The findings suggest early support for external validity for our driving simulator, indicating that the results of assessing driving errors when negotiating turns in the simulator can be generalized or transferred to the road under the same testing conditions. A follow-up study with larger sample size is needed to establish whether driving performance in the simulator is predictive of driving performance on the road.

In this pilot study among PD patients, the UFOV may be a superior screening measure (compared to other measures of disease, cognition, and vision) for predicting on-road driving performance but its rigor must be verified in a larger sample of people with PD.

The SMS differentiated between passing and failing drivers and can be used to inform clinical decision making.

To our knowledge, this is the first study to examine the medical predictors of failing a standardized road test. Advanced age and prolonged time on Trail Making Part B were the two major predictors of test failure and a lower Sum of Maneuvers Score. Our study also found that having a neurological diagnosis (primarily cerebrovascular and Parkinson's disease) predicted test failure. Medications from neurological class also predicted a lower Sum of Maneuvers Score. Further study needs to be done to explain the apparent protective effect of musculoskeletal conditions and hormonal medications.

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.