Faculty Bibliography

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.

Pain management clinic (PMC) laws were enacted by 12 states to promote appropriate opioid prescribing, but their impact is inadequately understood. We analyzed county-level opioid overdose deaths (National Vital Statistics System) and patients filling long-duration (≥30 day) or high-dose (≥90 morphine milligram equivalents) opioid prescriptions (IQVIA, Inc) in the United States from 2010-2018. We fit Besag-York-Mollié spatiotemporal models to estimate annual relative rates (RR) of overdose and prevalence ratios (PR) of high-risk prescribing associated with any PMC law and three provisions: payment restrictions, site inspections, and criminal penalties. Laws with criminal penalties were significantly associated with reduced PRs of long-duration and high-dose opioid prescriptions (adjusted PR: 0.82, 95% credible interval (CrI) 0.92-0.83, and 0.73, and 0.73, 0.74 respectively), and reduced RRs of total and natural/semi-synthetic opioid overdoses (adjusted RR: 0.86, 95% CrI: 0.80, 0.92; and 0.84, and 0.77, 0.92, respectively). Conversely, PMC laws were associated with increased relative rates of synthetic opioid and heroin overdose deaths, especially criminal penalties (adjusted RR: 1.83, 95% CrI: 1.59, 2.11; and 2.59, and 2.22, 3.02, respectively). Findings suggest laws with criminal penalties were associated with intended reductions in high-risk opioid prescribing and some opioid overdoses, but raise concerns regarding unintended consequences on heroin/synthetic overdoses.

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.

BACKGROUND:Limiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms. OBJECTIVE:To identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription. DESIGN/METHODS:Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017. METHODS AND MAIN MEASURES/UNASSIGNED:Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic). KEY RESULTS/RESULTS:Among 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents. CONCLUSIONS:Data routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

BACKGROUND:Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multi-stage, machine learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. METHODS:Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. RESULTS:PDMP patient data access provisions most consistently predicted high dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. CONCLUSIONS:Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing, and to examine potential interactions between PDMP access and pain management clinic provisions.

The Centers for Disease Control and Prevention's 2016 Guideline for Prescribing Opioids for Chronic Pain aimed to reduce unsafe opioid prescribing. It is unknown whether the guideline influenced prescribing in the target population: patients with chronic, noncancer pain, who may be at particular risk for opioid-related harms. To study this question, we used 2014-18 data from a commercial claims database to examine associations between the release of the guideline and opioid dispensing in a national cohort of more than 450,000 patients with four common chronic pain diagnoses. We also examined whether any reductions associated with the guideline were larger for diagnoses for which there existed stronger expert consensus against opioid prescribing. Overall, the guideline was associated with substantial reductions in dispensing opioids, including a reduction in patients' rate of receiving at least one opioid prescription by approximately 20 percentage points by December 2018 compared with the counterfactual, no-guideline scenario. However, the reductions in dispensing did not vary by the strength of expert consensus against opioid prescribing. These findings suggest that although voluntary guidelines can drive changes in prescribing, questions remain about how clinicians are tailoring opioid reductions to best benefit patients.

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:Variation in drug policies, norms, and substance use over time and across countries may affect the normative sequences of adolescent substance use initiation. We estimated relative and absolute time-varying associations between prior alcohol and tobacco use and adolescent marijuana initiation in Argentina and Chile. Relative measures quantify the magnitude of the associations, whereas absolute measures quantify excess risk. METHODS:We analyzed repeated, cross-sectional survey data from the National Surveys on Drug Use Among Secondary School Students in Argentina (2001-2014) and Chile (2001-2017). Participants included 8th, 10th, and 12th grade students (N = 680,156). Linear regression models described trends over time in the average age of first use of alcohol, tobacco, and marijuana. Logistic regression models were used to estimate time-varying risk ratios and risk differences of the associations between prior alcohol and tobacco use and current-year marijuana initiation. RESULTS:Average age of marijuana initiation increased and then decreased in Argentina and declined in Chile. In both countries, the relative associations between prior tobacco use and marijuana initiation weakened amid declining rates of tobacco use; e.g., in Argentina, the risk ratio was 19.9 (95% CI: 9.0-30.8) in 2001 and 11.6 (95% CI: 9.0-13.2) in 2014. The relative association between prior alcohol use and marijuana initiation weakened Chile, but not in Argentina. On the contrary, risk differences (RD) increased substantially across both relationships and countries, e.g., in Argentina, the RD for tobacco was 3% (95% CI: 0.02-0.03) in 2001 and 12% (95% CI: 0.11-0.13) in 2014. CONCLUSION/CONCLUSIONS:Diverging trends in risk ratios and risk differences highlight the utility of examining multiple measures of association. Variation in the strength of the associations over time and place suggests the influence of environmental factors. Increasing risk differences indicate alcohol and tobacco use may be important targets for interventions to reduce adolescent marijuana use.