Crash cost estimates for European countries, deliverable 3.2 of the H2020 project SafetyCube

Crash cost estimates for European countries, deliverable 3.2 of the H2020 project SafetyCub

SafetyCube: Building a decision support system on risks and measures

The EU research project SafetyCube (Safety CaUsation, Benefits and Efficiency) is developing an innovative road safety Decision Support System (DSS) collecting the available evidence on a broad range of road risks and possible countermeasures. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs

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

The European road safety decision support system on risks and measures

The European Road Safety Decision Support System (roadsafety-dss.eu) is an innovative system providing the available evidence on a broad range of road risks and possible countermeasures. This paper describes the scientific basis of the DSS. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation instrument (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs

Description of data-sources used in SafetyCube. Deliverable 3.1 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) that 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 deliverable describes the available data in the form of an inventory of databases that can be used for analyses within the project. Two general types of data are available: one describing the involvement of different components for the road safety (vehicles, infrastructure, and the road user) and one describing the injury outcomes of a crash. These two database categories are available to the partners of SafetyCube and gathered in two excel tables. One table contains traffic databases (accident and naturalistic driving studies) and the second table contains injury databases. The tables contain information on 58 and 35 variables, respectively. The key information describing the databases that was needed for the inventory were items such as: Type of data collected (crashes, injuries, etc.) Documentation of the variables Sampling criteria for the data collected SafetyCube partners with access to the data Extent of data access (raw data vs. summary tables) The tables contain 36 traffic accident databases, five naturalistic driving studies or field-tests and 22 injury databases where of four were coded in both sheets

Health burden of serious road injuries in the Netherlands

<p><b>Background</b>: The consequences of injuries in terms of disabilities and health burden are relevant for policy making. This article provides an overview of the current knowledge on this topic and discusses the health burden of serious road injuries in The Netherlands.</p> <p><b>Methods</b>: The overview of current knowledge on disabilities following a road crash is based on a literature review. The health burden of serious road injuries is quantified in terms of years lived with disability (YLD), by combining incidence data from the Dutch hospital discharge register with information about temporary and lifelong disability.</p> <p><b>Results</b>: Literature shows that road traffic injuries can have a major impact on victims' physical and psychological well-being and functioning. Reported proportions of people with disability vary between 11 and 80% depending on the type of casualties, time elapsed since the crash, and the health impacts considered. Together, all casualties involving serious injuries in The Netherlands in 2009 account for about 38,000 YLD, compared to 25,000 years of life lost (YLL) of fatalities. Ninety percent of the burden of injury is due to lifelong consequences that are experienced by 20% of all those seriously injured in road accidents. Lower leg injuries and head injuries represent a high share in the total burden of injury as have cyclists that are injured in a crash without a motorized vehicle. Pedestrians and powered 2-wheeler users show the highest burden of injury per casualty.</p> <p><b>Conclusion</b>: Given their major impacts and contribution to health burden, road policy making should also be aimed at reducing the number of serious road injuries and limiting the resulting health impacts.</p

Preliminary guidelines for priority setting between measures, Deliverable 3.4 of the H2020 project SafetyCube (Safety CaUsation, Benefits and Efficiency).

The present deliverable describes the economic assessment of counter measures. Cost-effectiveness analysis and cost-utility analysis are compared to cost-benefit analysis. Cost-effectiveness analysis helps to estimate the costs per prevented fatality or injury. To evaluate the effectiveness in terms of different levels of severity jointly, one has to conduct a cost-utility analysis, where fatality reduction and injury reduction are brought together on a joint scale: quality adjusted live years (QALY) saved. QALYs represent the years of life lost due to fatalities and the quality of life loss resulting from injuries. Cost-benefit analysis also allows the joint evaluation of measures’ effectiveness in reducing crashes of different severity. Moreover it provides information on the socio-economic return of counter measures, and in principle allows to include side-effects into the analysis. The valuation of other possible impacts of road safety measures is beyond the scope of SafetyCube, but presentation in terms of cost-benefit ratios allows for the post hoc inclusion of other impacts if DSS users have estimates of these. In the discussion of decision criteria within cost-benefit analysis it is demonstrated that measures with a high cost-benefit ratio (benefits/costs) do not necessarily have a large net-effect (benefits — costs). The net-present value will favour measures with large benefits even if they come at a relatively large cost, while the cost benefit ratio will favour measures with the best value for money, even if their actual benefits are relatively small (e.g., because they are targeted at a small group of crashes). The meaning of costs in the framework of economic welfare theory (the basis of cost-benefit analysis) is not necessarily the same as in everyday language. In this context concepts like opportunity costs and discounting are discussed. Opportunity costs (the value of things you could have done with the money or resources otherwise) are usually approximated by the market price. The exception are costs that are payed from tax-money, which are brought into cost benefit analysis at a higher rate. Discounting is used to bring costs made at different points in time to the same present value. There is a relation between the discount rate and a preference for short-term vs. long term projects. For the estimation of the cost of measures, different components and data sources for these costs are discussed with examples from infrastructure and vehicle measures. Furthermore, the report presents an overview and classification of crash costs components and estimation methods. One of the biggest components are the human costs. These are an indication of how much the prevention of crashes is worth for us (the people), which is measured by the willingness to pay method. Other costs are estimated by the restitution method (what are the costs to compensate the damage done) and the human capital approach (how much benefit would the victim have produced). The information on economic efficiency assessment will be integrated into the SafetyCube Decision support system by means of a cost-benefit calculator that is based on the costs of measures collected in the analysis work packages (WP4, 5, 6) and costs of crashes collected in WP3. (Author/publisher