Can high-risk older drivers be identified through performance-based measures in a Department of Motor Vehicles setting?

摘要

OBJECTIVES: To evaluate the relationship between performance-based risk factors and subsequent at-fault motor vehicle collision (MVC) involvement in a cohort of older drivers. DESIGN: Prospective cohort study. SETTING: Motor Vehicle Administration (MVA) field sites in Maryland. PARTICIPANTS: Of the 4,173 older drivers invited to participate in the study, 2,114 individuals aged 55 to 96 agreed to do so. These analyses focus on 1,910 individuals recruited through MVA field sites. MEASUREMENTS: Gross Impairment Screening Battery, which included Rapid Pace Walk, Head/Neck Rotation, Foot Tap, Arm Reach, Cued Recall, Symbol Scan, Visual Closure subtest of the Motor Free Visual Perception Test (MVPT), Delayed Recall, and Trail Making Test with an Abbreviated Part A and standard Part B; Useful Field of View (UFOV) subtest 2; a Mobility Questionnaire; and MVC occurrence. RESULTS: In drivers aged 55 and older with intact vision (20/70 far visual acuity and 140 degrees visual field), age, sex, history of falls, and poorer cognitive performance, as measured using Trails B, MVPT, and UFOV subtest 2, were predictive of future at-fault MVC involvement. After adjusting for annual mileage, participants aged 78 and older were 2.11 as more likely to be involved in an at-fault MVC, those who made four or more errors on the MVPT were 2.10 times as likely to crash, those who took 147 seconds or longer to complete Trails B were 2.01 times as likely to crash, and those who took 353 ms or longer on subtest 2 of the UFOV were 2.02 times as likely to incur an at-fault MVC. Older adults, men, and individuals with a history of falls were more likely to be involved in subsequent at-fault MVCs. CONCLUSION: Performance-based cognitive measures are predictive of future at-fault MVCs in older adults. Cognitive performance, in particular, is a salient predictor of subsequent crash involvement in older adults. High-risk older drivers can be identified through brief, performance-based measures administered in a MVA setting.