Faculty Bibliography

BACKGROUND:A majority of the 8.9 million Americans with opioid misuse have mild or no symptoms of opioid use disorder (OUD), but they may be at elevated risk of developing more severe OUD, overdose, or other health consequences of opioid use. The "Subthreshold Opioid Use Disorder Prevention"(STOP) Trial is evaluating a collaborative care intervention for risky opioid use in primary care. Here, we describe baseline characteristics of participants to understand their needs and assess the generalizability of the sample. METHODS:Recruitment at five primary care sites spanned March 2021-May 2023. Adult patients who screened positive for subthreshold OUD (current illicit or non-medical opioid use without meeting DSM-5 criteria for moderate-severe OUD) were eligible. Baseline assessments measured self-reported demographic characteristics, other substance use, pain, and physical and mental health symptoms. Descriptive statistics summarize characteristics of the enrolled sample across sites. RESULTS:Among the 202 participants, the majority identified as female (63.4%), white (70.8%), and non-Hispanic (96.5%), with mean age 55.7 (SD: 12.7) years. Nearly half (49.0%) had problem or high-risk use of prescription opioids, and most received a prescription for opioid medication in the past six months (74.8%). Many participants reported current problem use or high-risk use of alcohol (47.0%) or cannabis (31.2%). Approximately one-third endorsed mental health symptoms, including moderate-severe anxiety (35.6%), depression (31.2%), or sleep disturbance (29.7%), and 20.3% reported a past suicide attempt. In the prior six months, 14.7% had experienced a nonfatal overdose. Moderate-severe pain was reported by 63.4%, and 60.4% rated their general health as fair or poor. CONCLUSIONS:Patients with subthreshold OUD had high rates of polysubstance use and comorbidities that may present challenges to reducing risky opioid use. The STOP trial presents an opportunity to detect and address subthreshold OUD in a cohort with considerable medical and social needs, within primary care settings. CLINICAL TRIALS REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT04218201.

BACKGROUND:The Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) tool is a screening and brief assessment instrument to identify unhealthy tobacco, alcohol, drug use, and prescription medication use in primary care patients. This secondary analysis compares the TAPS tool to the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) for alcohol screening. METHODS:Adult primary care patients (1124 female, 874 male) completed the TAPS tool followed by AUDIT-C. Performance of each instrument was evaluated against a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview, to identify problem use and alcohol use disorder (AUD). Area under the curve (AUC) appraised discrimination, and sensitivity and specificity were calculated for Youden optimal score thresholds. RESULTS:For identifying problem use: On the AUDIT-C, AUC was 0.90 (95% Confidence Interval: 0.86-0.92) for females and 0.91 (0.89-0.93) for males. Sensitivity and specificity for females were 0.89 (0.83-0.93) and 0.78 (0.75-0.80), respectively, and for males were 0.84 (0.79-0.88) and 0.82 (0.79-0.85). On the TAPS tool, AUC was 0.82 (0.79-0.86) for females and 0.81 (0.78-0.84) for males. Sensitivity and specificity for females were 0.78 (0.72-0.84) and 0.78 (0.75-0.81), respectively, and for males were 0.76 (0.71-0.81) and 0.76 (0.72-0.79). For AUD: On the AUDIT-C, AUC was 0.90 (0.88-0.93) for both females and males. Sensitivity and specificity for females were 0.83 (0.74-0.90) and 0.83 (0.80-0.85), respectively, while for males, they were 0.81 (0.74-0.87) and 0.84 (0.81-0.87). On the TAPS tool, AUC was 0.84 (0.80-0.89) for females and 0.82 (0.78-0.86) for males. Sensitivity and specificity for females were 0.73 (0.63-0.81) and 0.85 (0.83-0.88), respectively, while for males, they were 0.75 (0.68-0.81) and 0.84 (0.81-0.86). CONCLUSION/CONCLUSIONS:The AUDIT-C performed somewhat better than the TAPS tool for alcohol screening. However, the TAPS tool had an acceptable level of performance for alcohol screening and may be advantageous in practice settings seeking to identify alcohol and other substance use with a single instrument.

OBJECTIVES/OBJECTIVE:We explored medications for opioid use disorder treatment (MOUD) utilization in six New York City public hospitals that implemented the "Consultation for Addiction Care and Treatment in Hospitals (CATCH)" program. METHODS:CATCH rolled out between October 2018 and February 2020. Data from the electronic health record were analyzed for the first year post-implementation. Eligible cases included adults with an opioid-related diagnosis admitted to inpatient departments served by CATCH, with a stay of ≥1 night. Patients were classified as receiving an MOUD order if there was at least 1 order of buprenorphine, methadone, or naltrexone. Logistic regression modeled the impact of CATCH consults on MOUD orders, controlling for demographic and clinical characteristics with hospital as a random effect. RESULT/RESULTS:Among 2117 eligible patients, 71.4% were male, with a mean age of 51.2 years, and 27.2% identified as Black, 21.2% as White, and 34.5% as Hispanic. MOUD was ordered in 60.9% of admissions, and 41.5% had a completed CATCH consult. Patients identified as Black had lower odds of receiving a MOUD order than those identified as White (OR: 0.52, 95% CI: 0.38-0.71; P < 0.001). Patients with a CATCH consult had higher odds of receiving a MOUD order (OR: 3.22, 95% CI: 2.54-4.07; P < 0.001). CONCLUSION/CONCLUSIONS:Majority of patients in our sample received a MOUD order, with higher odds among those with a CATCH consult. Further research is needed on the drivers of racial disparities in MOUD, and other contextual, organizational, and population-specific barriers and facilitators contributing to receipt of hospital-based addiction consult services and MOUD.

BACKGROUND:In response to the heavy burden of untreated substance use disorders (SUD) in hospital patients, many health systems are implementing addiction consult services staffed by interprofessional teams that diagnose SUD, make recommendations for SUD care in the hospital, and link patients to post-discharge treatment. In 2018, the New York City public hospital system began rolling out the Consult for Addiction Treatment and Care in Hospitals (CATCH) program in six hospitals. CATCH teams are comprised of an addiction-trained medical provider, social worker or addiction counselor, and peer counselor. METHODS:The study conducted qualitative interviews with CATCH staff at all six participating hospitals as part of a pragmatic trial studying the effectiveness and implementation of CATCH. The Consolidated Framework for Implementation Research (CFIR) framework guided interviews conducted from 2018 to 2021 with 26 staff at the start of implementation and with 33 staff 9-12 months post-implementation. The study team created a codebook a priori and further refined it through additional coding of initial interviews. Codes were systematically analyzed using the CFIR. RESULTS:Barriers and facilitators spanned four CFIR domains: inner setting, outer setting, process, and individual characteristics. Barriers identified were primarily related to the outer and inner settings, including patient characteristics and limited resources (e.g. medical comorbidities, homelessness), insurance, CATCH team role confusion, and infrastructure deficits (e.g., availability of physical space). Additional barriers related to process (workload burden), and characteristics of individuals (stigma and lack of comfort treating SUD among medical teams). Facilitators were mostly related to the characteristics of individuals on the CATCH team (advantages and expertise of the CATCH peer counselor, CATCH team communication and cohesiveness) and inner setting (CATCH team relationships with hospital staff, hospital leadership buy-in and support, and infrastructure). Community networks (outer setting) and CATCH training resources (process) were also facilitators of program implementation. CONCLUSION/CONCLUSIONS:Addiction consult services have great potential for improving care for hospital patients with SUD, but as new programs in busy hospital settings they face barriers to implementation that could impact their effectiveness. Barriers may be particularly impactful for programs operating in safety-net hospitals, given limited resources within the health system and the multiple and complex needs of their patients. Understanding the strengths of these programs as well as the barriers to their implementation is critical to utilizing addiction consult services effectively.

OBJECTIVE:This analysis evaluated the validation results of the Tobacco, Alcohol, Prescription Medication, and Other Substance Use (TAPS) tool for older adults. METHODS:We performed a subgroup analysis of older adults aged ≥65 (n = 184) from the TAPS tool validation study conducted in 5 primary care clinics. We compared the interviewer and self-administered versions of the TAPS tool at a cutoff of ≥1 for identifying problem use with a reference standard measure, the modified World Mental Health Composite International Diagnostic Interview. RESULTS:The mean age was 70.6 ± 5.9 years, 52.7% were female, and 49.5% were non-Hispanic Black. For identifying problem use, the self-administered TAPS tool had sensitivity of 0.91 (95% CI: 0.75-0.98) and specificity of 0.91 (95% CI: 0.85-0.95) for tobacco; sensitivity of 0.68 (95% CI: 0.45-0.86) and specificity of 0.88 (95% CI: 0.82-0.93) for alcohol; and sensitivity 0.86 (95% CI: 0.42-1.00) and specificity 0.94 (95% CI: 0.90-0.97) for cannabis. The interviewer-administered TAPS tool had similar results. We were unable to evaluate its performance for identifying problem use of individual classes of drugs other than cannabis in this population due to small sample sizes. CONCLUSIONS:While the TAPS had excellent sensitivity and specificity for identifying tobacco use among older adults, the results for other substances lack precision, and we were unable to evaluate its performance for prescription medications and individual illicit drugs in this sample. This analysis underlines the critical need to adapt and validate screening tools for unhealthy substance use, specifically for older populations who have unique risks.

BACKGROUND:Individuals with opioid use disorder have high rates of hospital admissions, which represent a critical opportunity to engage patients and initiate medications for opioid use disorder (MOUD). However, few patients receive MOUD and, even if MOUD is initiated in the hospital, patients may encounter barriers to continuing MOUD in the community. OBJECTIVE:Describe hospital providers' experiences and perspectives to inform initiatives and policies that support hospital-based MOUD initiation and continuation in community treatment programs. DESIGN/METHODS:As part of a broader implementation study focused on inpatient MOUD (NCT#04921787), we conducted semi-structured interviews with hospital providers. PARTICIPANTS/METHODS:Fifty-seven hospital providers from 12 community hospitals. APPROACH/METHODS:Thematic analysis examined an emergent topic on challenges transitioning patients to outpatient MOUD treatment and related impacts on MOUD initiation by inpatient providers. KEY RESULTS/RESULTS:Participants described structural barriers to transitioning hospitalized patients to continuing outpatient MOUD including (a) limited outpatient buprenorphine prescriber availability, (b) the siloed nature of addiction treatment, and (c) long wait times. As a result of observing these structural barriers, participants experienced a sense of futility that deterred them from initiating MOUD. Participants proposed strategies that could better support these patient transitions, including developing partnerships between hospitals and outpatient addiction treatment and supporting in-reach services from community providers. CONCLUSIONS:We identified concerns about inadequate and inaccessible community-based care and transition pathways that discouraged hospital providers from prescribing MOUD. As hospital-based opioid treatment models continue to expand, programmatic and policy strategies to support inpatient transitions to outpatient addiction treatment are needed. NCT TRIAL NUMBER/UNASSIGNED:04921787.

BACKGROUND:Despite proliferation of acute-care interventions to initiate medications for opioid use disorder (MOUD), significant challenges remain to supporting care continuity following discharge. Research is needed to inform effective hospital strategies to support patient transitions to ongoing MOUD in the community. OBJECTIVE:To inform a taxonomy of care transition strategies to support MOUD continuity from hospital to community-based settings and assess their perceived impact and feasibility among experts in the field. DESIGN/METHODS:A modified Delphi consensus process through three rounds of electronic surveys. PARTICIPANTS/METHODS:Experts in hospital-based opioid use disorder (OUD) treatment, care transitions, and hospital-based addiction treatment. MAIN MEASURES/METHODS:Delphi participants rated the impact and feasibility of 14 OUD care transition strategies derived from a review of the scientific literature on a scale from 1 to 9 over three survey rounds. Panelists were invited to suggest additional care transition strategies. Agreement level was calculated based on proportion of ratings within three points of the median. KEY RESULTS/RESULTS:Forty-five of 71 invited panelists participated in the survey. Agreement on impact was strong for 12 items and moderate for 10. Agreement on feasibility was strong for 11 items, moderate for 7, and poor for 4. Strategies with highest ratings on impact and feasibility included initiation of MOUD in-hospital and provision of buprenorphine prescriptions or medications before discharge. All original 14 strategies and 8 additional strategies proposed by panelists were considered medium- or high-impact and were incorporated into a final taxonomy of 22 OUD care transition strategies. CONCLUSIONS:Our study established expert consensus on impactful and feasible hospital strategies to support OUD care transitions from the hospital to community-based MOUD treatment, an area with little empirical research thus far. It is the hope that this taxonomy serves as a stepping-stone for future evaluations and clinical practice implementation toward improved MOUD continuity and health outcomes.

BACKGROUND:Emergency departments (EDs) are critical touchpoints for overdose prevention efforts. In New York City (NYC), the Health Department's Relay initiative dispatches trained peer "Wellness Advocates" (WAs) to engage with patients in EDs after an overdose and for up to 90 days subsequently. Interest in peer-delivered interventions for patients at risk for overdose has grown nationally, but few studies have explored challenges and opportunities related to implementing such interventions in EDs. METHODS:We conducted in-depth interviews with Relay WAs, ED patients, and ED providers across 4 diverse NYC EDs. Sampling was purposeful and continued until theoretical saturation was reached. Interviews followed a semi-structured interview guide based on key domains from the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted by telephone or web conferencing; audio recordings were professionally transcribed. The study utilized rapid qualitative analysis using template summaries and summary matrices followed by line-by-line coding conducted independently by 3 researchers, then discussed and harmonized at group coding meetings. Coding was both inductive (using an a priori code list based on CFIR domains and study goals) and deductive (new codes allowed to emerge from transcripts). Dedoose software was used for data organization. RESULTS:We conducted 32 in-depth interviews (10 WAs, 12 patients, 10 ED providers). Four overarching themes emerged: 1) EDs are characterized by multiple competing demands (e.g., related to provider time and physical space), underscoring the utility of Relay and leading to some practical challenges for its delivery; 2) There is a strong role distinction of WAs as peers with lived experience; 3) ED providers value Relay, even though they have a limited understanding of its full scope and outcomes; 4) While the role of structural factors (e.g., homelessness and unstable housing) is recognized, responsibility is often placed on patients for controlling their own success. CONCLUSIONS:We identified four themes that shed new light on the implementation of peer-based overdose prevention programs in EDs. Our findings highlight unique ED inner and outer setting factors that may impact program implementation and effectiveness. The findings provide actionable information to inform implementation of similar programs nationally.

INTRODUCTION/BACKGROUND:Six hospitals within the New York City public hospital system implemented the Consult for Addiction Treatment and Care in Hospitals (CATCH) program, an interprofessional addiction consult service. A stepped-wedge cluster randomized controlled trial tested the effectiveness of CATCH for increasing initiation and engagement in post-discharge medication for opioid use disorder (MOUD) treatment among hospital patients with opioid use disorder (OUD). The objective of this study was to identify facility characteristics that were associated with stronger performance of CATCH. METHODS:This study used a mixed methods multiple-case study design. The six hospitals in the CATCH evaluation were each assigned a case rating according to intervention reach. Reach was considered high if ≥50 % of hospitalized OUD patients received an MOUD order. Cross-case rating comparison identified attributes of high-performing hospitals and inductive and deductive approaches were used to identify themes. RESULTS:Higher-performing hospitals exhibited attributes that were generally absent in lower-performing hospitals, including (1) complete medical provider staffing; (2) designated office space and resources for CATCH; (3) existing integrated OUD treatment resources; and (4) limited overlap between the implementation period and COVID-19 pandemic. CONCLUSIONS:Hospitals with attributes indicative of awareness and integration of OUD services into general care were generally higher performing than hospitals that had siloed OUD treatment programs. Future implementations of addiction consult services may benefit from an increased focus on hospital- and community-level buy-in and efforts to integrate MOUD treatment into general care.

INTRODUCTION/BACKGROUND:Methadone and buprenorphine are effective treatment for opioid use disorder (OUD), yet they are vastly under-utilized across US hospitals. To inform a national trial assessing the effectiveness of implementation strategies to increase adoption of an inpatient hospital-based opioid treatment (HBOT) model (NCT04921787), we explored barriers and facilitators to expanding medication for opioid use disorder (MOUD) within community hospitals across the United States. METHODS:From November 2021 to March 2022, we used purposeful and snowball sampling to identify and interview participants involved in inpatient care of patients with OUD from twelve community hospitals. We conducted semi-structured interviews on providers' experiences and perspectives on current treatment approaches as well as potential influences on MOUD expansion in their hospitals. We used thematic analysis to identify key barriers and facilitators that could impact implementation of an HBOT model, and organized these findings based on the Consolidated Framework for Implementation Research (CFIR). RESULTS:From qualitative interviews with 57 participants (30 physicians, 7 pharmacists, 6 nurses, and 14 professionals involved in the care of patients with OUD), we identified key barriers and facilitators mapped to CFIR's internal and outer settings. The most salient inner setting domains included tension for change and relative priority, compatibility, available resources, organizational culture, access to knowledge and information, relational connections and communications, and information technology infrastructure. Outer setting domains included policies and laws, financing, and partnerships and connections. CONCLUSIONS:Identifying potential barriers and facilitators can inform hospital-specific strategies to support implementation of HBOT. Implementation strategies that address barriers such as staff availability, knowledge, and attitudes may support increased HBOT adoption. On a broader scale, national policy changes such as increased financing and public reporting of quality metrics would address other barriers we identified and may also encourage hospitals to adopt HBOT models.