Faculty Bibliography

Criminal justice involved (CJI) individuals with a history of opioid use disorder (OUD) are at high risk of overdose and death in the weeks following release from jail. We developed the Justice-Community Circulation Model (JCCM) to investigate OUD/CJI dynamics post-release and the effects of interventions on overdose deaths. The JCCM uses a synthetic agent-based model population of approximately 150,000 unique individuals that is generated using demographic information collected from multiple Chicago-area studies and data sets. We use a high-performance computing (HPC) workflow to implement a sequential approximate Bayesian computation algorithm for calibrating the JCCM. The calibration results in the simulated joint posterior distribution of the JCCM input parameters. The calibrated model is used to investigate the effects of a naloxone intervention for a mass jail release. The simulation results show the degree to which a targeted intervention focusing on recently released jail inmates can help reduce the risk of death from opioid overdose.

BACKGROUND:We conducted a scoping review focused on various forms of substance use amid the pandemic, looking at both the impact of substance use on COVID-19 infection, severity, and vaccine uptake, as well as the impact that COVID-19 has had on substance use treatment and rates. METHODS:A scoping review, compiling both peer-reviewed and grey literature, focusing on substance use and COVID-19 was conducted on September 15, 2020 and again in April 15, 2021 to capture any new studies. Three bibliographic databases (Web of Science Core Collection, Embase, PubMed) and several preprint servers (EuropePMC, bioRxiv, medRxiv, F1000, PeerJ Preprints, PsyArXiv, Research Square) were searched. We included English language original studies only. RESULTS:Of 1564 articles screened in the abstract and title screening phase, we included 111 research studies (peer-reviewed: 98, grey literature: 13) that met inclusion criteria. There was limited research on substance use other than those involving tobacco or alcohol. We noted that individuals engaging in substance use had increased risk for COVID-19 severity, and Black Americans with COVID-19 and who engaged in substance use had worse outcomes than white Americans. There were issues with treatment provision earlier in the pandemic, but increased use of telehealth as the pandemic progressed. COVID-19 anxiety was associated with increased substance use. CONCLUSIONS:Our scoping review of studies to date during COVID-19 uncovered notable research gaps namely the need for research efforts on vaccines, COVID-19 concerns such as anxiety and worry, and low- to middle-income countries (LMICs) and under-researched topics within substance use, and to explore the use of qualitative techniques and interventions where appropriate. We also noted that clinicians can screen and treat individuals exhibiting substance use to mitigate effects of the pandemic. FUNDING/BACKGROUND:Study was funded by the Institution for Social and Policy Studies, Yale University and The Horowitz Foundation for Social Policy. DH was funded by a NIDA grant (R01DA048860). The funding body had no role in the design, analysis, or interpretation of the data in the study.

OBJECTIVE/UNASSIGNED:The authors sought to estimate the prevalence of past-12-month and lifetime cannabis use and cannabis use disorder among U.S. veterans; to describe demographic, substance use disorder, and psychiatric disorder correlates of nonmedical cannabis use and cannabis use disorder; and to explore differences in cannabis use and cannabis use disorder prevalence among veterans in states with and without medical marijuana laws. METHODS/UNASSIGNED:Participants were 3,119 respondents in the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III) who identified as U.S. veterans. Weighted prevalences were calculated. Logistic regression analyses tested associations of nonmedical cannabis use and cannabis use disorder with demographic and clinical correlates and examined whether prevalence differed by state legalization status. RESULTS/UNASSIGNED:The prevalences of any past-12-month cannabis use and cannabis use disorder were 7.3% and 1.8%, respectively. Lifetime prevalences were 32.5% and 5.7%, respectively. Past-12-month and lifetime cannabis use disorder prevalence estimates among nonmedical cannabis users were 24.4% and 17.4%, respectively. Sociodemographic correlates of nonmedical cannabis use and use disorder included younger age, male gender, being unmarried, lower income, and residing in a state with medical marijuana laws. Nonmedical cannabis use and use disorder were associated with most psychiatric and substance use disorders examined. CONCLUSIONS/UNASSIGNED:Among veterans, the odds of nonmedical cannabis use and use disorder were elevated among vulnerable subgroups, including those with lower income or psychiatric disorders and among survey participants residing in states with medical marijuana laws. The study findings highlight the need for clinical attention (e.g., screening, assessment) and ongoing monitoring among veterans in the context of increasing legalization of cannabis.

BACKGROUND:Drug overdoses surged during the COVID-19 pandemic, underscoring the need for expanded and accessible substance use disorder (SUD) treatment. Relatively little is known about the experiences of patients receiving treatment during the pandemic. METHODS:We worked with 21 harm reduction and drug treatment programs in nine states and the District of Columbia from August 2020 to January 2021. Programs distributed study recruitment cards to clients. Clients responded to the survey by calling a study hotline and providing a unique study identification number. Our survey included detailed questions about use of SUD treatment prior to and since the COVID-19 pandemic. We identified settings where individuals received treatment and, for those treated for opioid use disorder, we examined use of medications for opioid use disorder. Individuals also reported whether they had received telehealth treatment and pandemic related treatment changes (e.g., more take-home methadone). We calculated p-values for differences pre and since COVID-19. RESULTS:We interviewed 587 individuals of whom 316 (53.8%) were in drug treatment both before and during the COVID-19 pandemic. Individuals in treatment reported substantial reductions in in-person service use since the start of the pandemic, including a 27 percentage point reduction (p<.001) in group counseling sessions and 28 percentage point reduction in mutual aid group participation (p<.001). By contrast, individuals reported a 21 percentage point increase in receipt of overdose education (p<.001). Most people receiving medications for opioid use disorder reported taking methadone and had high continuity of treatment (86.1% received methadone pre-COVID and 87.1% since-COVID, p=.71). Almost all reported taking advantage of new policy changes such as counseling by video/phone, increased take-home medication, or fewer urine drug screens. Overall, respondents reported relatively high satisfaction with their treatment and with telehealth adaptations (e.g., 80.2% reported "I'm able to get all the treatment that I need"). CONCLUSIONS:Accommodations to treatment made under the federal public health emergency appear to have sustained access to treatment in the early months of the pandemic. Since these changes are set to expire after the official public health emergency declaration, further action is needed to meet the ongoing need.

BACKGROUND:The endocannabinoid system has been implicated in physiological processes fundamental to pain, giving plausibility to the hypothesis that cannabis may be used as a substitute or complement to prescription opioids in the management of chronic pain. We examined the association of medical cannabis licensure with likelihood of prescription opioid receipt using administrative records. METHODS:This study linked registry information for medical cannabis licensure with records from the prescription drug monitoring program from April 1, 2016 to March 31, 2019 to create a population-based, retrospective cohort in Rhode Island. We examined within-person changes in receipt of any opioid prescription and receipt of an opioid prescription with a morphine equivalent dose of 50 mg or more, and of 90 mg or more. RESULTS:The sample included 5,296 participants with medical cannabis license. Medical cannabis licensure was not associated with the odds of filling any opioid prescription (OR: 0.99; 95% CI: 0.94-0.1.05) or the odds of filling a prescription with a morphine equivalent dose of 50 mg or more (OR: 0.93; 95% CI: 0.84-1.04) and 90 mg or more (OR: 0.99; 95% CI: 0.86-1.15). CONCLUSION/CONCLUSIONS:Medical cannabis licensure was not associated with subsequent cessation and reduction in prescription opioid use. Re-scheduling of cannabis will allow for the conduct of randomized controlled trials to determine the efficacy of medical cannabis as an alternative to prescription opioid use or a complement to the use of lower doses of prescription opioids in patients with chronic pain.

BACKGROUND:Tools are needed to aid clinicians in estimating their patients' risk of transitioning to long-term opioid use and to inform prescribing decisions. OBJECTIVE:The objective of this study was to develop and validate a model that predicts previously opioid-naive patients' risk of transitioning to long-term use. RESEARCH DESIGN/METHODS:This was a statewide population-based prognostic study. SUBJECTS/METHODS:Opioid-naive (no prescriptions in previous 2 y) patients aged 12 years old and above who received a pill-form opioid analgesic in 2016-2018 and whose prescriptions were registered in the California Prescription Drug Monitoring Program (PDMP). MEASURES/METHODS:A multiple logistic regression approach was used to construct a prediction model with long-term (ie, >90 d) opioid use as the outcome. Models were developed using 2016-2017 data and validated using 2018 data. Discrimination (c-statistic), calibration (calibration slope, intercept, and visual inspection of calibration plots), and clinical utility (decision curve analysis) were evaluated to assess performance. RESULTS:Development and validation cohorts included 7,175,885 and 2,788,837 opioid-naive patients with outcome rates of 5.0% and 4.7%, respectively. The model showed high discrimination (c-statistic: 0.904 for development, 0.913 for validation), was well-calibrated after intercept adjustment (intercept, -0.006; 95% confidence interval, -0.016 to 0.004; slope, 1.049; 95% confidence interval, 1.045-1.053), and had a net benefit over a wide range of probability thresholds. CONCLUSIONS:A model for the transition from opioid-naive status to long-term use had high discrimination and was well-calibrated. Given its high predictive performance, this model shows promise for future integration into PDMPs to aid clinicians in formulating opioid prescribing decisions at the point of care.