Predicting Substance Use and Related Antisocial Behavior with Psychiatric, Socioeconomic, and Brain Measures in Women Offenders

通过精神病学、社会经济和大脑测量来预测女性罪犯的药物使用和相关反社会行为

• 批准号: 9758947
• 负责人: Bethany G. Edwards
• 金额: $ 3.55万
• 依托单位: UNIVERSITY OF NEW MEXICO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-20 至 2021-08-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9758947
• 关键词: 4-aminospiroperidol; Abstinence; Adult; Age; Alcohol consumption; Alcohol or Other Drugs use; Anterior; Anxiety; Automobile Driving; Behavior; Biological; Brain; Brain region; Classification; Clinical; Crime; Criminal Justice; Data; Data Files; Databases; Decision Making; Development; Drug usage; Economics; Education; Employment; Enrollment; Etiology; Fibrinogen; Forensic Medicine; Funding; Future; Goals; Growth; Healthcare; Image; Imprisonment; Individual; Insula of Reil; Logistic Regressions; Logistics; Machine Learning; Marital Status; Measures; Mental Health; Methods; Modeling; Moods; Odds Ratio; Outcome; Pathway interactions; Pattern; Performance; Personal Satisfaction; Personality; Pharmaceutical Preparations; Play; Predictive Factor; Prisons; Psychopathology; Recording of previous events; Regression Analysis; Research; Rest; Rewards; Risk; Risk Factors; Role; Sample Size; Sampling; Sex Differences; Societies; Socioeconomic Factors; Statistical Models; Symptoms; System; Technology; Telephone; Testing; Time; Underserved Population; United States; United States National Institutes of Health; Woman; Work; anti social; antisocial behavior; anxiety symptoms; base; cognitive control; cost; data structure; design; flexibility; follow-up; hazard; improved; interest; machine learning algorithm; men; neural circuit; neurobiological mechanism; neuroimaging; novel; offender; outcome prediction; pediatric trauma; predictive modeling; protective factors; psychopathic personality; reward processing; social; socioeconomics

Project Summary/Abstract Substance use costs society $740 billion dollars each year, placing an enormous burden on our nation’s health care and criminal justice systems. Approximately 85% of incarcerated offenders in the United States have a history of substance use and/or are imprisoned for crimes involving or motivated by alcohol and/or drug use. Incarcerated individuals tend to show poorer outcomes following substance use treatment, and forced abstinence via imprisonment is associated with risk for future substance use, which likely contributes to substance-related antisocial behavior following imprisonment. Over the past few decades, women have been sentenced to prison for drug-related reasons at alarming rates, with a growth rate exceeding that for men. Further, women offenders tend to be impacted more heavily by substance use with co-morbid psychopathology, placing greater demands on the system in terms of substance use and mental health treatments. Using the world’s largest forensic neuroimaging database on women offenders (SWANC-F), this proposal investigates substance use and related antisocial behavior following release from prison in a large sample of women offenders, with a focus on neurobiological mechanisms, to demonstrate the utility of brain measures in estimating long-term substance use outcomes in at-risk women. Substance-related antisocial behavior, defined as committing crime(s) related to substance use after release from prison, will be obtained from re-arrest data in institutional files and comprehensive background checks on all women enrolled in the study. A random sample (n = 100) will then be followed-up with via phone to gather data on substance use and obtain supplemental information to corroborate re-arrest data from files and background checks. Employing regression analyses and machine learning/pattern classifier approaches, models will be compared testing effects of psychiatric and socioeconomic variables, along with resting-state functional connectivity (rsFC) brain measures, to examine unique and combined effects in differentiating among heterogeneous etiological mechanisms driving substance use outcomes of interest in women. Specifically, this proposal seeks to test the extent to which psychiatric and socioeconomic factors confer risk for substance-related antisocial behavior following release from prison in women (Aim 1), and integrates and compares the utility of rsFC brain measures in improving these prediction models (Aim 2). Then, similar methods will be applied to test the prediction of substance use following release from prison (Aim 3). It is expected that psychiatric risk factors and socioeconomic protective factors, as well as rsFC brain measures, will be useful in predicting substance use and related antisocial behavior following incarceration, along with time elapsed between release from prison and initiation of substance use and related behavior. Testing factors that aid in predicting these behaviors in women has the potential to be far-reaching by informing the development of targeted treatments, including those that help to account for sex differences and co-morbid conditions related to substance use.

项目概要/摘要 药物使用每年给社会造成 7,400 亿美元的损失，给我们国家的健康带来巨大负担 护理和刑事司法系统。美国大约 85% 的被监禁罪犯有 有药物使用史和/或因涉及酒精和/或吸毒或因酗酒和/或吸毒而犯罪而入狱。 被监禁的人在药物使用治疗和强迫治疗后往往表现出较差的结果 通过监禁戒毒与未来使用药物的风险有关，这可能会导致 在过去的几十年里，女性在入狱后出现了与物质相关的反社会行为。 因毒品相关原因被判入狱的人数比例惊人，增长率超过了男性。 此外，女性罪犯往往受到药物滥用和共病的影响更大 精神病理学，在物质使用和心理健康方面对系统提出了更高的要求 使用世界上最大的女性罪犯法医神经影像数据库 (SWANC-F)，这 提案调查大规模出狱后的药物使用和相关反社会行为 女性罪犯样本，重点关注神经生物学机制，以证明大脑的效用 评估高危女性的长期药物使用结果的措施。 行为，定义为出狱后犯下与药物使用相关的犯罪行为，将获得 机构档案中的重新逮捕数据以及对所有注册妇女的全面背景调查 然后将通过电话对随机样本（n = 100）进行跟踪，以收集有关物质使用和使用的数据。 获取补充信息以证实档案和背景调查中的重新逮捕数据。 回归分析和机器学习/模式分类器方法、模型将进行比较测试 精神和社会经济变量以及静息态功能连接（rsFC）大脑的影响 措施，检查区分异质病因的独特和组合效应 具体来说，该提案旨在测试推动女性物质使用结果的机制。 精神和社会经济因素在多大程度上导致与物质相关的反社会行为的风险 女性出狱后（目标 1），并整合和比较 rsFC 大脑的效用 然后，将应用类似的方法来测试这些预测模型的措施（目标2）。 预测出狱后的物质使用情况（目标 3）。 和产妇保护因素以及 rsFC 大脑测量值将有助于预测药物使用 以及入狱后的相关反社会行为，以及出狱之间的时间间隔 测试有助于预测这些行为的因素。 妇女有潜力通过为有针对性的治疗的发展提供信息而产生深远的影响，包括 那些有助于解释性别差异和与药物使用相关的共病的疾病。