Research Papers

Data from comprehensive driving evaluations: predictors of failing a road test

Version 1
Date added June 26, 2017
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Category 2017 CARSP XXVII Toronto
Tags Research and Evaluation, Session 3C
Author/Auteur Alexander Crizzle
Stream/Volet Research and Evaluation

Slidedeck Presentation Only (no paper submitted)



The increasing number of older drivers presents a major public health concern given that older adults are more likely to develop functional impairments associated with age-related medical conditions that can impair their ability to drive safely. Hence, determining the most effective means to identify, screen and assess medically at-risk drivers has become a major concern for clinicians who are responsible for identifying these drivers through medical screening. The purpose was to collect data from drivers referred for a comprehensive driving evaluation to determine predictors of failing the road test. Data was collected from one driving assessment center in South-Western Ontario. Data was collected retrospectively from 2012-2015 and prospectively from 2015 to October 2016. In total, there were 175 client records containing information concerning demographics, clinical test scores (Montreal Cognitive Assessment [MoCA] Screen for the Identification of Cognitively Impaired Medically At-Risk Drivers [SIMARD], Trails A & B, Useful Field of View [UFOV]) and on-road pass/fail outcomes. Descriptive statistics (mean and standard deviation) and frequencies (valid percent) were used to describe the sample. Chi-square and independent t-tests were used to compare categorical and continuous variables, respectively. A binary logistic regression was performed to examine predictors of failing a road test. All statistical tests were considered significant at p ≤ .05. Of the 175 assessments, clients were primarily referred due to concerns related to mild cognitive impairment (26%), dementia (17%), stroke (19%) and traumatic brain injury (5%). The average age of clients was 69±14.9 years; 73% were male. Clients scored on average 21.9±4.6 on the MOCA, 69±80 and 240±147 seconds on Trails A and B, respectively. Clients were classified on the UFOV as being very low to low (55%), low/moderate (18%), or moderate to high risk (26%). On the SIMARD, clients were classified as being low risk (18%), require further testing (61%) and high risk (21%). Approximately 42% failed the road test. Those who failed were significantly older (62.5 versus 77.8 years; p < .001), had worse scores on the MoCA (20.1 versus 23.5; p < .001), Trails A (57 versus 68 seconds; p < .05) and Trails B (182 versus 303 seconds; p < .001), respectively, and more likely to be categorized as high risk on the SIMARD (χ2 = 27.2; p < .001) and the UFOV risk index (χ2 = 36.7; p < .001). A logistic regression with all the variables above (N = 125; -2 Log Likelihood = 78.03; Nagelkerke R = .695) found that age (p < .001) and MoCA scores (p < .05) predicted failing the road test. The findings suggest that common tools can be used by clinicians across clients as part of the comprehensive driving evaluation. However, optimal cut-points for clinical tests are needed to better inform clinical practice. The MoCA should be strongly considered as part of driver screening and comprehensive driving evaluations.

Alexander Crizzle