Homewood Research Institute, Guelph, ON, Canada.
Homewood Research Institute, Guelph, ON, Canada.
Drug Alcohol Depend. 2021 Oct 1;227:108943. doi: 10.1016/j.drugalcdep.2021.108943. Epub 2021 Jul 28.
Continuing care following inpatient addiction treatment is an important component in the continuum of clinical services. Mutual help, including 12-step groups like Alcoholics Anonymous, is often recommended as a form of continuing care. However, the effectiveness of 12-step groups is difficult to establish using observational studies due to the risks of selection bias (or confounding).
To address this limitation, we used both conventional and machine learning-based propensity score (PS) methods to examine the effectiveness of 12-step group involvement following inpatient treatment on substance use over a 12-month period.
Using data from the Recovery Journey Project - a longitudinal, observational study - we followed an inpatient sample over 12-months post-treatment to assess the effect of 12-step involvement on substance use at 12-months (n = 254). Specifically, PS models were constructed based on 34 unbalanced confounders and four PS-based methods were applied: matching, inverse probability weighting (IPW), doubly robust (DR) with matching, and DR with IPW.
Each PS-based method minimized the potential of confounding from unbalanced variables and demonstrated a significant effect (p < 0.001) between high 12-step involvement (i.e., defined as having a home group; having a sponsor; attending at least one meeting per week; and, being involved in service work) and a reduced likelihood of using substances over the 12-month period (odds ratios 0.11 to 0.32).
PS-based methods effectively reduced potential confounding influences and provided robust evidence of a significant effect. Nonetheless, results should be considered in light of the relatively high attrition rate, potentially limiting their generalizability.
住院成瘾治疗后的延续性护理是临床服务连续体中的一个重要组成部分。互助，包括像匿名戒酒会这样的 12 步团体，通常被推荐为一种延续性护理形式。然而，由于选择偏差（或混杂）的风险，使用观察性研究很难确定 12 步团体的效果。
为了解决这一限制，我们使用了传统和基于机器学习的倾向评分（PS）方法，在 12 个月的时间内检查住院治疗后参与 12 步团体对物质使用的影响。
使用康复之旅项目的数据——一项纵向观察性研究——我们在治疗后 12 个月内对住院样本进行了随访，以评估 12 步参与对 12 个月时物质使用的影响（n=254）。具体来说，基于 34 个不平衡的混杂因素构建了 PS 模型，并应用了四种 PS 方法：匹配、逆概率加权（IPW）、匹配的双重稳健（DR）和 IPW 的双重稳健（DR）。
每种基于 PS 的方法都最大限度地减少了不平衡变量引起的混杂影响，并显示出高 12 步参与（即定义为有家庭小组；有赞助商；每周至少参加一次会议；并参与服务工作）与减少物质使用的可能性之间存在显著差异（12 个月期间的优势比为 0.11 至 0.32）。
PS 方法有效地减少了潜在的混杂影响，并提供了显著效果的有力证据。尽管如此，应考虑到较高的流失率，结果可能限制了其普遍性。
