Findings from a project linking hospital and police road casualty data to identify culpable drivers in severe collisions has been published to help inform future road safety work.

The TRIP project looked in detail at crashes that cause severe injury and death, in particular examining the types of drivers that are involved in these crashes. This innovative project brought together partners from the local authority, emergency services and Cambridge University Hospitals to explore whether prevention strategies targeted at groups of drivers similar to those considered culpable for crashes, rather than targeting groups who are likely to be injured, could have an impact on road safety.

The work was funded by the Road Safety Trust and the Police and Crime Commissioner for Cambridgeshire and Peterborough. 

An extension to the project to examine the delivery of research-led practice in road safety, funded by the Eastern Highways Alliance, is expected to continue to 2023/24.

Related publications

Details of the project are also available on the Road Safety Trust website

James Nunn, Jo Barnes, Andrew Morris, Emily Petherick, Roderick Mackenzie & Matt Staton (2018) Identifying MAIS 3+ injury severity collisions in UK police collision records, Traffic Injury Prevention, 19:sup2, S142-S144, DOI: 10.1080/15389588.2018.1532205

Executive summary

Prior to 2012 the UK saw a sustained reduction in road casualties where deaths from road collisions nearly halved. However, since then there has been a general plateauing of road deaths per year and incidents of serious injuries have also followed this trend. In 2018, 6.5% of all national killed and seriously injured casualties were fatalities, but in Cambridgeshire it was 7.6%. Additionally, the rate in 2018 for fatalities per 100,000 population in Great Britain was 2.8 for Cambridgeshire this rate was 5.9. The Cambridgeshire and Peterborough Road Safety Partnership sought to explore new ways that road safety interventions can be delivered to reduce serious and fatal injuries resulting from collisions. The notion being to target specific drivers who are responsible for the collisions based on geodemographic profiles. This study is a proof-of-concept study exploring the available data and methods involved to enable routine use of geodemographic profiling in road safety interventions.

Aim

The aim of the study was to inform innovative approaches to road safety interventions to reduce the numbers of killed or seriously injured in road collisions.

Methods

A series of methods were used to identify Cambridgeshire drivers who were culpable of causing the collisions and if they were different to non-culpable drivers.

• The police collision database (STATS19) was linked with hospital trauma audit research network (TARN) data for a five-year period to identify Cambridgeshire resident drivers who were involved in clinically defined serious injury collisions.
• All drivers were culpability scored and categorised as being culpable, contributory, or non-culpable for the collisions. To achieve this the STATS19 variables were mapped on to an existing tool.
• Full geodemographic profiles were appended to the drivers with a culpability score.
• Analysis of the data investigated the culpability and geodemographic profiles of the drivers and explored differences in Cambridgeshire drivers to inform road safety interventions.

Results

The study identified 564 drivers involved in a serious injury or fatal collision on the Cambridgeshire road network and had a culpability score. The mean age of drivers was 43 years (SD17) and most were male (434, 77%). For these drivers, the significant factors impacting on the odds of being culpable were their age, being under 26 or over 76 showed higher odds compared to the mid-aged (46-55years) as did being the rider of a motorcycle compared to cars. Being the driver of an agricultural vehicle or goods vehicle showed lower odds of being culpable compared to cars as did residing at an address with an Acorn Type designation of 6 (‘financially comfortable families’) compared to the most frequent Type 23 (‘owner occupiers in small villages’). When considering only Cambridgeshire resident motor vehicle drivers (367 (65%)) the significant factors impacting on the likelihood of being culpable, were being the rider of a motorcycle compared to a car or an Index of Multiple Deprivation (IMD) in the 6th decile compared to the most frequent IMD 5th decile. Similarly, to all drivers those Cambridgeshire resident drivers living at an address with an Acorn type designation of 6 had lower odds compared to Type 23 of being culpable. In general non-resident drivers were involved in more fatal collisions (49%) compared to Cambridgeshire residents (42%). The use of risk indexation was explored for the geodemographic Types to identify if there were any Types overrepresented in the study sample compared to the population of Cambridgeshire. Overrepresentation on the risk index determines the extent to which a Type is found culpable compared to the general population of Cambridge. Type 41 culpable drivers were high frequency for fatal collisions and overrepresented compared to the general Cambridgeshire population (risk index >300). Type 41 describes ‘Labouring semi-rural estates.’ For serious collisions Type 23 were high frequency and overrepresented and had a risk index >200. Interestingly there were some Types underrepresented on the risk index specifically Type 10 (Better-off villagers) for fatal collisions and Type 5 (Wealthy countryside commuters) for both fatal and serious (MAIS3+[1]) collisions, suggesting lower risk of culpability. This would be interesting to explore further with larger datasets to understand how typical the over or underrepresentation of culpable drivers is.

Conclusion

Overall, the methods have allowed for culpable drivers causing clinically defined serious injuries to be identified residing in Cambridgeshire. STATS19 has been mapped to a culpability tool for the first time and is being validated for use on a large dataset. The results indicate the potential if using this methodology to identify drivers causing collisions and to use this knowledge to target specific road safety interventions. However, the sample was small and any inferences in the data need to be made with caution as the focus has been on serious and fatal injury collisions and not those with minor or damage only outcomes and are limited to Cambridgeshire.

Road Safety implications

This method would enhance road safety professionals’ opportunities to develop targeted innovative road safety interventions at the culpable drivers. However, the automaticity of determining culpability from STATS19 variables is required before the method can become user friendly in the real world. It would also be beneficial to explore the nuances of the geodemographic Types identified in the study with residents from the profile Types. This would identify whether the profile descriptions have any similarities with residents and determine the best method of delivery of road safety interventions. This would enhance their effectiveness at reaching the target audience and subsequent reduction in serious injury and fatal collisions.

Notes

This study was undertaken as post graduate research for the award of Doctor of Philosophy (PhD).

[1] MAIS3+ refers to a clinically defined serious injury for example fractured femur.