable requires the reasonable value of the plaintiff's medical care to be established. Plaintiff and defense attorneys hold two distinct views regarding the concept of reasonable value. Plaintiff attorneys assert the basis for establishing reasonable value should be billed charges, while defense attorneys believe the amount paid should determine reasonable value. Complicating the issue is the collateral source rule, which prohibits courts from considering health insurance payments, or other payments, made on behalf of the plaintiff. This paper reviews issues pertaining to the concept of reasonable value and presents a new cost-based model for establishing the reasonable value of medical care that overcomes the limitations of previous approaches using methods that do not violate the collateral source rule.