Funded Projects

One novel approach to address the opioid crisis is predicting the likelihood of retention in treatment for opioid use disorder (OUD) by assessing someone?s risk of early departure from treatment. Current methods rely on providers intuition to identify when an individual is at risk of leaving treatment early in order to intervene. This intervention, when it happens, often comes too late. Mobile health (mHealth) and Machine Learning (ML) predictive analytics offer a new opportunity to personalize OUD treatment, improve retention in OUD care, and mitigate the risk of relapse and overdose episodes. Project Motivate will combine physiological and behavioral data from disparate sources in order to predict when an individual is at risk of early departure from OUD treatment. If successful, results of the study will save lives, and lower medical costs, municipal emergency response costs, recidivism, workplace accidents, lost workplace productivity and costs to families.

Risk for opioid use disorder (OUD) often begins in adolescence and young adulthood. Engaging and retaining adolescents and young adults (collectively, “youth”) in early, effective treatment is critical for improving the life course trajectory of addiction. For adults with OUD, office-based opioid treatment (OBOT) with a collaborative care approach that includes behavioral therapy optimizes patient engagement and retention in care. Collaborative care OBOT is especially promising for youth, who can receive treatment from a trusted primary care provider in the same familiar setting they receive their usual medical care. To date, however, OBOT has not been formally adapted for treating youth. The central objective of this project is to develop and pilot an enhanced OBOT model for youth that is developmentally appropriate and family centered. The multidisciplinary nature of our team, which includes expertise in advanced biostatistical analysis, qualitative research, intervention development, developmental psychology, and implementation and improvement science, will maximize the chances of filling an important gap in the provision of youth specific evidence-based OUD interventions.

People with opioid use disorders (OUD) have high rates of co-occurring alcohol, stimulant and other drug disorders, as well as mental disorders. Traditionally, treatment for OUD has been ?siloed? even though these high rates of co-occurring conditions emphasize the need for comprehensive treatment to address holistic needs. As the opioid crisis continues, attention to the whole person and access to comprehensive mental health and substance use treatment as well as primary care is needed. This study aims to better understand co-occurring mental health disorders, alcohol use disorders, and/or other substance use disorders among people with OUD, in the context of innovative integrated care networks for people with OUD. This study examines how innovative OUD treatment models work for individuals with co-occurring mental health and substance use by 1) assessing mental health treatment quality measures and outcomes; 2) testing how these innovative treatment models compare to other OUD treatment for people who have OUD and other substance use disorders; and 3) considering the ways people with OUD access co-occurring disorder care. The findings from this study will provide needed information to improve mental health, alcohol, and other substance use treatment for individuals with OUD, whether or not they are in OUD treatment and may provide information to help move the system from siloed efforts to truly integrated care

Four out of five youth in the juvenile justice (JJ) system show evidence of being under the influence during their offenses, and more than half test positive for substances at the time of their arrest. Preventive intervention approaches that can be easily implemented within JJ settings may offer greater access to substance use care as well as increase families’ motivation to comply with court referrals to seek further services. It is especially important to evaluate interventions for court-involved, non-incarcerated (CINI) juveniles, as these youth account for two-thirds of those arrested; however, the bulk of extant research has been conducted with detained or incarcerated youth. In this application for supplemental funding, we capitalize on our parent grant (Brief Individual and Parent Interventions for Marijuana Misuse in Truant Adolescents) by proposing to develop an adjunctive, targeted preventive intervention for marijuana-using, JJ youth who are at elevated risk for illicit opioid use. The goal will be to develop a protocol for a single-session, parent-adolescent preventive intervention to decrease the likelihood of illicit opioid use in CINI adolescents. This formative work will culminate in a draft intervention manual.

Social safety net programs and Medicaid that provide basic necessities such as shelter, health care, and food to people with low incomes are particularly important for women parents who use drugs. This project will examine the separate and combined impact of state social safety net program eligibility and administration on opioid-related behavioral outcomes for women parents experiencing poverty. Findings from this research will provide actionable recommendations for changes to these programs that may promote health and well-being for these women.

Sleep and circadian rhythms are understudied risk factors for opioid use disorder (OUD) and its treatment. Opioids affect sleep quality in a way that can inhibit recovery. The two most effective medications for OUD also cause sleep problems. This project will increase understanding about underlying circadian and behavioral mechanisms, such as changes in craving and/or the ability to regulate emotions, that link poor sleep with suboptimal opioid treatment response outcomes.

According to SAMHSA’s 2017 National Survey on Drug Use and Health (NSDUH), 11.4 million persons in the U.S. report past-year opioid misuse; out of them, only 2.1 million individuals met criteria for an OUD. Very little is known about efficacious interventions for those who do not meet criteria for moderate/severe OUD (i.e., subthreshold OUD). The prevalence of subthreshold OUD in primary care settings is 5 percent to 10 percent, with higher rates (21 percent to 29 percent) among those receiving prescribed opioids. Although they are at high risk of developing moderate/severe OUD and/or dying from an overdose, little or no empirical evidence exists for pragmatic prevention interventions that can be adopted at integrated general medical settings. To study the efficacy of prevention interventions to arrest the progression from risky opioid use, researchers will test the efficacy of a STOP intervention in primary care settings. STOP adopts an early intervention approach, based on a collaborative care model to prevent progression to moderate/severe OUD, and consists of a practice-embedded nurse care manager who provides patient education and supports the primary care provider (PCP) in engaging, monitoring and guiding patients who have risky opioid use; brief advice delivered to patients by their PCP; and phone counseling of patients by behavioral health providers to motivate and support behavior change. Researchers will determine whether STOP reduces risky opioid use and examine the impact of STOP on progression to moderate/severe OUD, overdose risk behavior and overdose events in adults with risky use of illicit or prescription opioids.

This project is focused on identifying the clinical, genetic, and neural characteristics that convey risk for prescription opioid addiction. We will leverage the central biorepository and electronic health record (EHR) database of the Geisinger Health System to conduct large-scale genomics research and phenotype development. Through a collaboration with Regeneron Pharmaceuticals, the Geisinger biobank currently contains DNA samples on about 110,000 participants and includes both Illumina OmniExpressExome genotyping and whole exome sequence data, including common and rare variants, from over 60,000 of these subjects. This discovery cohort contains thousands of chronic musculoskeletal pain patients who have been taking greater than 120 mg equivalents of morphine for more than three months. Using EHR and self-report tools to develop a case definition and quantitative scoring, we will derive a clinical/genetic profile of prescription opioid addiction. This profile will be enhanced via integration of neuroimaging data.

People with opioid use disorder (OUD) are at increased risk of depression and other mental health conditions, and a significant proportion of opioid-related deaths are likely suicides. Nearly 50% of patients who die by suicide make a healthcare visit in the month prior, most often to primary care. Yet systematic screening of patients with OUD for suicide risk is rarely done. Clinical decision support tools within the electronic health record can improve healthcare prevention measures and important clinical outcomes. This primary care-based clinic-randomized trial will integrate a clinical decision support tool for suicide risk prediction with a clinical decision support tool for the identification, diagnosis, and treatment of opioid use disorder. By integrating these two tools, the study will identify patients with opioid use disorder who have increased risk for suicide, ultimately increasing engagement in both OUD treatment and outpatient mental health care

This supplement supports the goals of the current award, “An Evaluation of the National Zero Suicide Model Across Learning Healthcare Systems” (U01MH114087). Safety planning is a highly recommended practice within the Zero Suicide framework, but little is known about the effectiveness of the individual elements that can make up a safety plan, such as lethal means assessment, identification of supportive contacts, coping skills, warning signs, and sources of distraction. All of the documentation lives in text-based clinical narratives. This supplement will support development of new metrics using natural language processing to determine baseline rates, from which we can quantify the change in safety planning and lethal means assessment practice longitudinally after implementation of new safety planning templates using our Zero Suicide main award.

Patients with opioid use disorder and co-occurring chronic pain and anxiety are at the highest risk for opioid overdose deaths. Low-intensity focused ultrasound (LIFU) is an innovative, noninvasive method that can be used to alter brain activity and potentially repair dysfunctional brain circuits involved in these disorders. This project will examine how LIFU directed to a small but critical brain region implicated in all three of these disorders, the anterior insula, can reduce drug craving, pain response, and anxiety symptoms as well as improve the physiological processes that may underlie the symptoms experienced by these patients.

Chronic pain and opioid use disorders (OUD) are burgeoning interrelated epidemics. Sleep disturbances are prevalent, treatable, and increasingly recognized as risk factors for both chronic pain and OUD. Sleep disruption impairs endogenous pain inhibition, linked to analgesic efficacy and rewarding properties of mu-opioid receptor (MOR) agonists. It is not known, however, whether sleep disturbance causally alters mechanisms that contribute to OUD risk. Sleep continuity disruption (SCD) and/or sleep fragmentation (SF) may alter cerebral MOR availability, and these forms of sleep disruption may increase OUD risk. This study aims to 1) evaluate whether experimental SCD and/or SF alter resting or pain-evoked MOR binding potential (BP) in brain regions associated with pain inhibition; 2) examine whether SCD and/or SF alters the analgesic response; and 3) determine whether MOR BP in brain regions of interest are associated with analgesia and abuse liability.