Traffic Violations Versus Driving Errors of Older Adults: Informing Clinical Practice

Author:

Classen Sherrilene1,Shechtman Orit2,Awadzi Kezia D.3,Joo Yongsung4,Lanford Desiree N.5

Affiliation:

1. Sherrilene Classen, PhD, MPH, OTR/L, is Assistant Professor, Department of Occupational Therapy, College of Public Health and Health Professions; Adjunct Assistant Professor, Department of Epidemiology and Biostatistics; Affiliate Assistant Professor, Department of Behavioral Science and Community Health, College of Public Health and Health Professions; and Director, Institute for Mobility, Activ

2. Orit Shechtman, PhD, is Associate Professor, Department of Occupational Therapy, College of Public Health and Health Professions, and an affiliated researcher with the Institute for Mobility, Activity and Participation and the National Older Driver Research and Training Center, University of Florida, Gainesville

3. Kezia D. Awadzi, PhD, is Postdoctoral Associate, Department of Occupational Therapy, College of Public Health and Health Professions, and an affiliated researcher with the National Older Driver Research and Training Center, University of Florida, Gainesville

4. Yongsung Joo, PhD, is Assistant Professor, Department of Statistics, Dongguk University, Seoul, Korea

5. Desiree N. Lanford, MOT, CDRS, is Staff Occupational Therapist, Department of Occupational Therapy, College of Public Health and Health Professions, and Certified Driving Rehabilitation Specialist, Institute for Mobility, Activity and Participation and National Older Driver Research and Training Center, University of Florida, Gainesville

Abstract

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.

Publisher

AOTA Press

Subject

Occupational Therapy

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