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

描述（由申请人提供）：得克萨斯大学奥斯汀分校与太平洋研究与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，该研究将得克萨斯州的驾驶记录数据和得克萨斯州的治疗数据合并，以确定和评估曾经接受过酒精和/或药物治疗的个人中DWI/DUI累犯的风险和保护因素。这项研究有两个目的：（1）检查曾接受过药物滥用治疗的个人中预测DWI再犯的因素的强度，以及（2）创建基于人口统计学、药物滥用因素和驱动因素的相互作用预测DWI再犯风险的治疗中的个人概况。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DWI教育课程和联锁等威慑因素的累犯率，以及接受治疗的人的累犯率。假设有几个因素相互作用，以预测累犯的风险：人口统计学，物质障碍的潜在严重程度，以前的危险驾驶行为，物质治疗的强度，和法律的威慑（如联锁，DWI教育等）。我们建议解开这些因素，以创建一个预测谁是最有可能复发的人，并确定最适合具有不同特征或背景组合的人的干预措施。我们将合并超过8年的驾驶记录纵向数据（每年超过94，000起案件，其中包含DWI逮捕和处罚，事故报告和其他驾驶传票），并提供治疗数据（每年超过80，000例入院）关于滥用的一级、二级和三级物质、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随访时的人口统计学信息和状态，包括禁欲。

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.