Journal of substance use and addiction treatment最新文献

Introduction: The global rise of concurrent use of opioids and methamphetamine presents challenges to stable methadone maintenance treatment (MMT). We assessed non-adherence to treatment and concurrent opioid use, along with associated factors, among people on MMT using methamphetamine in Vietnam.

Methods: This analysis utilized baseline data from a randomized clinical trial designed to investigate the effectiveness of various combinations of behavioral evidence-based interventions for methamphetamine use among patients on MMT in Vietnam (STAR-OM R01DA050486). We used data from 498 participants on MMT who self-reported using methamphetamine in the past 30 days and had been in MMT treatment for at least three months. The study determined adherence to MMT through medical record review and assessed concurrent opioid use via urine testing and documented test results in medical records.

Results: Within the last three months, 145 (29.1 %) did not adhere to MMT, and 212 (42.6 %) concurrently used opioid. Non-adherence to MMT were strongly associated with living at a considerable distance from the clinic (aOR = 1.80, 95 % CI: 1.10-2.90), being HIV positive (aOR = 0.51, 95 % CI: 0.27-0.97), and were marginally associated with using methamphetamine to reduce emotional distress (aOR = 1.56, 95 % CI: 0.99-2.45). Concurrent opioid use was associated with receiving treatment at clinics established during the scale-up period (aOR = 1.80, 95 % CI: 1.13-2.87), an extended duration of treatment at current clinics (aOR = 0.98, 95 % CI: 0.97-0.99), male gender (aOR = 2.09, 95 % CI: 1.05-4.13), and ever injecting methamphetamine (aOR = 1.98, 95 % CI: 1.13-3.46), besides MMT non-adherence (aOR = 2.71, 95 % CI: 1.72-4.29).

Conclusion: Non-adherence to MMT and concurrent opioid use are prevalent among methamphetamine-using patients on methadone in Vietnam. Alongside well-documented factors influencing methadone program, the rise in methamphetamine adds further complexity to these challenges. Future intervention strategies must address both the underlying issues and methamphetamine use behaviors to effectively improve treatment outcomes.

Trial registration: NCT04706624. Registered 13 January 2021. https://clinicaltrials.gov/ct2/show/NCT04706624.

Introduction: The Eastern European and Central Asian region has the most rapidly growing HIV epidemic worldwide, concentrated among people who inject drugs (PWID). Scaling up opioid agonist therapies (OAT) is a highly effective primary and secondary HIV prevention strategy, yet coverage remains low (2.7 %) among the 18,000 PWID injecting opioids in Tajikistan.

Methods: As part of a pre-implementation activity before using the blended NIATx implementation strategy, we focused on the first NIATx principle, to understand and involve the customer by exploring the barriers and facilitators to OAT scale-up (i.e. greater methadone program entry and retention resulting in more people on methadone). From October to December 2023, recipients (i.e., PWID) were assessed across all 14 OAT sites in 12 cities in 28 focus groups, stratified by those on (N = 120) and not on (N = 108) methadone. Nominal group technique (NGT) was selected as a rapid, inclusive and mixed methods strategy to identify and prioritize the most important barriers and facilitators. Barriers and solutions were categorized to guide implementation targeting individual (i.e., patients/providers), organizational (i.e., clinic) and policy (e.g., laws/regulations) factors.

Results: The three highest-ranking barriers nationally to scaling up methadone were similar for both groups: 1) organizational factors like logistical inconvenience and demands on patients (transport, schedule, daily dosing); 2) policy factors like requirements to be listed in the national OAT registry which can restrict access to a driver's license and employment; and 3) individual and societal factors like widespread disinformation about methadone. The three highest-ranking solutions included: 1) changing policies to allow take-home dosing (both groups); 2) expanding the number of sites where methadone could be dispensed (e.g., primary healthcare clinics and pharmacies); and 3) widely disseminating reliable information about methadone to PWID and other stakeholders like governmental organizations and police. For those not on methadone, site expansion was the second highest solution, while for those not on methadone, disseminating accurate information was second highest.

Conclusion: This pre-implementation study provides important insights into implementation strategies that might be considered to scale-up methadone that targets recipients (patients, providers, and family), organizations and policies.

Background: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks of adverse outcomes, including death. Predicting important outcomes, including non-prescribed opioid use (NPOU) and treatment discontinuation among persons receiving medications for OUD (MOUD) can provide a proactive approach to these challenges. Our study uses ecological momentary assessment (EMA) and deep learning to predict momentary NPOU, medication nonadherence, and treatment retention in MOUD patients.

Methods: Study participants included adults receiving MOUD at a large outpatient treatment program. We predicted NPOU (EMA-based), medication nonadherence (Electronic Health Record [EHR]- and EMA-based), and treatment retention (EHR-based) using context-sensitive EMAs (e.g., stress, pain, social setting). We used recurrent deep learning models with 7-day sliding windows to predict the next-day outcomes, using Area Under the ROC Curve (AUC) for assessment. We employed SHapley additive ExPlanations (SHAP) to understand feature latency and importance.

Results: Participants comprised 62 adults with 14,322 observations. Model performance varied across EMA subtypes and outcomes with AUCs spanning 0.58-0.97. Recent substance use was the best performing predictor for EMA-based NPOU (AUC = 0.97). Life-contextual factors performed best for EMA-based medication nonadherence (AUC = 0.68) and retention (AUC = 0.89), and substance use risk factors (e.g., nicotine and alcohol use) performed best for predicting EHR-based medication nonadherence (AUC = 0.79). SHAP revealed varying latencies between predictors and outcomes.

Conclusions: Findings support the effectiveness of EMA and deep learning for forecasting actionable outcomes in persons receiving MOUD. These insights will enable the development of personalized dynamic risk profiles and just-in-time adaptive interventions (JITAIs) to mitigate high-risk OUD outcomes.