治疗后阿片类药物使用的预测因素中的性别差异。
Sex differences in factors predicting post-treatment opioid use.
机构信息
Suzanne Dworak-Peck School of Social Work, USC Center for Artificial Intelligence in Society, USC Center for Mindfulness Science, USC Institute for Addiction Science, University of Southern California, Los Angeles, CA, USA.
Recovery Research Institute, Center for Addiction Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
出版信息
Addiction. 2021 Aug;116(8):2116-2126. doi: 10.1111/add.15396. Epub 2021 Jan 22.
BACKGROUND AND AIMS
Several reports have documented risk factors for opioid use following treatment discharge, yet few have assessed sex differences, and no study has assessed risk using contemporary machine learning approaches. The goal of the present paper was to inform treatments for opioid use disorder (OUD) by exploring individual factors for each sex that are most strongly associated with opioid use following treatment.
DESIGN
Secondary analysis of Global Appraisal of Individual Needs (GAIN) database with follow-ups at 3, 6 and 12 months post-OUD treatment discharge, exploring demographic, psychological and behavioral variables that predict post-treatment opioid use.
SETTING
One hundred and thity-seven treatment sites across the United States.
PARTICIPANTS
Adolescents (26.9%), young adults (40.8%) and adults (32.3%) in treatment for OUD. The sample (n = 1,126) was 54.9% male, 66.1% white, 20% Hispanic, 9.8% multi-race/ethnicity, 2.8% African American and 1.3% other.
MEASUREMENT
Primary outcome was latency to opioid use over 1 year following treatment admission.
RESULTS
For women, regularized Cox regression indicated that greater withdrawal symptoms [hazard ratio (HR) = 1.31], younger age (HR = 0.88), prior substance use disorder (SUD) treatment (HR = 1.11) and treatment resistance (HR = 1.11) presented the largest hazard for post-treatment opioid use, while a random survival forest identified and ranked substance use problems [variable importance (VI) = 0.007], criminal justice involvement (VI = 0.006), younger age (VI = 0.005) and greater withdrawal symptoms (VI = 0.004) as the greatest risk factors. For men, Cox regression indicated greater conduct disorder symptoms (HR = 1.34), younger age (HR = 0.76) and multiple SUDs (HR = 1.27) were most strongly associated with post-treatment opioid use, while a random survival forests ranked younger age (VI = 0.023), greater conduct disorder symptoms (VI = 0.010), having multiple substance use disorders (VI = 0.010) and criminal justice involvement (VI = 0.006) as the greatest risk factors.
CONCLUSION
Risk factors for relapse to opioid use following opioid use disorder treatment appear to be, for women, greater substance use problems and withdrawal symptoms and, for men, younger age and histories of conduct disorder and multiple substance use disorder.
背景与目的
已有多项报告记录了治疗出院后使用阿片类药物的风险因素，但很少有研究评估性别差异，也没有研究使用现代机器学习方法评估风险。本研究的目的是通过探索与治疗后使用阿片类药物最相关的每个性别的个体因素，为阿片类药物使用障碍（OUD）的治疗提供信息。
设计
对全球个体需求评估（GAIN）数据库进行二次分析，随访时间为 OUD 治疗出院后 3、6 和 12 个月，探讨预测治疗后阿片类药物使用的人口统计学、心理和行为变量。
地点
美国 137 个治疗点。
参与者
接受 OUD 治疗的青少年（26.9%）、年轻成年人（40.8%）和成年人（32.3%）。样本（n=1126）中，54.9%为男性，66.1%为白人，20%为西班牙裔，9.8%为多种族/族裔，2.8%为非裔美国人，1.3%为其他种族。
测量
主要结局是治疗后 1 年内使用阿片类药物的潜伏期。
结果
对于女性，正则化 Cox 回归表明，更高的戒断症状（危险比[HR] = 1.31）、更年轻的年龄（HR = 0.88）、先前的物质使用障碍（SUD）治疗（HR = 1.11）和治疗抵抗（HR = 1.11）是治疗后使用阿片类药物的最大危险，而随机生存森林则确定并对物质使用问题进行了排序（变量重要性[VI] = 0.007）、刑事司法参与（VI = 0.006）、年龄较小（VI = 0.005）和戒断症状较重（VI = 0.004）是最大的危险因素。对于男性，Cox 回归表明，更多的品行障碍症状（HR = 1.34）、更年轻的年龄（HR = 0.76）和多种 SUD（HR = 1.27）与治疗后使用阿片类药物最相关，而随机生存森林则对年龄较小（VI = 0.023）、品行障碍症状更严重（VI = 0.010）、有多种物质使用障碍（VI = 0.010）和刑事司法参与（VI = 0.006）进行了排序。
结论
治疗后阿片类药物使用复发的风险因素似乎是女性更多的物质使用问题和戒断症状，而男性则是更年轻的年龄和品行障碍和多种物质使用障碍的病史。
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