Clinician’s Guide to Assessing and Counseling Older Drivers, 3rd Edition

NHTSA · 2015 · ROSA P / United States. National Highway Traffic Safety Administration

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Summary

This document, the third edition of the *Clinician’s Guide to Assessing and Counseling Older Drivers*, addresses the critical public health challenge of maintaining safe mobility among the rapidly growing population of older adults. Motivated by the fact that motor vehicle crashes are a leading cause of injury-related death for adults aged 65–74 and the second leading cause for those aged 75–84, the guide aims to provide health care practitioners with evidence-based tools to prevent crashes and facilitate safe driving or timely cessation. The aging population is expected to double by 2050, with older adults driving more miles than previous generations, yet they face higher fatality rates per mile driven due to increased frailty and comorbidities. The guide was developed through a cooperative agreement between the American Geriatrics Society and the National Highway Traffic Safety Administration, updating earlier versions created by the American Medical Association. It adopts an interprofessional approach, outlining specific roles for physicians, nurses, pharmacists, occupational therapists, and social workers in screening, assessing, and counseling older drivers. The methodology centers on the Plan for Older Drivers’ Safety (PODS) algorithm, which structures clinical care into three levels: primary prevention to maintain driving ability, secondary prevention to restore skills through rehabilitation, and tertiary prevention to manage irreversible loss of driving skills. The guide provides a toolbox of office-based functional assessments, such as the Clinical Assessment of Driving Related Skills (CADReS), alongside resources for navigating ethical, legal, and state-specific licensing issues. Key findings and recommendations emphasize that while driving cessation is inevitable for many older adults, it is often associated with negative outcomes such as social isolation, decreased activity, and depression. The guide highlights that many older drivers self-regulate their behavior by avoiding night driving or heavy traffic, but clinical intervention is necessary when functional deficits arise from age-related changes or medical conditions. The text provides specific guidance on identifying medical conditions and medications that impair driving, offering strategies to optimize treatment and functional ability. It also includes practical resources for clinicians, such as Current Procedural Terminology codes for assessment procedures, sample letters for medical review boards, and educational materials for patients and caregivers to assist in transitioning to alternative transportation. The significance of this guide lies in its provision of a standardized, comprehensive framework for clinicians to address older driver safety within routine care. By integrating screening, assessment, and counseling into everyday practice, the guide seeks to prolong safe driving life expectancy while ensuring a supported transition to driving retirement when necessary. This approach aims to balance individual independence with public safety, reducing crash risks and mitigating the adverse health impacts associated with abrupt driving cessation. The guide serves as a vital resource for the clinical team to manage the complex interplay of medical, functional, and social factors affecting older adult mobility.

Key finding

The guide provides a comprehensive clinical toolkit and algorithm for healthcare providers to evaluate the fitness to drive of older adults and implement interventions to maintain safety or manage driving cessation.

Methodology

review

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• mci dementia driving
• fitness to drive assessment
• older driver retraining
• cognitive impairment
• older drivers
• medical conditions

Information type

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• Methodological Resource: validation psychometrics
• Theoretical Contribution: computational model