Using statistical modelling to analyze risk factors for severe and fatal road traffic accidents

Abstract

Road traffic accidents (RTAs) are still frequent events in the UK with severe/fatal RTAs leading to significant morbidity and mortality. Therefore, this study aimed to explore clusters of risk factors which affect the severity of RTAs in the UK. A retrospective analysis of 76,334 driver-level records between 2005 and 2014 was conducted. Two methods were used: ‘partially constrained generalized logistic regression models’ and ‘classification and regression tree’ (CART) analysis in order to identify individual factors and combinations of risk factors relating to severity of accidents. Several established risk factors were confirmed which contribute to the severity of RTAs. Specific combinations of factors were identified which were more likely to lead to fatal accidents: the involvement of one older person, one or no cycles involved, speed limit over 40 mph in culmination with several other factors. This study reaffirmed risk factors relating to severity of RTAs in the UK, but also established combinations of risk factors which led to the most severe outcomes allowing for targeting of accident-prevention measures. In addition, this study demonstrates the use of CART analysis which can be used in wider public health evaluations where multiple risk factors are at play.

Keywords:

• Road traffic accidents
• fatal accidents
• morbidity
• mortality
• safety
• transport
• police

Acknowledgements

We would like to thank Anindya Banerjee and Eddie Kane for comments on a draft of this article and Norfolk and Suffolk Constabulary for assisting and providing us with accident data.

Disclosure statement

The authors declare no competing interests.

Additional information

Funding

This work was supported by funding from Norfolk and Suffolk constabulary. KR also received funding from ESRC for a doctoral studentship during which time this article was written.