Alcohol, clinical & experimental research · 2026
Using machine learning to identify unique predictors of alcohol and cannabis impaired driving.
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Calhoun Brian H, Hultgren Brittney A, McCabe Connor J, Rhew Isaac C, Larimer Mary E, Kilmer Jason R, Guttmannova Katarina
Аннотация
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.
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.