Predicting attrition in long-term residential substance use disorder treatment: A modifiable risk factors perspective.

Abstract

Although numerous factors are associated with attrition in substance use disorder (SUD) treatment, many are unmodifiable and therefore difficult to target in efforts to improve treatment outcomes. The current study sought to identify the strongest and most modifiable predictors of attrition in long-term residential SUD treatment from myriad characteristics associated with treatment termination. Archival data were examined for 2,069 adults (74% male; 38% non-Hispanic White) who entered a long-term residential SUD treatment facility between January 2010 and June 2016. Program staff recorded clients’ demographic, situational, substance use, and intake data at admission; discharge data were recorded at termination. To increase the likelihood our results were clinically meaningful, we randomly split our sample, ran 2 5-step hierarchical logistic regressions, and cross-validated our results. Across samples, we found younger age, having less than a high school education (Step 1), unstable living arrangements (Step 2), greater prior month use of primary substances, less prior month use of alcohol, and prior year needle use preceding treatment (Step 4), and longer recommended length of stay in treatment (Step 5) predicted attrition. To improve long-term residential SUD treatment completion, we propose treatment adaptations begin with the most modifiable predictors of attrition. Accordingly, the current data indicate initial focus should be placed on refurbishing the process through which recommended treatment durations are approached by providers. Subsequent focus should be placed on modifiable factors that present greater systemic challenges, followed by those that are unmodifiable but can be indirectly targeted by interventions tailored to specific underrepresented groups.

Impact of Cluster B Personality Disorders in Drugs Therapeutic Community Treatment Outcomes: A Study Based on Real World Data

“…Moreover, more than half of the patients did not achieve their therapeutic objectives. This number is slightly higher than that observed by Baker et al [31] and is in line with other results found in observational studies [3,14]. These dropout rates highlight the therapeutic complexity of TC treatment for patients with SUD.…”

“…Consequently, it has been possible to provide more precise statistical estimates [19]. Moreover, unlike another study that used EHR data [31], we analyzed the impact of mental disorders diagnosed according to ICD-10 criteria, one of the most widely used nosological classifications in the clinical context.…”

“…In the context of addictions, some authors have identified a high use of EHRs [25] in treatment centers (TCs), highlighting the potential utility of these records in research [26]. However, published studies with EHRs are relatively scarce [27][28][29][30] and even less frequent in TCs [31]. In this group of patients with high dropout rates, RWD analysis could help understand the variables associated with patient therapeutic dropout in general and the impact of comorbid mental disorders in particular.…”

Correlates related to follow‐up in a community engagement program in North Central Florida

“…While demographic factors such as race (Black; White et al, 2016) and low education (White et al, 2016), may partly explain the differential study follow‐up completion rates among drug users and non‐users, there are other reasons as well. For example, among adults who use illicit drugs, the transitory nature of the population, having unstable accommodation, and having no access to phones are potential reasons for drop‐out (Baker et al, 2019). Further, the transtheoretical model of change (TTM), based on the process of individuals′ decision making to effect change, has been used as the framework for behavioral modification to explain why some individuals are successful and others are not in treating various psychological or health problems.…”

Prediction of Cocaine Inpatient Treatment Success Using Machine Learning on High-Dimensional Heterogeneous Data

“…Some patients drop out in advanced stages of treatment and represent a great loss due to the health resources consumed. According to the evidence, after 37.37 days of treatment there is a reliable change in psychological recovery and well-being [64] and, in the case of late dropouts, shorter residential treatment with longer aftercare may be beneficial [65], both in terms of cost and clinical outcomes [66]. On the other hand, late dropouts could also be subjected to a pre-treatment phase to improve the stage of change, which has been shown to be a good predictor of treatment completion [67].…”