September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Big data and primary eye care: road accident reporting data and vision in the UK
Author Affiliations & Notes
  • Michael Bowen
    Research, The College of Optometrists, London, United Kingdom
  • Carol Hawley
    Medical School, University of Warwick, Warwick, United Kingdom
  • Claire Roberts
    Birmingham, Solihull and Blackcountry, Local Eye Health Network, Birmingham, United Kingdom
    Medical School, University of Warwick, Warwick, United Kingdom
  • Tanya Fosdick
    Research, Road Safety Analysis (MAST), Birmingham, United Kingdom
  • Footnotes
    Commercial Relationships   Michael Bowen, None; Carol Hawley, None; Claire Roberts, None; Tanya Fosdick, None
  • Footnotes
    Support  This research was funded by The College of Optometrists
Investigative Ophthalmology & Visual Science September 2016, Vol.57, No Pagination Specified. doi:
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      Michael Bowen, Carol Hawley, Claire Roberts, Tanya Fosdick; Big data and primary eye care: road accident reporting data and vision in the UK. Invest. Ophthalmol. Vis. Sci. 2016;57(12):No Pagination Specified. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose : Road Safety Analysis UK has developed MAST Online an analysis tool for exploring national road traffic collision data (STATS19) collected by police officers at the scenes of RTAs. Attending officers can record data about contributing factors (CF) to collisions, including: 'uncorrected defective eyesight'; 'illness or disability' and 'failed to look properly'. Data on these contributory factors, when combined with socio-demographic data, provide scope to explore relationships between driver age, accident type and factors relating to drivers' vision and health.

Methods : The MAST data tool was used to examine 7 years of CF data from the UK Department of Transport (2006 - 2013). MAST contains complete UK STATS19 collision data - including RTAs resulting in fatal, serious and slight injuries - all severities were included in the analysis. Four groups were identified based on eyesight requirements for driver licenses: 'normal' (included cars, motorbikes, taxis), 'specialist' (included buses and HGVs), 'other' (included horses, bicycles and mobility scooters) and 'pedestrians'. Data was analysed by CFs including uncorrected defective eyesight, failed to look properly, dazzling headlights, dazzling sun, illness / disability, failed to judge other's path or speed.

Results : During 2006 - 2013, for England, Scotland and Wales there were 1,295,540 police reported injury collisions recorded on STATS19. Of these 1,008,929 (78%) had CFs recorded by the attending officer. No difference was found between the rates at which CFs were recorded for drivers <60 years and drivers >60 years. 'Failed to look properly' was the most common CF recorded (328,077); nearly 50% of drivers >75 years received this CF. 'Uncorrected defective eyesight' is rarely recorded as a CF (1,679 instances) but there were large differences between drivers <60 and >60 - this CF is not an issue for specialist drivers (0.02% coded with this CF) for whom a sight test is required to maintain their licenses.

Conclusions : This study has shown an association between injury collisions and visual impairment and health. It highlights the value of large data sets, but is also limited by the nature of the way STATS19 data is entered - because of this it is likely that uncorrected defective eyesight is under-recorded as a CF, though more research is needed to explore this.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.


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