Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape
David Eddie , John Prindle , Paul Somodi , Isaac Gerstmann , Bistra Dilkina , Shaddy K. Saba , Graham DiGuiseppi , Michael Dennis , Jordan P. Davis
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引用次数: 0
Abstract
Background
Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement.
Methods
This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment.
Results
Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained.
Conclusions
Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes.
背景:更好地了解影响治疗参与度的因素有助于治疗机构和系统更好地吸引患者参与治疗。本研究利用机器学习来探索个人和社区层面的因素与 SUD 治疗参与度之间的关联:本研究利用随机森林机器学习和广义线性混合模型对全球个人需求评估(GAIN)数据集和美国人口普查局数据进行了二次分析。我们的样本(N = 15,873)包括 2006 年至 2012 年期间在 GAIN 站点接受 SUD 治疗的所有人。预测因素包括一系列人口统计学、社会心理学、治疗特异性和临床测量指标,以及患者接受治疗的社区环境水平测量指标:青少年年龄和精神疾病合并症以及低失业率和高人口密度在社区层面上预测了参与治疗的几率。黑人/非洲裔美国人的种族以及在邻里层面上的高公共援助率和高收入不平等则预示着参与治疗的几率较低。无论参与治疗的程度如何,在失业率高、酒类销售点集中和贫困地区接受治疗的个人,在基线时的药物使用和相关问题都更多。虽然随着治疗的进行和时间的推移,这些差异有所缩小,但差距依然存在:结论:邻里层面的因素似乎在参与 SUD 治疗中起着重要作用。无论个人是否参与治疗,治疗环境中失业、酒类销售点密度和贫困等健康社会决定因素的负荷越大,SDD 治疗结果越差。