Big Data Analysis of Vision Screening Standards Used to Evaluate Fitness to Drive

Visual acuity (VA) assessment is the most commonly performed vision screening method for drivers. The standards and repeat assessment intervals used, however, are arbitrary, lack an evidence base and are highly variable across different countries. This study utilizes the power of Big Data to provide evidence-based recommendations for standardized driver vision screening. Anonymized electronic medical record data were gathered from 40 Irish optometry practices comprising 81,184 unique patients. A Kaplan–Meier Survival (KMS) analysis was used to determine the effect of increasing age and time since screening on the likelihood of passing the VA standard for driving. A logistic function was fit to assess the effect of varying the minimum VA standard required to drive on the screening pass rate within the population. The likelihood of failing repeat screening increased as a function of time since initial screening for all age groups (χ2 =1447, df = 6, p VA-based screening should take place at regular intervals for all drivers, not just those over 70. Re-screening intervals should be based on age, with shorter intervals for older drivers due to the combined effect of age and time on the likelihood of passing the driving VA standards. The most commonly used standard of 0.3 logMAR results in a minimal number of potential drivers being excluded from driving.