Literature Collection

BACKGROUND: Contingency management (CM) is an effective yet underutilized behavioral intervention that uses rewards to improve outcomes in medication for opioid use disorder (MOUD) treatment. Prior implementation attempts have focused on specialized addiction clinics, using intensive daily treatment with methadone and high reward values (e.g. >$200 total). However, many people get MOUD from less specialized, more accessible, family medicine clinics. These clinics could also benefit from CM, yet present unique challenges for CM. Family medicine clinics typically use buprenorphine as their primary medication, which requires less intensive dosing schedules and thus provides fewer CM opportunities. They may also have lower institutional willingness to use high-value rewards. As an initial step in user-centered design of a low value reward (<$75 total) CM program for the family medicine context, we conducted qualitative interviews with patients and staff in the buprenorphine treatment program of a family medicine department. We gathered and analyzed qualitative data on CM knowledge, preferred program parameters, and implementation considerations. METHOD: Participants (N = 24) were buprenorphine treatment staff (n = 12) and patients (n = 12). Participants completed 30-50-minute semi-structured interviews, analyzed using rapid matrix analysis. RESULTS: Participants had little experience with CM, but generally viewed CM as acceptable, appropriate, and feasible. Interviewees coalesced around having staff who were not providers with prescription privileges conduct CM, consistent rather than escalating payments, and physical rewards delivered in-person. Potential challenges included medical record integration, demands on staff time, and confirmation of patients' goal completion. CONCLUSIONS: Patient and staff feedback was well-aligned, especially regarding rewards as an opportunity for staff-patient connection and the need for simplicity. Some consensus suggestions (e.g. non-escalating rewards) conflict with extant CM literature. Implications for implementation of CM in this setting are presented. These findings inform user-centered design and iteration of a CM program for this accessible, non-specialized family medicine setting.

RATIONALE: Few coronary heart disease (CHD) patients engage in evidence-based depression treatments (ie, antidepressants, therapy, exercise). We present the protocol and analysis plan for a hybrid type II effectiveness-implementation trial evaluating the impact of a theory-informed, multilevel implementation strategy centered around an electronic shared decision making (eSDM)/patient activation tool. DESIGN: The IHEART DEPCARE Trial uses a pre-post single group, open label design with 4 sites (each with a cluster of cardiology clinics and a cluster of primary care clinics, 8 clusters in total) introduced to the multilevel strategy (ie, single arm) in random order with patients (a pre-implementation cohort and nonoverlapping post-implementation cohort) nested within clinicians, nested within clusters. All primary care and cardiology clinicians at participating clinics are included. The patient sample includes English- and Spanish-speaking CHD patients ≥21 years of age with screen-detected elevated depressive symptoms (ie, Patient Health Questionnaire-9 score ≥10) and a scheduled visit during the relevant time period. In the pre-implementation period, CHD patients receive usual care. At the start of each implementation period, a site's behavioral health providers (BHPs) and clinic administrators are invited to problem solving meetings; patients receive an eSDM and patient activation tool that includes psychoeducation, patient activation, and treatment selection support; and clinicians/BHPs receive a summary report of patients' preferences (implementation strategy). During pre- and post-implementation periods, patients are assessed at baseline and 6 months for depressive symptoms, depression treatment intensification, health-related quality of life years, and (at baseline only) patient activation and decisional conflict. The primary effectiveness outcome is change in depressive symptoms from baseline to follow-up during the post-implementation period compared to pre-implementation period. Key trial design changes, relative to our initial pre-COVID-19 trial protocol, include transition from a stepped wedge design to a single pre- post design randomized to strategy timing, reduction of exclusion criteria, options to bypass clinicians for direct BHP referrals (vs reliance on referrals) and addressing multiplicity in our statistical analysis plan. The trial was launched in April 2019 and is estimated to conclude by July 2025. DISCUSSION: The IHEART DEPCARE Trial is the first hybrid type II effectiveness-implementation trial to examine the effect of a brief, theory-informed eSDM and patient activation tool strategy on depression treatment uptake and symptoms in CHD patients. Our protocol advances the field of implementation science by incorporating a multilevel (vs single-level) implementation strategy to address depression, highlighting unique challenges of stepped wedge designs and hybrid effectiveness-implementation trials, and demonstrating alternative design approaches.

BACKGROUND: Type 1 diabetes (T1D) management requires following a complex and constant regimen relying on child or caregiver behaviors, skills, and knowledge. Psychological factors such as diabetes distress (DD), depression, and burnout are pertinent considerations in the treatment of pediatric T1D. Approximately 40% of youth and 61% of caregivers experience DD. Implementation of DD screening as part of clinical best practice is recommended and may facilitate treatment referral, perhaps leading to improved health or well-being for youth with T1D and their caregivers. By building on existing institutional infrastructure when available, screening via digital health platforms (applications, or "apps") may allow for timely screening of, and response to, DD. OBJECTIVE: This work details the creation, implementation, and refinement of a process to screen for DD in youth and their caregivers in the context of routine T1D care using a digital health platform. METHODS: DD screening was implemented in an outpatient endocrinology clinic over 1 year as part of a larger screen-to-treat trial for children aged 8-12.99 years and their caregivers. Validated measures were sent via digital health platform to be completed prior to the clinic visit. Results were initially reviewed manually, but a digital best practice alert (BPA) was later built to notify staff of elevated scores. Families experiencing DD received resources sent via the digital health platform. For this secondary analysis, child demographics and glycated hemoglobin A1c (HbA1c) were collected. RESULTS: During the screening period, absolute completion rates were 36.78% and 38.83%, with adjusted screening rates at 52.02% and 54.48%, for children and caregivers, respectively. A total of 21 children (mean HbA1c 8.04%, SD 1.39%) and 26 caregivers (child mean HbA1c 8.04%, SD 1.72%) reported elevated DD. Prior to BPA development, resources were sent to all but 1 family. After BPA implementation, all families were sent resources. CONCLUSIONS: Early findings indicate that DD education, screening, and response can be integrated via digital platforms in a freestanding outpatient endocrinology clinic, thereby facilitating timely treatment referral and provision of resources for those identified with distress. Notably, in the observed 1-year screening period, screening rates were low, and barriers to implementation were identified. While some implementation challenges were iteratively addressed, there is a need for future quality improvement initiatives to improve screening rates and the identification of, or response to, DD in our pediatric patients and their families.

AIM: Integrated youth services (IYS) have been identified as a national priority in response to the youth mental health and substance use (MHSU) crisis in Canada. In British Columbia (BC), an IYS initiative called Foundry expanded to 11 physical centres and launched a virtual service. The aim of the study was to describe the demographics of Foundry clients and patterns of service utilization during this expansion, along with the impact of the COVID-19 pandemic. METHODS: Data were analysed for all youth (ages 12-24) accessing both in-person (April 27th, 2018-March 31st, 2021) and virtual (May 1st, 2020-March 31st, 2021) services. Cohorts containing all clients from before (April 27th, 2018-March 16th, 2020) and during (March 17th, 2020-March 31st, 2021) the COVID-19 pandemic were also examined. RESULTS: A total of 23 749 unique youth accessed Foundry during the study period, with 110 145 services provided. Mean client age was 19.54 years (SD = 3.45) and 62% identified as female. Over 60% of youth scored 'high' or 'very high' for distress and 29% had a self-rated mental health of 'poor', with similar percentages seen for all services and virtual services. These ratings stayed consistent before and during the COVID-19 pandemic. CONCLUSIONS: Foundry has continued to reach the target age group, with a 65% increase in number of clients during the study period compared with the pilot stage. This study highlights lessons learned and next steps to promote youth-centred data capture practices over time within an integrated youth services context.

Background: Group-based models of Office-Based Opioid Treatment with buprenorphine-naloxone (B/N) are increasingly being implemented in clinical practice to increase access to care and provide additional therapeutic benefits. While previous studies reported these Group-Based Opioid Treatment (GBOT) models are feasible for providers and acceptable to patients, there has been no literature to help providers with the more practical aspects of how to create and maintain GBOT in different outpatient settings. Case series: We present 4 cases of GBOT implementation across a large academic health care system, highlighting various potential approaches for providers who seek to implement GBOT and demonstrate "success" based on feasibility and sustainability of these models. For each case, we describe the pros and cons and detail the personnel and resources involved, patient mix and group format, workflow logistics, monitoring and management, and sustainability components. Discussion: The implementation details illustrate that there is no one-size-fits-all approach, although feasibility is commonly supported by a team-based, patient-centered medical home. This approach includes the capacity for referral to higher levels of mental health and addiction support services and is bolstered by ongoing provider communication and shared resources across the health system. Future research identifying the core and malleable components to implementation, their evidence base, and how they might be influenced by site-specific resources, culture, and other contextual factors can help providers better understand how to implement a GBOT model in their unique clinical environment.