Collaborative care in the treatment of opioid use disorder and mental health conditions in primary care: A clinical study protocol

Adapting psychotherapy in collaborative care for treating opioid use disorder and co-occurring psychiatric conditions in primary care

INTRODUCTION

Opioid use disorder (OUD) and psychiatric conditions commonly co-occur yet are infrequently treated with evidence-based therapeutic approaches, resulting in poor outcomes. These conditions, separately, present challenges to treatment initiation, retention, and success. These challenges are compounded when individuals have OUD and psychiatric conditions.

METHOD

Recognizing the complex needs of these individuals, gaps in care, and the potential for primary care to bridge these gaps, we developed a psychotherapy program that integrates brief, evidence-based psychotherapies for substance use, depression, and anxiety, building on traditional elements of the Collaborative Care Model (CoCM). In this article, we describe this psychotherapy program in a primary care setting as part of a compendium of collaborative services.

RESULTS

Patients receive up to 12 sessions of evidence-based psychotherapy and case management based on a structured treatment manual that guides treatment via Motivational Enhancement; Cognitive Behavioral Therapies for depression, anxiety, and/or substance use disorder; and/or Behavioral Activation components.

DISCUSSION

Novel, integrated treatments are needed to advance service delivery for individuals with OUD and psychiatric conditions and these programs must be rigorously evaluated. We describe our team's efforts to test our psychotherapy program in a large primary care network as part of an ongoing three-arm randomized controlled trial. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Opioid overdose prevention education in Texas during the COVID-19 pandemic

BACKGROUND

Distribution of naloxone and training on its proper use are evidence-based strategies for preventing opioid overdose deaths. In-person naloxone training was conducted in major metropolitan areas and urban centers across Texas as part of a state-wide targeted opioid response program. The training program transitioned to a live, virtual format during the COVID-19 public health emergency declaration. This manuscript describes the impact of this transition through analyses of the characteristics of communities reached using the new virtual training format.

CASE PRESENTATION

Training participant addresses were compared to county rates of opioid overdose deaths and broadband internet access, and census block comparison to health services shortages, rural designation, and race/ethnicity community characteristics.

CONCLUSIONS

The virtual training format reached more learners than the in-person events. Training reached nearly half of the counties in Texas, including all with recent opioid overdose deaths. Most participants lived in communities with a shortage of health service providers, and training reached rural areas, those with limited broadband internet availability, and majority Hispanic communities. In the context of restrictions on in-person gathering, the training program successfully shifted to a live, online format. This transition increased participation above rates observed pre-pandemic and reached communities with the need for equipping those most likely to witness an opioid overdose with the proper use of naloxone.

Adaptation of the collaborative care model to integrate behavioral health care into a low-barrier HIV clinic

Background

The collaborative care management (CoCM) model is an evidence-based intervention for integrating behavioral health care into nonpsychiatric settings. CoCM has been extensively studied in primary care clinics, but implementation in nonconventional clinics, such as those tailored to provide care for high-need, complex patients, has not been well described.

Method

We adapted CoCM for a low-barrier HIV clinic that provides walk-in medical care for a patient population with high levels of mental illness, substance use, and housing instability. The Exploration, Preparation, Implementation, and Sustainment model guided implementation activities and support through the phases of implementing CoCM. The Framework for Reporting Adaptations and Modifications to Evidence-Based Interventions guided our documentation of adaptations to process-of-care elements and structural elements of CoCM. We used a multicomponent strategy to implement the adapted CoCM model. In this article, we describe our experience through the first 6 months of implementation.

Results

The key contextual factors necessitating adaptation of the CoCM model were the clinic team structure, lack of scheduled appointments, high complexity of the patient population, and time constraints with competing priorities for patient care, all of which required substantial flexibility in the model. The process-of-care elements were adapted to improve the fit of the intervention with the context, but the core structural elements of CoCM were maintained.

Conclusions

The CoCM model can be adapted for a setting that requires more flexibility than the usual primary care clinic while maintaining the core elements of the intervention.

Integrating peer support services into primary care-based OUD treatment: Lessons from the Penn integrated model

Opioid use disorder (OUD) is a major public health emergency in the United States. In 2020, 2.7 million individuals had an OUD. Medication for opioid use disorder is the evidence-based, standard of care for treating OUD in outpatient settings, especially buprenorphine because it is effective and has low toxicity. Buprenorphine is increasingly prescribed in primary care, a setting that provides greater anonymity and convenience than substance use disorder treatment centers. Yet two-thirds of people who begin buprenorphine treatment discontinue within the first six months. Treatment dropout elevates the risks of return to use, infections, higher levels of medical care and related costs, justice system involvement, and death. One promising form of retention support is peer service programs. Peers combine their lived experience of substance use and recovery with formal training to help patients engage and persist in OUD treatment. They provide a range of services, including health education, encouragement and empathy, coping skills, recovery modeling, and concrete assistance in overcoming the situational barriers to retention. However, guidance is needed to define the peer role in primary care, the specific tasks peers should perform, the competencies those tasks require, training and professional development needs, and peer performance standards. Guidance also is needed to integrate peers into the care team, allocate and coordinate responsibilities among care team members, manage peer operations and workflow, and facilitate effective team communication. Here we describe a peer support program in the University of Pennsylvania Health System (UPHS or Penn Medicine) network of primary care practices. This paper details the program's core components, values, and activities. We also report the organizational challenges, unresolved questions, and lessons for the field in administering a peer support program to meet the needs of patients served by a large, urban medical system with an extensive suburban and rural catchment area. CLINICAL TRIALS REGISTRATION: www.clinicaltrials.gov registration: NCT04245423.

Associations between new health conditions and healthcare service utilizations among older adults in the United Kingdom: effects of COVID-19 risks, worse financial situation, and lowered income

Background

Health services are critically important for older adults, particularly during the Coronavirus disease-19 (COVID-19) pandemic. However, COVID-19 risks, worse financial situation, and lowered income may seriously impact health services by feasibility and accessibility. Therefore, the aim of the present study was empirically to explore how health-seeking behaviors are influenced by new health conditions through COVID-19 risks, worse financial situation, and lowered income.

Methods

Data were from ELSA COVID-19 waves 1 and 2 which included a sample of 6952 and 6710 older adults in the United Kingdom, respectively. The frequency distribution analyses were conducted by Chi-square analysis by gender groups. Zero-inflated Poisson regressions were used to examine how worse financial situation and lowered income were associated with COVID-19 risks and new health conditions. Logistic regressions were employed to examine the associations of COVID-19 risks, worse financial situation, and lowered income with treatment cancellation and accessible care. Cross-sectional mediation models, cross-sectional moderation models, longitudinal mediation models, and longitudinal moderation models were conducted based on Hayes model 6, Hayes model 29, Montoya model 1, and Montoya model 2, respectively.

Results

Most of the sample was >65 years old, females, located in urban place, and involved in long-standing condition. Regression analysis showed that COVID-19 risks, worse financial situation, and lowered income were associated with treatment cancellation and accessible care. In the longitudinal mediations, effect coefficients of ‘X’ → (treatment cancellation in wave 1 (Tcn1)- treatment cancellation in wave 2 (Tcn2))(β = −.0451, p < .0001, low limit confidence interval (LLCI) = −.0618, upper limit confidence interval (ULCI) = −.0284), ‘X’ → (COVID-19 risks in wave 1 (Csk1)- COVID-19 risks in wave 2 (Csk2)) (β = .0592, p < .0001, LLCI = .0361, ULCI = .0824), and ‘X’ → (lowered income in wave 1 (CIn1)- lowered income in wave 2 (CIn2)) (β = −.0351, p = .0001, LLCI = -.0523, ULCI = -.0179) were significant. Additionally, effect coefficients of ‘X’ → (accessible care in wave 1 (Acr1)- accessible care in wave 2 (Acr2)) (β = .3687, p < .0001, LLCI = .3350, ULCI = .4025),'X’ → (Csk1- Csk2) (β = .0676, p = .0005, LLCI = .0294, ULCI = .1058), and ‘X’ → (worse financial situation in wave 1- worse financial situation in wave 2) (β = −.0369, p = .0102, LLCI = -.0650, ULCI = -.0087) were significant.

Conclusions

There were longitudinal mediating effects of COVID-19 risks, worse financial situation, and lowered income on the relationship between new health conditions and treatment cancellation and relationship between new health conditions and accessible care. These findings suggest that worse financial situation, lowered income, and COVID-19 risks exerted an influence on the relationship between new health conditions and treatment cancellation and relationship between new health conditions and accessible care among older adults. Findings suggest that longitudinal mediations may be important components of interventions aiming to meet service needs. Long-term health policy implications indicate the need for reducing COVID-19 risks, improving financial situation, and increasing income among the targeted population.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-02995-8.

Review, Assess, Classify, and Evaluate (RACE): a framework for studying m-health apps and its application for opioid apps

OBJECTIVE

The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps.

MATERIALS AND METHODS

The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy.

RESULTS

Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps.

DISCUSSION

Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions.

CONCLUSION

With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care.