title: Traffic violations versus driving errors of older adults: informing clinical practice.
abstract: Certain driving errors are predictive of crashes, but whether the type of errors evaluated during on-road assessment is similar to traffic violations that are associated with crashes is unknown. Using the crash data of 5,345 older drivers and expert reviewers, we constructed a violation-to-error classification based on rater agreement. We examined the effects of predictor variables on crash-related injuries by risk probability using logistic regression. Drivers' mean age was 76.08 (standard deviation = 7.10); 45.7% were women. Of drivers, 44.6% sustained crash-related injuries, and female drivers had a higher injury probability (44%) than male drivers (29%). Lane maintenance, yielding, and gap acceptance errors predicted crash-related injuries with almost 50% probability; speed regulation (34%), vehicle positioning (25%), and adjustment-to-stimuli (21%) errors predicted crash-related injuries to a lesser degree. We suggest injury prevention strategies for clinicians and researchers to consider for older drivers, especially older women.
authors.affiliation: Department of Occupational Therapy, College of Public Health and Health Professions, Institute for Mobility, Activity and Participation and the National Older Driver Research and Training Center, University of Florida, PO Box 100164, Gainesville, FL 32611-0164, USA. sclassen@phhp.ufl.edu
authors.forename: Sherrilene
authors.initials: S
authors.lastname: Classen
authors.forename: Orit
authors.initials: O
authors.lastname: Shechtman
authors.forename: Kezia D
authors.initials: KD
authors.lastname: Awadzi
authors.forename: Yongsung
authors.initials: Y
authors.lastname: Joo
authors.forename: Desiree N
authors.initials: DN
authors.lastname: Lanford
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data; D000368:Aged; D000369:Aged, 80 and over; D001334:Automobile Driving / Q000331:legislation & jurisprudence; D016208:Databases, Factual; D005260:Female; D005431:Florida; D006801:Humans; D016015:Logistic Models; D008297:Male; D013647:Task Performance and Analysis