Marco Amorim

Risk factors affecting injury severity determined by the MAIS score

ABSTRACT Objective: Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, called the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score. Method: In this study, the MAIS score was derived from the International Classification of Diseases. The European Union adopted an MAIS score equal to or greater than 3 as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital data sets from the Porto metropolitan area of Portugal over a 6-year period (2006–2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyze their impact on the injury severity classified according to LHS. Results: This study showed the impact of variables, such as the presence of blood alcohol, the use of protection devices, the type of crash, and the site characteristics, on the injury severity classified according to the MAIS score. Additionally, the sex and age of the victims were analyzed as risk factors, showing that elderly and male road users are highly associated with MAIS 3+ injuries. The comparison between the marginal effects of the variables estimated by the MAIS and LHS models showed significant differences. In addition to the differences in the magnitude of impact of each variable, we found that the impact of the road environment variable was dependent on the injury severity classification. Conclusions: The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.

Linking Police and Hospital Road Accident Records How Consistent Can It Be

This paper presents the description of various steps to be applied in the linkage of road traffic accident records through a case study from the city of Porto, Portugal. The complexity of this process stems primarily from several issues found in the data sets: mistakes and missing values were frequently detected, and only a few common data fields could be matched by the linkage process. This study used the application of a mixed deterministic and weight-based probabilistic method to link police and hospital records. The tolerance calibration and weights computation were critical for the final linkage rate as well as for the correct matching of the results. The results obtained lay within the range of rates found by other authors. Furthermore, to improve the record linkage results, a validation process based on the emergency ambulance data was performed. Despite missing values, 98% of the matched records were verified as true matches. Finally, a preliminary investigation of bias after data linkage is described; it shows that the variables selected for comparison indicate similar statistical values. The main outcome of this study is a road accident linkage process that can be adapted, developed, and applied in different contexts and that aims to promote development of police, hospital, and emergency ambulance data in Portugal and other countries. Additional development is planned for each step presented in this paper.

Emergency Medical Service Response: Analyzing Vehicle Dispatching Rules

In an era of information and advanced computing power, emergency medical services (EMS) still rely on rudimentary vehicle dispatching and reallocation rules. In many countries, road conditions such as traffic or road blocks, exact vehicle positions, and demand prediction are valuable information that is not considered when locating and dispatching emergency vehicles. Within this context, this paper presents an investigation of different EMS vehicle dispatching rules by comparing them using various metrics and frameworks. An intelligent dispatching algorithm is proposed, and survival metrics are introduced to compare the new concepts with the classic ones. This work shows that the closest idle vehicle rule (classic dispatching rule) is far from optimal and even a random dispatching of vehicles can outperform it. The proposed intelligent algorithm has the best performance in all the tested situations where resources are adequate. If resources are scarce, especially during peaks in demand, dispatching delays will occur, degrading the system’s performance. In this case, no conclusion could be drawn as to which rule might be the best option. Nevertheless, it draws attention to the need for research focused on managing dispatch delays by prioritizing the waiting calls that inflict the higher penalty on the system performance. Finally, the authors conclude that the use of real traffic information introduces a considerable gain to the EMS response performance.

Modelling the operating speed in segments of two-lane highways from probe vehicle data: a stochastic frontier approach

Most of the existing operating speed statistical models are applicable to individual design elements, particularly horizontal curves and tangents. A segment approach to operating speed has rarely been followed, with a few exceptions mainly related to the performance assessment of urban and freeway corridors, or design consistency studies using speed profiles built from successive design elements. This study introduces a new model to predict operating speeds in segments of two-lane highways. The maximum operating speed is given by a stochastic frontier function of the average daily traffic and road geometrics; the asymmetric disturbance accounts for the diversity in drivers’ behaviour and vehicle characteristics, allowing estimating any percentile speed. The model was calibrated using probe vehicle data from noncongested roads. The accuracy of the average daily traffic in representing the actual driving conditions was further validated using simultaneous speed-traffic measurements. The new model aims to assist practitioners in the evaluation of design consistency from a macroscopic perspective since the early stages of road planning and design, as well as to support the definition of speed limits at new or existing infrastructures.

Exploring clinical metrics to assess the health impact of traffic injuries

ABSTRACT In order to allow a deep knowledge of the nonfatal injuries, recently the European Commission adopted the maximum abbreviated injury scale classification which is based on medical diagnosis. This classification will open the door to a new source of information based on international hospital data such as diagnosis-related group and international classification of diseases. In this study, we seek to explore these clinical metrics, which are used to describe the diagnosis and the medical treatment, and to infer consequences of crashes mainly through the costs and severity. Therefore, statistical analyses were applied using generalized linear models selected depending on the type of response variable, i.e. discrete or continuous. Relationships between these metrics were identified revealing for instance that head is the region of the body associated with high severity as well as to higher health care costs. Additionally, a discussion is presented regarding study results and future developments of clinical metrics are pointed out.