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Latent Classes of Polydrug Users as a Predictor of Crash Involvement and Alcohol Consumption
Ist Teil von
Journal of studies on alcohol and drugs, 2018-05, Vol.79 (3), p.481
Ort / Verlag
Piscataway: Alcohol Research Documentation, Inc
Erscheinungsjahr
2018
Link zum Volltext
Quelle
Applied Social Sciences Index & Abstracts (ASSIA)
Beschreibungen/Notizen
Objective: Polydrug users have been shown to be at higher risk for alcohol consumption and crash involvement. However, research has shown that polydrug groups differ in some important ways. It is currently unknown how polydrug-using groups differ in terms of crash involvement and alcohol consumption. Method: The current study used latent class analysis to examine subgroups of polydrug users (n = 384) among a sample of drivers in Virginia Beach, Virginia (N = 10,512). A series of logistic regression analyses were conducted to determine the relationship between polydrug use categories and crash involvement and alcohol consumption. Results: Four distinct subclasses of users were identified among polydrug-using drivers: Class 1 is the “marijuana-amphetamines class” and accounts for 21.6% of polydrug users. Class 2 is the “benzo-antidepressant class” and accounts for 39.0% of polydrug users. Class 3 is the “opioid-benzo class” and accounts for 32.7% of polydrug users. Finally, Class 4 is the “marijuana-cocaine class” and accounts for 6.7% of the study sample. Drivers in the opioid-benzo class were significantly more likely than those in any other class as well as non–drug users and single-drug users to be involved in a crash and were more likely than those in most other conditions to consume alcohol. No significant difference was found between marijuana-amphetamine users or benzo-antidepressant users and non–drug users on crash risk. Conclusions: Some polydrug users are indeed at greater risk for crash involvement and alcohol consumption; however, not all polydrug users are significantly worse than single-drug users and/or non–drug users, and the practice of lumping polydrug users together when predicting crash risk runs the risk of inaccurately attributing crash involvement to certain drivers.