College of Business and Economics, California State University Fullerton, Fullerton, California, USA.
International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China.
Health Serv Res. 2022 Apr;57(2):411-421. doi: 10.1111/1475-6773.13896. Epub 2021 Oct 24.
To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race.
Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes.
This retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation.
Client data were collected by states during personal interviews at admission and discharge.
Random forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication-assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities.
The methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment.
利用一种新的统计方法将交叉性框架具体化，并通过这些努力，提高对阿片类药物使用障碍（OUD）治疗机会（即治疗开始前的等待时间）差距的估计，超越种族。
美国 2015-2017 年治疗阶段数据调查（TEDS-A）收集的 941286 个治疗阶段样本，以及加利福尼亚州（n=188637）和马里兰州（n=184276）的一个子集，这两个州的样本量最大。
这项回顾性亚组分析采用了一种称为虚拟双胞胎的两步方法。在步骤 1 中，我们训练了一个分类模型，该模型给出了等待（1 天或更长时间）的概率。在步骤 2 中，我们确定了由于种族而存在更高差异概率的亚组。我们为步骤 1 测试了三种分类模型，并确定了估计效果最佳的模型。
各州在入院和出院时通过个人访谈收集客户数据。
随机森林是亚组分析第一步中最准确的模型。我们发现各州之间的种族差异存在很大差异。对两个样本量最大的州进行分层分析表明，除种族外，还有一些关键因素加剧了差异。在加利福尼亚州，服务设置、转介来源和无家可归等因素定义了最容易受到种族差异影响的亚组。在马里兰州，服务设置、既往阶段、接受药物辅助阿片类药物治疗以及主要药物使用频率等因素加剧了种族差异。确定的亚组存在显著更大的种族差异。
本研究中使用的方法使我们能够更细致地了解差异研究的复杂性。我们发现了与种族相交并加剧等待时间差异的州和服务因素。研究结果可以帮助决策者针对可能使亚组更容易等待更长时间进入治疗的可修改因素。
