Trenette Clark Goings1, Christopher Salas-Wright2, Michael Vaughn3. 1. School of Social Work, University of North Carolina at Chapel Hill, 325 Pittsboro St., CB#3550, Chapel Hill, NC, 27599, USA. ttclark@email.unc.edu. 2. School of Social Work, Boston College, 140 Commonwealth Ave, Chestnut Hill, MA, 02467, USA. 3. College for Public Health and Social Justice, St. Louis University, 3550 Lindell Blvd., Room 316, St. Louis, MO, 63103, USA.
Abstract
PURPOSE: Most research on driving under the influence (DUI) has relied upon variable-centered methods that examine predictors/correlates of DUI. In the present study, we utilize a person-level approach-latent class analysis (LCA)-to model a typology of individuals reporting DUI. This allows us to understand the degree to which individuals drive under the influence of a particular substance or do so across multiple substance types. METHODS: We use public-use data collected between 2016 and 2019 from the National Survey on Drug Use and Health. The analytic sample was 189,472 participants with a focus on those reporting DUI of psychoactive substances in the past-year (n = 24,619). LCA was conducted using self-reported DUI of past-year alcohol, cannabis, cocaine, heroin, hallucinogens, and methamphetamine as indicator variables. RESULTS: More than 1 in 10 Americans reported a DUI within the past-year. One in five people who reported DUI of one substance also reported DUI of at least one additional substance. Using LCA to model heterogeneity among individuals reporting DUI, four classes emerged: "Alcohol Only" (55%), "Cannabis and Alcohol" (36%), "Polydrug" (5%), and "Methamphetamine" (3%). Rates of risk propensity, drug involvement, illicit drug use disorders, and criminal justice system involvement were highest among members of the "Polydrug" and "Methamphetamine" classes. CONCLUSION: Drug treatment centers should take care to include discussions of the dangers and decision-making processes related to DUI of the full spectrum of illicit substances. Greater investment in drug treatment across the service continuum, including the justice system, could prevent/reduce future DUI episodes.
PURPOSE: Most research on driving under the influence (DUI) has relied upon variable-centered methods that examine predictors/correlates of DUI. In the present study, we utilize a person-level approach-latent class analysis (LCA)-to model a typology of individuals reporting DUI. This allows us to understand the degree to which individuals drive under the influence of a particular substance or do so across multiple substance types. METHODS: We use public-use data collected between 2016 and 2019 from the National Survey on Drug Use and Health. The analytic sample was 189,472 participants with a focus on those reporting DUI of psychoactive substances in the past-year (n = 24,619). LCA was conducted using self-reported DUI of past-year alcohol, cannabis, cocaine, heroin, hallucinogens, and methamphetamine as indicator variables. RESULTS: More than 1 in 10 Americans reported a DUI within the past-year. One in five people who reported DUI of one substance also reported DUI of at least one additional substance. Using LCA to model heterogeneity among individuals reporting DUI, four classes emerged: "Alcohol Only" (55%), "Cannabis and Alcohol" (36%), "Polydrug" (5%), and "Methamphetamine" (3%). Rates of risk propensity, drug involvement, illicit drug use disorders, and criminal justice system involvement were highest among members of the "Polydrug" and "Methamphetamine" classes. CONCLUSION: Drug treatment centers should take care to include discussions of the dangers and decision-making processes related to DUI of the full spectrum of illicit substances. Greater investment in drug treatment across the service continuum, including the justice system, could prevent/reduce future DUI episodes.
Authors: Sehun Oh; Michael G Vaughn; Christopher P Salas-Wright; Millan A AbiNader; Mariana Sanchez Journal: Addict Behav Date: 2020-04-10 Impact factor: 3.913
Authors: Brittany Killion; Audrey Hang Hai; Abdulaziz Alsolami; Michael G Vaughn; P Sehun Oh; Christopher P Salas-Wright Journal: Drug Alcohol Depend Date: 2021-04-20 Impact factor: 4.492
Authors: Timothy S Naimi; Ziming Xuan; Vishnudas Sarda; Scott E Hadland; Marlene C Lira; Monica H Swahn; Robert B Voas; Timothy C Heeren Journal: JAMA Intern Med Date: 2018-07-01 Impact factor: 21.873
Authors: Gillian W Smith; Michael Farrell; Brendan P Bunting; James E Houston; Mark Shevlin Journal: Drug Alcohol Depend Date: 2010-09-21 Impact factor: 4.492
Authors: Christopher P Salas-Wright; Rachel John; Michael G Vaughn; Rob Eschmann; Mariana Cohen; Millan AbiNader; Jorge Delva Journal: Addict Behav Date: 2019-06-19 Impact factor: 3.913
Authors: Christopher P Salas-Wright; Manuel Cano; James Hodges; Sehun Oh; Audrey Hang Hai; Michael G Vaughn Journal: Drug Alcohol Depend Date: 2021-09-23 Impact factor: 4.492
Authors: Christopher P Salas-Wright; Manuel Cano; Audrey Hang Hai; Sehun Oh; Michael G Vaughn Journal: Am J Prev Med Date: 2021-03-13 Impact factor: 6.604