In English

The Effect of Advanced Automatic Collision Notification (AACN) on Road Fatality Reduction in Sweden

Jonathan Jonsson
Göteborg : Chalmers tekniska högskola, 2015. Diploma work - Department of Applied Mechanics, Chalmers University of Technology, Göteborg, Sweden, ISSN 1652-8557; 2015:50, 2015.
[Examensarbete på avancerad nivå]

Road traffic accidents account for approximately 1.3 million fatalities each year and are the eighth most common cause of death globally and the leading cause of death for people between the age of 15 and 29. The outcome of a collision is affected by actions taken before, during, and after the collision. By optimizing these actions fatalities and injuries in traffic can be reduced. In addition to injury reduction due to active and passive safety systems, emergency medical service providers play an important role for the medical outcome of the persons involved in a collision. Advanced Automatic Collision Notification (AACN) is a system that, given a collision, can establish a communication link with the rescue services and forward the collision location as well as an estimation of the injury severity of the occupants involved. An AACN system is thus able to provide information to aid pre-hospital triage and give the emergency service operator information that is vital when deciding on appropriate action. Using this information it is more likely that appropriate medical service units can be dispatched to the collision scene and that patients in need of trauma care can be identified at an early stage, possibly enabling swifter transport to a medical facility with adequate trauma care level. To evaluate the potential benefit of AACN in Sweden a benefit analysis based on accidents during the years 2006 to 2014 was conducted. Two different databases were used: 1) the statistical database STRADA (Swedish Traffic Accident Data Acquisition) and 2) the in-depth database of fatal accidents in the Swedish road transport system. The two databases were matched to identify the cases relevant for the analysis. Variables assumed to affect the outcome were selected and included in a multivariable logistic regression model. Thereafter, backward selection with stepwise exclusion of variables with p-value > 0.1 was carried out to obtain the final model. In addition to exclusion based on significance, variables with an estimated effect that were not consistent with previous research were excluded. Using the final model an estimated fatality reduction due to AACN was obtained by calculating the probability to die without AACN (actual outcome) and compare it to the probability to die when using AACN (alternative outcome). The variables ‘admission to trauma center’, ‘age’ and ‘injury severity’ were identified as significant. Based on regression coefficients the effect of trauma center admission was associated with an odds ratio of 0.781 (95% CI = 0.609-1.003), thus beneficial. With additional restrictions (distance and AACN performance) applied to cases with alternative outcome, AACN was estimated to reduce road fatalities by 8.6% (95% CI = -0.3-16.4%). To further improve the estimation model a better defined trauma classification is needed along with additional accident data, possibly obtained with a better match between STRADA and in-depth cases.

Nyckelord: Advanced Automatic Collision Notification, AACN, post-crash, injury prediction, fatality reduction, hospital classification, emergency medical service

Publikationen registrerades 2015-07-01. Den ändrades senast 2015-07-01

CPL ID: 219222

Detta är en tjänst från Chalmers bibliotek