The Rutgers Integrated Care Evaluation (RICE) Research Framework: An Innovative and Rigorous Set of Methods to Evaluate Integrated Care Programs.

Introduction: Integrated care programs that prioritize comprehensive service delivery for behavioural health and medical conditions have the potential to improve patient outcomes. Few programs, however, use data-driven methods to guide program evaluation and implementation, limiting their effectiveness, as well as the scope of findings in the research literature. Purpose: To address these gaps, we describe an innovative and rigorous evaluative research framework: the Rutgers Integrated Care Evaluation (RICE) Research Framework, designed to be tailorable across conditions and care settings. Method: The RICE Research Framework is guided by two core concepts: (1) an approach built on engaging as equal partners and (2) data source triangulation. For the former, the approach relies on multiple teams (Project, Clinical Site, Evaluation, and Consumer) working in collaboration. While teams have specific roles, all teams engage frequently as equal partners to facilitate performance and advance research deliverables. For the latter, we provide a template with recommended primary and secondary data sources with areas of focus, applicable methods, and samples. These sources, when used in combination, can guide implementation, advance replicability, develop/refine health care programs, and foster dissemination of scientific findings. Conclusions: We recommend clinicians and scientists implement the RICE Research Framework to enhance their integrated care programs.

Methadone treatment utilization and overdose trends among Medicaid beneficiaries in New Jersey before and during the COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic disrupted the traditional mode of methadone maintenance treatment (MMT) delivery through the imposition of lockdowns and social distancing measures. In response, policy makers granted flexibilities to providers delivering MMT to change their practices to maintain patient participation while accommodating the measures imposed to prevent the spread of COVID-19. This study examines the utilization of MMT and overdoses of patients receiving MMT during the COVID-19 pandemic in one mid-Atlantic state. MATERIALS AND METHODS: We analyzed Medicaid claims data for 2018-2020, calculating weekly trends for starts, discontinuations, and medically-treated overdoses for beneficiaries receiving MMT who had been continuously enrolled in Medicaid for the previous 12 months, to account for changes in the composition of the Medicaid population following the COVID-19 public health emergency (PHE). We completed data analyses from January to June 2022. RESULTS: We observed countervailing trends in new starts, which experienced an immediate, non-significant dip of -22.47 per 100,000 Medicaid beneficiaries (95%CI, -50.99 to 6.04) at the outset of the pandemic followed by an increasing upward trend of 1.41 per 100,000 beneficiaries per week (95%CI, 0.37 to 2.46), and in discontinuations, which also experienced an immediate dip of -3.23 per 1000 MMT enrollees (95%CI, -4.49 to -1.97) followed by an increasing upward trend of 0.14 per 1000 MMT enrollees per week (95%CI, 0.09 to 0.19). The net result of these shifts was a stable, slowly increasing rate of MMT treatment of 0.02 % per week before and after the PHE. We also found no statistically significant association of the PHE with medically-treated overdoses among beneficiaries enrolled in MMT (trend change = 0.02 overdoses per 10,000 MMT enrollees, 95%CI, -0.05 to 0.09). CONCLUSIONS: New Jersey achieved overall stability in MMT treatment prevalence following the pandemic's onset, while some changes in treatment dynamics took place. This outcome may reflect that the extensive flexibilities granted to providers of MMT by the state and federal government successfully maintained access to MMT for Medicaid beneficiaries through the pandemic without increasing risk of medically-treated overdose. These findings should inform policy makers developing the post-COVID-19 legal and regulatory landscape.

Artificial Neural Network Analysis Examining Substance Use Problems Co-Occurring with Anxiety and Depressive Disorders Among Adults Receiving Mental Health Treatment.

Objective: The co-occurrence of anxiety disorders, depressive disorders, and substance use problems was examined. Methods: The Mental Health Client-Level Data dataset was used to conduct logistic regression models and an artificial neural network analysis. Logistic regression analyses were conducted among adults with anxiety (n = 547,473) or depressive disorders (n = 1,610,601) as their primary diagnosis who received treatment in a community mental health center. The artificial neural network analysis was conducted with the entire sample (N = 2,158,074). Results: Approximately 30% of the sample had co-occurring high-risk substance use or substance use disorder. Characteristics including region of treatment receipt, age, education, gender, race and ethnicity, and the presence of co-occurring anxiety and depressive disorders were associated with the co-occurring high-risk substance use or a substance use disorder. Conclusions: Findings from this study highlight the importance of mental health facilities to screen for and provide integrated treatment for co-occurring disorders.

Mediational pathways among drug use initiation, use-related consequences, and quit attempts.

Background: Factors that predict attempts to discontinue drug use are clinically relevant and may inform treatment. This study investigated drug use-related consequences as a predictor of drug quit attempts and treatment seeking among two cohorts of persons who use drugs. Methods: Drug use and clinical characteristics were assessed among persons who use cocaine (N=176; urine-verified; 'Cocaine Cohort') and among those who use heroin (N=166; urine-verified; 'Heroin Cohort'). Mediation analyses assessed relationships among age at initial drug use, adverse drug-specific use-related consequences, and drug-specific quit attempts, separately for each cohort. Forward conditional logistic regression models evaluated drug use and clinical symptom scores as predictors of drug-specific treatment seeking. Results: Controlling for age, mediation models showed that drug use consequences fully mediated the relationship between age at initial drug use and number of drug-specific quit attempts for the 'Cocaine Cohort' and 'Heroin Cohort' (R2=0.30, p<.001; R2=0.17, p<.001; respectively). Reporting more consequences predicted more quit attempts in each cohort, accounting for duration of use (ps<.001). Reporting more consequences also predicted greater likelihood of seeking drug use treatment (ps<.001) and was associated with more severe clinical symptoms in each cohort (ps<.05). Conclusions: Using a parallel analysis design, we showed that reporting more drug-specific use-related consequences predicted more drug-specific quit attempts and greater likelihood to seek treatment in two cohorts: persons who use cocaine and those who use heroin. Our findings suggest that experiencing more drug use consequences predicts more attempts to seek drug abstinence and that assessment of consequences may be informative for treatment.

Decreases in Employer Referrals to First-Time Substance Use Treatment for Adults From 2004 to 2020.

OBJECTIVE: The aim of the study is to examine trends of employer/employee assistance program referred admissions to outpatient substance use disorder (SUD) treatment in the United States. METHODS: The publicly available Treatment Episode Data Set was used. Full-time employed adults with no history of SUD treatment referred to outpatient treatment by an employer/employee assistance program from 2004 to 2020 were included ( N = 36,142). Joinpoint regression examined admission trends. RESULTS: Employer/employee assistance program referred admissions to outpatient treatment decreased annually by 6.4% from 2004 to 2020 ( P < 0.001). Joinpoint analyses identified 2 linear segments from 2004 to 2008 (increased but not significant) and from 2008 to 2020. From 2008 to 2020, an average annual percent decrease of 8.7% ( P < 0.001) was identified. CONCLUSIONS: Findings from this repeated cross-sectional study suggest a missed opportunity for workplaces to serve as a potential SUD treatment access point.

Experiences of healthcare and substance use treatment provider-based stigma among patients receiving methadone.

Background: Medications for Opioid Use Disorder (MOUD) are efficacious, however only one-third of individuals with an opioid use disorder (OUD) enter into treatment. Low rates of MOUD utilization are partially due to stigma. This study examines provider-based stigma toward MOUD and identifies factors associated with experiencing stigma related to MOUD from substance use treatment and healthcare providers among people receiving methadone. Methods: Clients receiving MOUD at an opioid treatment program (N = 247) were recruited to complete a cross-sectional computer-based survey assessing socio-demographics, substance use, depression and anxiety symptoms, self-stigma, and recovery supports/barriers. Logistic regression was used to examine factors associated with hearing negative comments about MOUD from substance use treatment and healthcare providers. Results: 27.9% and 56.7% of respondents reported they sometimes/often hear negative comments about MOUD from substance use treatment and healthcare providers, respectively. Logistic regression results indicate that individuals who experience more negative consequences resulting from their OUD (OR=1.09, p=.019) had greater odds of hearing negative comments from substance use treatment providers. Age (OR=0.966, p=.017) and treatment stigma (OR=1.42, p=.030) were associated with greater odds of hearing negative comments from healthcare providers. Conclusions: Stigma can be a deterrent to seeking substance use treatment, healthcare, and recovery support. Understanding factors associated with experiencing stigma from substance use treatment providers and healthcare providers is important as these individuals may act as advocates for those with OUD. This study highlights individual factors associated with hearing negative comments about methadone and other MOUD and point to areas for targeted education.

Differences in intent to refer buprenorphine among community correctional and treatment staff: A set of cross-lagged models predicting efficacy beliefs and familiarity with buprenorphine for opioid use disorder.

BACKGROUND AND OBJECTIVES: Despite high rates of individuals with opioid use disorder, community correctional agencies underutilize medications for opioid use disorder (MOUD). Knowledge about the mechanisms which motivate correctional employees to refer buprenorphine remains underdeveloped, and differences in these patterns by employee status are unknown. This study has two objectives: (1) investigate the presence of a reciprocal relationship between familiarity with buprenorphine and efficacy beliefs among community corrections and community treatment staff and (2) identify whether this relationship differs by staff status in referral intentions. METHODS: Data were used from the Criminal Justice Drug Abuse Treatment Studies 2 (CJ-DATS 2) among correctional and treatment employees (N = 873). Four models investigated whether a reciprocal relationship existed between buprenorphine familiarity and efficacy beliefs. Then, the best fitting model was used to test the influence that prior training had on future referral intention through familiarity and efficacy beliefs among the analytic sample (n = 612), by comparing two separate structural equation models (SEMs) among correctional staff and treatment staff, respectively. RESULTS: The fully cross-lagged model provided a significantly better fit to the data than other models ( χ diff 2 ${\chi }_{\mathrm{diff}}^{2}$ (1) = 7.189, p < .01). The results of the multigroup SEM show that training had positive, indirect effects on future referral intentions that significantly differed between treatment and community correction staff. DISCUSSION AND CONCLUSIONS: Findings show that training may influence correctional staff intent to refer individuals to receive buprenorphine through familiarity. SCIENTIFIC SIGNIFICANCE: Tailored training for MOUD treatment for specific staff populations may prove more beneficial than existing approaches.

Exploring potential moderators of depressive symptoms and treatment outcomes among patients with opioid use disorder.

BACKGROUND: Depressive symptoms are common in patients seeking medication treatment for opioid use disorder (MOUD treatment) and decrease quality of life but have been inconsistently related to opioid treatment outcomes. Here, we explore whether depressive symptoms may only be related to adverse treatment outcomes among individuals reporting high opioid use-related coping motives (i.e., use of opioids to change affective states) and high trait impulsivity, two common treatment targets. METHODS: Patients seeking MOUD treatment (N = 118) completed several questionnaires within two weeks of their treatment intake. Treatment outcomes (opioid-positive urine screens and days retained in treatment) were extracted from treatment records. Moderation analyses controlling for demographic characteristics and main effects were conducted to explore interaction effects between depressive symptoms and two distinct moderators. RESULTS: Depressive symptoms were only related to opioid use during early treatment among patients reporting high opioid use-related coping motives (B = 2.67, p =.004) and patients reporting high trait impulsivity (B = 2.01, p =.039). Further, depressive symptoms were only inversely related to days retained among individuals with high opioid use-related coping motives (B = -10.12, p =.003). CONCLUSIONS: Individuals presenting to treatment with opioid-related coping motives and/or impulsivity in the context of depressive symptoms may confer unique risk for adverse treatment outcomes. Clinicians may wish to consider these additive risk factors when developing their treatment plan.

Advancing the behavioral health workforce: An innovative integrated care and substance use disorder training model to improve outcomes for people in medically underserved communities.

INTRODUCTION: It is critical that we strengthen the ability of the behavioral health workforce to better manage the complex behavioral and physical health needs of people in medically underserved areas. Despite the knowledge that integrated care (IC) models improve patient outcomes and experience, provider satisfaction, and health care costs, educational and experiential training in IC is limited, limiting workforce capacity to deliver this care. METHOD: Through the Health Resources and Services Administration-funded Rutgers University Integrated Substance Use Disorder Training Program (RUISTP), we partner with community-based primary care clinics to implement an interprofessional fellowship program for psychologists, social workers, physician assistants, and advanced practice nurses. The RUISTP simultaneously provides training and implements IC within these community-based systems. Our multiple-methods evaluation design examines data-driven indicators of feasibility, uptake, and program success during implementation and sustainability phases and assesses changes in organizational beliefs and practices, provider competencies, and service utilization throughout the project period. RESULTS: This article describes the significance and innovation of (a) an IC training program, (b) an implementation plan for sustained change within systems of care, and (c) evaluative methodology to assess and improve IC and SUD service delivery and training. These data will be used to create a template for other academic and health care systems nationally. DISCUSSION: It is the mission of this program to use an innovative training, implementation, and evaluation design to enhance IC and SUD services, bolster the behavioral health workforce trained to provide high-quality IC, and inform replications of this model in other geographic and clinical settings, particularly those in medically underserved communities. (PsycInfo Database Record (c) 2022 APA, all rights reserved).