{"id":17810,"date":"2017-06-29T00:00:00","date_gmt":"2017-06-29T00:00:00","guid":{"rendered":"https:\/\/carsp.ca\/?p=17810"},"modified":"2022-10-29T18:58:48","modified_gmt":"2022-10-29T18:58:48","slug":"data-from-comprehensive-driving-evaluations-predictors-of-failing-a-road-test","status":"publish","type":"post","link":"https:\/\/carsp.ca\/en\/presentations-and-papers\/carsp-conference-acpser-toronto-2017\/data-from-comprehensive-driving-evaluations-predictors-of-failing-a-road-test\/","title":{"rendered":"Data from comprehensive driving evaluations: predictors of failing a road test"},"content":{"rendered":"Author(s): Alexander Crizzle<\/p>\n<h2>Slidedeck Presentation Only (no paper submitted):<\/h2>\n<p><a href=\"https:\/\/carsp.ca\/wp-content\/uploads\/2017\/06\/3C_2_Crizzle.pdf\">3C_2_Crizzle<\/a><\/p>\n<div class=\"su-divider su-divider-style-default\" style=\"margin:15px 0;border-width:1px;border-color:#ccc\"><\/div>\n<h2>Abstract:<\/h2>\n<p>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 &amp;amp; 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 \u00e2\u2030\u00a4 .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\u00c2\u00b114.9 years; 73% were male. Clients scored on average 21.9\u00c2\u00b14.6 on the MOCA, 69\u00c2\u00b180 and 240\u00c2\u00b1147 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 &amp;lt; .001), had worse scores on the MoCA (20.1 versus 23.5; p &amp;lt; .001), Trails A (57 versus 68 seconds; p &amp;lt; .05) and Trails B (182 versus 303 seconds; p &amp;lt; .001), respectively, and more likely to be categorized as high risk on the SIMARD (\u00cf\u20212 = 27.2; p &amp;lt; .001) and the UFOV risk index (\u00cf\u20212 = 36.7; p &amp;lt; .001). A logistic regression with all the variables above (N = 125; -2 Log Likelihood = 78.03; Nagelkerke R = .695) found that age (p &amp;lt; .001) and MoCA scores (p &amp;lt; .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.<\/p>\n<p><div class=\"su-divider su-divider-style-default\" style=\"margin:15px 0;border-width:1px;border-color:#ccc\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Alexander Crizzle<\/p>\n","protected":false},"author":163,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[341,346],"tags":[],"class_list":["post-17810","post","type-post","status-publish","format-standard","hentry","category-carsp-conference-acpser-toronto-2017","category-research-and-evaluation"],"acf":[],"_links":{"self":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts\/17810","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/users\/163"}],"replies":[{"embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/comments?post=17810"}],"version-history":[{"count":2,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts\/17810\/revisions"}],"predecessor-version":[{"id":18237,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/posts\/17810\/revisions\/18237"}],"wp:attachment":[{"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/media?parent=17810"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/categories?post=17810"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/carsp.ca\/en\/wp-json\/wp\/v2\/tags?post=17810"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}