Indicators of Crash Risk in Older Adults: A Longitudinal Analysis From the ACTIVE Study

Abstract

Objective: This study aims to examine indicators of crash risk longitudinally in older adults ( n = 486). Method: This study applied secondary data analyses of the 10 years of follow-up for the ACTIVE study combined with state-recorded crash records from five of the six participating sites. Cox proportional hazards models were first used to examine the effect of each variable of interest at baseline after controlling for miles driven and then to assess the three cognitive composites as predictors of time to at-fault crash in covariate-adjusted models. Results: Older age, male sex, and site location were each predictive of higher crash risk. Additionally, worse scores on the speed of processing cognitive composite were associated with higher crash risk. Discussion: Results support previous findings that both age and male sex are associated with higher crash risk. Our significant finding of site location could be attributed to the population density of our testing sites and transportation availability.