Practical guidelines for the registration and monitoring of serious traffic injuries, D7.1 of the H2020 project SafetyCube

BACKGROUND AND OBJECTIVES Crashes also cause numerous serious traffic injuries, resulting in considerable economic and human costs. Given the burden of injury produced by traffic, using only fatalities as an indicator to monitor road safety gives a very small picture of the health impact of traffic crashes, just the tip of the iceberg. Moreover, in several countries during the last years the number of serious traffic injuries has not been decreasing as fast as the number of fatalities. In other countries the number of serious traffic injuries has even been increasing (Berecki-Gisolf et al., 2013; IRTAD Working Group on Serious Road Traffic Casualties, 2010; Weijermars et al., 2015).Therefore, serious traffic injuries are more commonly being adopted by policy makers as an additional indicator of road safety. Reducing the number of serious traffic injuries is one of the key priorities in the road safety programme 2011-2020 of the European Commission (EC, 2010). To be able to compare performance and monitor developments in serious traffic injuries across Europe, a common definition of a serious road injury was necessary. In January 2013, the High Level Group on Road Safety, representing all EU Member States, established the definition of serious traffic injuries as road casualties with an injury level of MAIS ≥ 3. The Maximum AIS represents the most severe injury obtained by a casualty according to the Abbreviated Injury Scale (AIS). Traditionally the main source of information on traffic accidents and injuries has been the police registration. This provides the official data for statistics at national and European level (CARE Database). Data reported by police usually is very detailed about the circumstances of the crash particularly if there are people injured or killed. But on the other hand police cannot assess the severity of injuries in a reliable way, due, obviously to their training. Therefore, police based data use to classify people involved in a crash as fatality, severe injured if hospitalised more than 24 hours and slight injured if not hospitalised. Moreover, it is known that even a so clear definition as a fatality is not always well reported and produces underreporting. This is due to several factors such as lack of coverage of police at the scene or people dying at hospital not followed by police (Amoros et al., 2006; Broughton et al., 2007; Pérez et al., 2006). Hospital records of patients with road traffic injuries usually include very little information on circumstances of the crash but it does contain data about the person, the hospitalisation (date of hospitalisation and discharge, medical diagnosis, mechanism or external cause of injury, and interventions). Hospital inpatient Discharge Register (HDR) offers an opportunity to complement police data on road traffic injuries. Medical diagnoses can be used to derive information about severity of injuries. Among others, one of the possible scales to measure injury severity is the Abbreviated Injury Scale (AIS). The High Level group identified three main ways Member States can collect data on serious traffic injuries (MAIS ≥ 3): 1) by applying a correction on police data, 2) by using hospital data and 3) by using linked police and hospital data. Once one of these three ways is selected, several additional choices need to be made. In order to be able to compare injury data across different countries, it is important to understand the effects of methodological choices on the estimated numbers of serious traffic injuries. A number of questions arise: How to determine the correction factors that are to be applied to police data? How to select road traffic casualties in the hospital data and how to derive MAIS ≥ 3 casualties? How should police and hospital data be linked and how can the number of MAIS ≥ 3 casualties be determined on the basis of the linked data sources? Currently, EU member states use different procedures to determine the number of MAIS ≥ 3 traffic injuries, dependent on the available data. Given the major differences in the procedures being applied, the quality of the data differs considerably and the numbers are not yet fully comparable between countries. In order to be able to compare injury data across different countries, it is important to understand the effects of methodological choices on the estimated numbers of serious traffic injuries. Work Package 7 of SafetyCube project is dedicated to serious traffic injuries, their health impacts and their costs. One of the aims of work package 7 is to assess and improve the estimation of the number of serious traffic injuries. The aim of this deliverable (D7.1) is to report practices in Europe concerning the reporting of serious traffic injuries and to provide guidelines and recommendations applied to each of the three main ways to estimate the number of road traffic serious injuries. Specific objectives for this deliverable are to: Describe the current state of collection of data on serious traffic injuries across Europe Provide practical guidelines for the estimation of the number of serious traffic injuries for each of the three ways identified by the High Level Group Examine how the estimated number of serious traffic injuries is affected by differences in methodology

Identification of road user related risk factors, deliverable 4.1 of the H2020 project SafetyCube.

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the first deliverable (4.1) of work package 4 which is dedicated to identifying and assessing human related risk factors and corresponding countermeasures as well as their effect on road safety. The focus of deliverable 4.1 is on identification and assessment of risk factors and describes the corresponding operational procedure and corresponding outcomes. The following steps have been carried out: Identification of human related risk factors – creation of a taxonomy Consultation of relevant stakeholders and policy papers for identification of topic with high priority (‘hot topics’) Systematic literature search and selection of relevant studies on identified risk factors •Coding of studies •Analysis of risk factors on basis of coded studies •Synopses of risk factors, including accident scenarios The core output of this task are synopses of risk factors which will be available through the DSS. Within the synopses, each risk factor was analysed systematically on basis of scientific studies and is further assigned to one of four levels of risk (marked with a colour code). Essential information of the more than 180 included studies were coded and will also be available in the database of the DSS. Furthermore, the synopses contain theoretical background on the risk factor and are prepared in different sections with different levels of detail for an academic as well as a non-academic audience. These sections are readable independently. It is important to note that the relationship between road safety and road user related risk factors is a difficult task. For some risk factors the available studies focused more on conditions of the behaviour (in which situations the behaviour is shown or which groups are more likely to show this behaviour) rather than the risk factor itself. Therefore, it cannot be concluded that those risk factors that have not often been studied or have to rely more indirect and arguably weaker methodologies, e.g. self-reports , do not increase the chance of a crash occurring. The following analysed risk factors were assessed as ‘risky’, ‘probably risky’ or ‘unclear’. No risk factors were identified as ‘probably not risky’. Risky Probably risky Unclear • Influenced driving – alcohol • Influenced Driving – drugs (legal & illegal) • Speeding and inappropriate speed • Traffic rule violations – red light running • Distraction – cell phone use (hand held) • Distraction – cell phone use (hands free) • Distraction – cell phone use (texting) • Fatigue – sleep disorders – sleep apnea • Risk taking – overtaking • Risk taking – close following behaviour • Insufficient knowledge and skills • Functional impairment – cognitive impairment • Functional impairment – vision loss • Diseases and disorders – diabetes • Personal factors – sensation seeking • Personal factors – ADHD • Emotions – anger, aggression • Fatigue – Not enough sleep/driving while tired • Distraction – conversation with passengers • Distraction – outside of vehicle • Distraction – cognitive overload and inattention • Functional impairment – hearing loss (few studies) • Observation errors (few studies) • Distraction – music – entertainment systems (many studies, mixed results) • Distraction – operating devices (many studies, mixed results) The next step in SafetyCube’s WP4 is to identify and assess the effectiveness of measures and to establish a link to the identified risk factors. The work of this first task indicates a set of risk factors that should be centre of attention when identifying corresponding road safety measures (category ‘risky’)

Indentation fracture testing of nitrided layers on H11 tool steel

Nitriding and nitrocarburising treatments are well accepted methods of improving the wear performance of tool and die steels. However, our understanding of the relationship between nitriding process parameters, microstructure and fracture behaviour of the surface layers is far from complete. Vickers hardness indentations generate radial fractures in brittle surface layers, and it has been shown that the length of these cracks can be used to provide valuable information about the fracture toughness of these layers. This paper describes an investigation of the application of indentation fracture testing to nitrided and nitrocarburized HI 1 hot work tool steel. The results suggest that where a sufficiently thick compound layer has formed, this method has the potential to be applied as a pseudo non-destructive method of monitoring the fracture properties of treated surfaces on actual tool parts

Life Tree: Understanding the Design of Breathing Exercise Games

Regular breathing exercises can be a beneficial part of leading a healthy life. Digital games may have the potential to help people practice breathing exercises in an engaging way, however designing breathing exercise games is not well understood. To contribute to such an understanding, we created Life Tree as the culmination of three prototypal breathing games. Life Tree is a virtual reality (VR) game in which a player controls the growth of a tree by practicing pursed-lip breathing. We selected VR head-mounted display technology because it allows players to focus and limit external distractions, which is beneficial for breathing exercises. 32 participants played Life Tree and analysis of the collected data identified four key themes: 1) Designing Breathing Feedback; 2) Increasing Self-Awareness of Breathing and Body; 3) Facilitating Focused Immersion; and, 4) Engagement with Breathing Hardware. We used these themes to articulate a set of breathing exercise game design strategies that future game designers may consider to develop engaging breathing exercise games

Timing and propane dose of broadcast flaming to control weed population influenced yield of sweet maize (Zea mays L. var. rugosa)

Farmers are interested to produce sweet maize under organic production systems and propane flaming could be a potential alternative tool for weed control in organic sweet maize production. Therefore, the objective of this study was to investigate the response of sweet maize to broadcast flaming as influenced by propane dose and crop growth stage. Field experiments were conducted at the Haskell Agricultural Laboratory of the University of Nebraska, Concord, NE in 2008 and 2009 using five propane doses applied at three different growth stages of V2 (2-leaf), V5 (5-leaf) and V7 (7-leaf). The propane doses were 0, 13, 24, 44 and 85 kg ha(-1). The response of sweet maize to propane flaming was evaluated in terms of visual crop injury, effects on plant height, yield components (plants m(-2), tillers plant(-1), number of ears plant(-1), cob length and number of seeds cob(-1)) and fresh marketable yield. The response of different growth stages of sweet maize to propane doses was described by log-logistic models. Based on most parameters tested. V7 was the most tolerant while V2 was the least tolerant stage for broadcast flaming. The maximum yield reductions with the highest propane dose of 85 kg ha(-1) were 22%, 12% and 6% for V2, V5 and V7 stages, respectively. Furthermore, a 5% yield reduction was evident with 23,25 and 36 kg ha(-1) of propane for V2, V5 and V7 growth stages, respectively, suggesting that plants flamed at V7 stage can tolerate higher dose of propane for the same yield reduction compared to the other growth stages. We believe that flaming has a potential to be used effectively in organic sweet maize production if properly used

Yield and yield components of soybean [Glycine max (L.) Merr.] are influenced by the timing of broadcast flaming

Weed management is a major constraint in organic crop production. Propane flaming could be an additional tool for weed control in organic soybean production. The objective of this study was to investigate the response of soybean to broadcast flaming as influenced by propane dose and crop growth stage. We initiated a 2-year field study at the Haskell Agricultural Laboratory of the University of Nebraska, Concord, NE using five propane doses applied at four growth stages of VC (unfolded cotyledons), VU (fully unrolled unifoliate leaves), V2 (second trifoliate stage) and V5 (fifth trifoliate stage). The propane doses tested were 0, 13, 24,44 and 85 kg ha(-1). Flaming treatments were applied utilizing a custom-built research flamer mounted on the back of a four-wheeler driven at a constant speed of 6.4 km h(-1). The response of soybean to propane flaming was described by using log-logistic models on the basis of visual ratings of crop injury, yield components (plants m(-2), branches plant(-1), pods plant(-1), seeds pod(-1) and 100-seed weight) and grain yield. In general, soybean at VC stage was the most tolerant whereas VU stage was the most susceptible to broadcast flaming resulting in the highest visual crop injury, and the largest loss of yield and its components. The maximum yield reductions with the highest propane dose were 19%, 96%, 54% and 30% for VC, VU, V2 and V5 stages, respectively. An arbitrarily assigned 5% yield reduction was evident with 55, 13, 21 and 47 kg ha(-1) propane for VC, VU, V2 and V5 growth stages, respectively, suggesting that soybean flamed at VC stage can tolerate higher dose of propane for the same yield reduction compared to other growth stages. It appears that flaming has a potential to be used effectively in organic soybean production when conducted properly at VC stage