Davis Jordan P, Rao Prathik, Dilkina Bistra, Prindle John, Eddie David, Christie Nina C, DiGuiseppi Graham, Saba Shaddy, Ring Colin, Dennis Michael
Suzanne Dworak-Peck School of Social Work, University of Southern California, USA.
Viterbi School of Engineering, Computer Science, University of Southern California, USA.
Drug Alcohol Depend. 2022 Apr 1;233:109359. doi: 10.1016/j.drugalcdep.2022.109359. Epub 2022 Feb 16.
The United States (US) continues to grapple with a drug overdose crisis. While opioids remain the main driver of overdose deaths, deaths involving psychostimulants such as methamphetamine are increasing with and without opioid involvement. Recent treatment admission data reflect overdose fatality trends suggesting greater psychostimulant use, both alone and in combination with opioids. Adolescents and young adults are particularly vulnerable with generational trends showing that these populations have particularly high relapse rates following treatment.
We assessed demographic, psychosocial, psychological comorbidity, and environmental factors (percent below the poverty line, percent unemployed, neighborhood homicide rate, population density) that confer risk for opioid and/or psychostimulant use following substance use disorder treatment using two complementary machine learning approaches-random forest and least absolute shrinkage and selection operator (LASSO) modelling-with latency to opioid and/or psychostimulant as the outcome variable.
Individual level predictors varied by substance use disorder severity, with age, tobacco use, criminal justice involvement, race/ethnicity, and mental health diagnoses emerging at top predictors. Environmental variabels including US region, neighborhood poverty, population, and homicide rate around patients' treatment facility emerged as either protective or risk factors for latency to opioid and/or psychostimulant use.
Environmental variables emerged as one of the top predictors of latency to use across all levels of substance use disorder severity. Results highlight the need for tailored treatments based on severity, and implicate environmental variables as important factors influencing treatment outcomes.
美国仍在努力应对药物过量危机。虽然阿片类药物仍然是过量死亡的主要驱动因素,但涉及甲基苯丙胺等精神兴奋剂的死亡人数,无论是否涉及阿片类药物,都在增加。最近的治疗入院数据反映了过量死亡趋势,表明单独使用和与阿片类药物联合使用精神兴奋剂的情况增多。青少年和年轻人尤其容易受到影响,代际趋势表明,这些人群在治疗后复发率特别高。
我们使用两种互补的机器学习方法——随机森林和最小绝对收缩和选择算子(LASSO)建模——以阿片类药物和/或精神兴奋剂使用的延迟时间作为结果变量,评估了人口统计学、心理社会、心理共病和环境因素(贫困线以下百分比、失业率、邻里凶杀率、人口密度),这些因素会导致物质使用障碍治疗后使用阿片类药物和/或精神兴奋剂的风险。
个体层面的预测因素因物质使用障碍的严重程度而异,年龄、烟草使用、刑事司法介入、种族/族裔和心理健康诊断成为主要预测因素。包括美国地区、邻里贫困、人口以及患者治疗机构周围的凶杀率在内的环境变量,成为阿片类药物和/或精神兴奋剂使用延迟的保护因素或风险因素。
环境变量成为所有物质使用障碍严重程度水平下使用延迟的主要预测因素之一。结果强调了根据严重程度进行量身定制治疗的必要性,并表明环境变量是影响治疗结果的重要因素。