Toward a typology of driving under the influence of alcohol and drugs.
Toward a typology of driving under the influence of alcohol and drugs.
复制标题
DOI：
10.1007/s00127-022-02342-7
复制
发表时间：
2023-02
影响因子：
4.4
通讯作者：
Vaughn, Michael
中科院分区：
文献类型：
--
作者：
Goings, Trenette Clark;Salas-Wright, Christopher;Vaughn, Michael
大多数关于酒后驾驶（DUI）的研究都依赖于以变量为中心的方法，这些方法检查DUI的预测因子/相关因子。在本研究中，我们利用一个人的水平的方法-潜在的类分析（LCA）-模型的类型学的个人报告酒后驾车。这使我们能够了解个人在特定物质的影响下驾驶的程度，或者在多种物质类型中驾驶的程度。我们使用2016年至2019年期间从全国药物使用和健康调查中收集的公共使用数据。分析样本为189，472名参与者，重点是过去一年报告精神活性物质DUI的人（n = 24，619）。LCA使用自我报告的去年酒精，大麻，可卡因，海洛因，致幻剂和甲基苯丙胺的DUI作为指标变量进行。超过十分之一的美国人在过去一年中报告了DUI。报告一种物质的DUI的五分之一的人也报告了至少一种其他物质的DUI。使用LCA对报告DUI的个体之间的异质性进行建模，出现了四类：“仅酒精”（55%），“大麻和酒精”（36%），“多种药物”（5%）和“甲基苯丙胺”（3%）。“多种毒品”和“甲基苯丙胺”类别的成员的风险倾向、毒品参与、非法药物使用障碍和刑事司法系统参与率最高。戒毒治疗中心应注意将与所有非法药物的酒后驾车有关的危险和决策过程的讨论包括在内。在整个服务连续体，包括司法系统，加大对戒毒治疗的投资，可预防/减少今后酒后驾车事件。
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. 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. 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. 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.
登录
查看更多内容
影响因子：
4.4
作者：
Goings, Trenette C.;Cano, Manuel;Vaughn, Michael G.
通讯作者：
Vaughn, Michael G.
影响因子：
3.4
作者：
Jackson, Kristina M.;Sokolovsky, Alexander W.;White, Helene R.
通讯作者：
White, Helene R.
影响因子：
4.8
作者：
Norton, Edward C.;Miller, Morgen M.;Kleinman, Lawrence C.
通讯作者：
Kleinman, Lawrence C.
影响因子：
4.4
作者：
Jordan, B. Kathleen;Karg, Rhonda S.;Wiesen, Chris
通讯作者：
Wiesen, Chris
影响因子：
25.8
作者：
Grant, Bridget F.;Saha, Tulshi D.;Ruan, W. June;Goldstein, Rise B.;Chou, S. Patricia;Jung, Jeesun;Zhang, Haitao;Smith, Sharon M.;Pickering, Roger P.;Huang, Boji;Hasin, Deborah S.
通讯作者：
Hasin, Deborah S.