Evaluating racial disparity in referral source and successful completion of substance abuse treatment

Sahker, Ethan; Toussaint, Maisha; Ramirez, Marizen; Ali, Saba Rasheed; Arndt, Stephan

doi:10.1016/j.addbeh.2015.04.006

Cited by 57 publications

(32 citation statements)

References 25 publications

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“…Furthermore, we removed all detoxification treatment episodes from the analysis as they are shorter and serve the purpose of stabilizing, not treating patients for substance use disorders. This approach is consistent with other studies that have removed detoxification episodes when evaluating factors associated with treatment completion in the TEDS (Saloner et al, 2014; Sahker et al, 2015). For the sake of completeness, sensitivity analyses were conducted including detoxification episodes and this had no effect on results of the analyses (results not shown).…”

Section: Methodssupporting

confidence: 87%

The association of psychiatric comorbidity with treatment completion among clients admitted to substance use treatment programs in a U.S. national sample

Krawczyk

Feder

Saloner

et al. 2017

Drug and Alcohol Dependence

104

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Background Psychiatric disorders are highly comorbid with substance use disorders and play an important role in their course and recovery. However, the impact of comorbidity on treatment outcomes has not been examined in a U.S. national sample. This study explores the impact of psychiatric comorbidity on treatment completion among individuals admitted to publicly funded substance use treatment facilities across the United States. Methods Using data on first-time treatment episodes in the U.S. from the Treatment Episode Dataset-Discharges (TEDS-D) for the years 2009–2011, logistic regression was used to assess the association between psychiatric comorbidity and treatment non-completion, and Cox proportional hazards regression was used to assess the association between comorbidity and rate of attrition. Analyses were performed for all substances together and then stratified by primary substance of abuse (alcohol, cannabis, stimulants, or opioids). Results Of 856,385 client treatment episodes included in our analysis, 28% had a psychiatric comorbidity and 38% did not complete treatment. After adjusting for socio-demographic and treatment characteristics, clients with psychiatric comorbidity had higher odds of not completing treatment relative to those without comorbidity [OR = 1.28 (1.27–1.29)], and had an earlier time to attrition [HR = 1.14 (1.13–1.15)]. Psychiatric comorbidity was most strongly associated with treatment non-completion and rate of attrition in those admitted primarily for alcohol [OR = 1.37 (1.34–1.39); HR = 1.19 (1.17–1.21), respectively]. Conclusions Individuals with psychiatric comorbidities receiving treatment for substance use disorders face unique challenges that impact their ability to complete treatment. The findings call for further efforts to integrate treatment for psychiatric comorbidities in substance use treatment settings.

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Section: Methodssupporting

confidence: 87%

The association of psychiatric comorbidity with treatment completion among clients admitted to substance use treatment programs in a U.S. national sample

Krawczyk

Feder

Saloner

et al. 2017

Drug and Alcohol Dependence

104

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“…Consequently, only records that indicate the individual had no prior SUD treatment were included in the analyses. Part of our previous work focuses on racial and ethnic minorities [11, 12, 23, 24]. It is known that racial and ethnic minorities vary in their treatment access and success levels [25–27].…”

Section: Methodsmentioning

confidence: 99%

Use of a machine learning framework to predict substance use disorder treatment success

et al. 2017

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There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.

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“…Unlike other comprehensive administrative datasets (Sahker et al, 2015;Alterman et al, 2001;Stahler et al, 2016), NDTMS has patient-level identifiers that enable cross-referencing with subsequent treatment admissions. This utility provides not only an objective summative measure of the sustainability of recovery in this population, it also enables national administrative systems to objectively capture whether patients had previously accessed treatment services, which is an important negative predictor of treatment outcome (Siguel and Spillane, 1978;Marsden et al, 2012).…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Effectiveness of treatment for opioid use disorder: A national, five-year, prospective, observational study in England

Eastwood

Strang²,

Marsden

2017

Drug and Alcohol Dependence

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Evaluating racial disparity in referral source and successful completion of substance abuse treatment

Cited by 57 publications

References 25 publications

The association of psychiatric comorbidity with treatment completion among clients admitted to substance use treatment programs in a U.S. national sample

The association of psychiatric comorbidity with treatment completion among clients admitted to substance use treatment programs in a U.S. national sample

Use of a machine learning framework to predict substance use disorder treatment success

Effectiveness of treatment for opioid use disorder: A national, five-year, prospective, observational study in England

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