A Meta-analysis of Brief, Non-computerized Cognitive Screening Tools for Predicting Unsafe Driving Among Older Adults

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Ward Vanlaar, PhD Anna McKiernan Heather McAteer Robyn Robertson, M.C.A. Daniel Mayhew, M.A. Steve Brown Erin Holmes David B. Carr, MD http://orcid.org/0000-0003-2641-3850

Abstract

Seniors represent the fastest growing population group in Canada and the United States. Given the expected increase in elderly drivers with dementia, efficient and effective screening for cognitive impairment will become more important. In light of this need, the objective of this study was to systematically review and meta-analyze the literature for brief, non-computerized cognitive screens that were evidenced-based and could be easily adopted in a driver license renewal or fitness-to-drive setting to determine their predictive value to identify unsafe driving. Studies were considered that examined road tests, driving simulator assessment or motor vehicle crashes as primary outcomes and 9 studies identifying 10 separate tests were identified based on our inclusion criteria. A small to medium-sized, significant pooled effect of 1.94 was found, meaning that on average, when cognitive screening tools predict a driver is unsafe, there is a 94% greater chance that this driver will indeed exhibit unsafe driving behavior. These results suggest that brief, paper and pencil cognitive screens may be feasible and efficiently adopted either during routine license renewal or during fitness to drive evaluations. Further studies are warranted in regards to their acceptability, reliability and validity before widespread dissemination.

Article Details

How to Cite
VANLAAR, PHD, Ward et al. A Meta-analysis of Brief, Non-computerized Cognitive Screening Tools for Predicting Unsafe Driving Among Older Adults. Medical Research Archives, [S.l.], v. 5, n. 4, apr. 2017. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/1035>. Date accessed: 19 july 2024.
Keywords
cognitive screening; meta-analysis; older drivers; elderly drivers; multilevel model
Section
Research Articles

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