Using machine learning to identify unique predictors of alcohol and cannabis impaired driving.

BACKGROUND

Alcohol- and cannabis-impaired driving remain major public health concerns, particularly among young adults. Although prior studies have identified numerous risk factors, most have focused on limited subsets of predictors, restricting a broader understanding of impaired driving. This study applied machine learning to identify salient predictors of alcohol- and cannabis-impaired driving from a wide range of candidate variables.

METHODS

Data came from annual cross-sectional surveys of 18- to 25-year-olds participating in the Washington Young Adult Health Survey (2015-2022). Analyses were limited to two overlapping subsets of participants: those who reported past-month alcohol use for analyses predicting alcohol-impaired driving (N = 9852) and those who reported past-month cannabis use for analyses predicting cannabis-impaired driving (N = 4891). Regularized regression and random forests were used to identify the most salient predictors of each type of impaired driving from a large set of approximately 80 candidate variables. These methods were selected for their complementary strengths and their shared capacity for robust performance when handling high-dimensional data with potentially collinear predictors.

RESULTS

For likelihood of alcohol-impaired driving, top predictors included alcohol use frequency, participants' age, peak drinking quantity, age of alcohol initiation, full-time employment, and cannabis use frequency. For likelihood of cannabis-impaired driving, top predictors included cannabis use frequency, cannabis-related memory problems, simultaneous alcohol and cannabis use frequency, increased cannabis tolerance, and age of cannabis initiation.

CONCLUSIONS

Two complementary machine learning methods yielded convergent findings on the most salient predictors of impaired driving, increasing confidence in their validity. These methods provide a flexible alternative to traditional models for analyzing high-dimensional data and highlight recent use patterns, substance use disorder symptoms, and age of initiation as key priorities for prevention.