Drinking, Driving and Drugs: Trajectories of DWI Recidivism and How to Intervene

描述(由申请人提供)：德克萨斯大学奥斯汀分校与太平洋研究和评估研究所的顾问合作，提交了这份提案，以获得对一项探索性研究的支持，该研究旨在合并德克萨斯州的驾驶记录数据和德克萨斯州的治疗数据，以确定和评估曾接受酒精和/或药物治疗的个人中酒后驾车/酒后驾车再次犯罪的风险和保护因素。这项研究有两个目的：(1)检验曾经接受过药物滥用治疗的个体中预测DWI再犯的因素的强度；(2)基于人口统计学、药物滥用因素和驱动因素的交互作用，建立个体在治疗中预测DWI再犯风险的概况。除了研究与物质相关和与驾驶相关的因素外，我们还将调查所有德克萨斯州司机的再犯罪率，以及那些已经接受治疗的人的再犯罪率。假设有几个因素可以相互作用来预测再次犯罪的风险：人口结构、潜在的物质障碍严重程度、既往危险驾驶行为、物质治疗的强度以及法律威慑(如连锁、酒后驾车教育等)。我们建议将这些因素分开，以创建谁最有可能复发的预测性简档，并确定对具有不同特征或背景的人最有效的干预措施。我们将把超过8年的驾驶记录纵向数据(每年超过94,000例，其中包括酒后驾车逮捕和处罚、事故报告和其他驾驶传票)与治疗数据(每年超过80,000例滥用药物、使用频率、治疗事件史、严重程度和精神健康问题以及出院和后续治疗时的人口统计信息和状况，包括戒酒)合并。

DESCRIPTION (provided by applicant): The University of Texas at Austin, in collaboration with consultants from the Pacific Institute on Research and Evaluation, is submitting this proposal to obtain support for an exploratory study to merge Texas driving record data and Texas treatment data to identify and evaluate the risk and protective factors for DWI/DUI recidivism among individuals who have ever received alcohol and/or drug treatment. The study has two aims: (1) To examine the strength of factors predicting DWI recidivism among individuals who have ever received substance abuse treatment, and (2) To create profiles of individual in treatment that predict risk of DWI recidivism based on the interaction of demographics, substance abuse factors, and driving factors. In addition to studying substance-related and driving-related factors, we will investigate recidivism rates for deterrents such as DWI education classes and interlocks for all Texas drivers, as well as rates for those who have been in treatment. Several factors are hypothesized to interact to predict the risk of recidivism: demographics, underlying severity of substance disorder, previous risky driving behaviors, intensity of substance treatment, and legal deterrents (e.g. interlocks, DWI education, etc.). We propose to disentangle these factors to create a predictive profile of who is most likely to recidivate and determine the interventions that work best for people with different combinations of characteristics or backgrounds. We will merge well over 8 years of longitudinal data on driving records (over 94,000 cases per year, which contain DWI arrests and penalties, accident reports, and other driving citations issued) with treatment data (over 80,000 admissions per year) on primary, secondary, and tertiary substances of abuse, frequency of use, history of treatment episodes, and severity and mental health problems as well as demographic information and status at discharge and follow-up, including abstinence.