Faculty Bibliography

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.

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:As of July 2018, 45 United States (US) states and the District of Columbia have enacted an overdose Good Samaritan law (GSL). These laws, which provide limited criminal immunity to individuals who request assistance during an overdose, may be of importance in the current wave of the overdose epidemic, which is driven primarily by illicit opioids including heroin and fentanyl. There are substantial differences in the structures of states' GSL laws which may impact their effectiveness. This study compared GSLs which have legal provisions protecting from arrest and laws which have more limited protections. METHODS:Using national county-level overdose mortality data from 3109 US counties, we examined the association of enactment of GSLs with protection from arrest and GSLs with more limited protections with subsequent overdose mortality between 2013 and 2018. Since GSLs are often enacted in conjunction with Naloxone Access Laws (NAL), we examined the effect of GSLs separately and in conjunction with NAL. We conducted these analyses using hierarchical Bayesian spatiotemporal Poisson models. RESULTS:GSLs with protections against arrest enactment in conjunction with a NAL were associated with 7% lower rates of all overdose deaths (rate ratio (RR): 0.93% Credible Interval (CI): 0.89-0.97), 10% lower rates in opioid overdose deaths (RR: 0.90; CI: 0.85-0.95) and 11% lower rates of heroin/synthetic overdose mortality (RR: 0.89; CI: 0.82-0.96) two years after enactment, compared to rates in states without these laws. Significant reductions in overdose mortality were not seen for GSLs with protections for charge or prosecution. CONCLUSION/CONCLUSIONS:GSLs with more expansive legal protections combined with a NAL, were associated with lower rates of overdose deaths, although these risk reductions take time to manifest. Policy makers should consider enacting and implementing more expansive GSLs with arrest protections to increase the likelihood people will contact emergency services in the event of an overdose.

Importance/UNASSIGNED:Prescription opioids are involved in more than half of opioid overdoses among younger persons. Understanding opioid prescribing practices is essential for developing appropriate interventions for this population. Objective/UNASSIGNED:To examine temporal trends in opioid prescribing practices in children, adolescents, and younger adults in the US from 2006 to 2018. Design, Setting, and Participants/UNASSIGNED:A population-based, cross-sectional analysis of opioid prescription data was conducted from January 1, 2006, to December 31, 2018. Longitudinal data on retail pharmacy-dispensed opioids for patients younger than 25 years were used in the analysis. Data analysis was performed from December 26, 2019, to July 8, 2020. Main Outcomes and Measures/UNASSIGNED:Opioid dispensing rate, mean amount of opioid dispensed in morphine milligram equivalents (MME) per day (individuals aged 15-24 years) or MME per kilogram per day (age <15 years), duration of prescription (mean, short [≤3 days], and long [≥30 days] duration), high-dosage prescriptions, and extended-release or long-acting (ER/LA) formulation prescriptions. Outcomes were calculated for age groups: 0 to 5, 6 to 9, 10 to 14, 15 to 19, and 20 to 24 years. Joinpoint regression was used to examine opioid prescribing trends. Results/UNASSIGNED:From 2006 to 2018, the opioid dispensing rate for patients younger than 25 years decreased from 14.28 to 6.45, with an annual decrease of 15.15% (95% CI, -17.26% to -12.99%) from 2013 to 2018. The mean amount of opioids dispensed and rates of short-duration and high-dosage prescriptions decreased for all age groups older than 5 years, with the largest decreases in individuals aged 15 to 24 years. Mean duration per prescription increased initially for all ages, but then decreased for individuals aged 10 years or older. The duration remained longer than 5 days across all ages. The rate of long-duration prescriptions increased for all age groups younger than 15 years and initially increased, but then decreased after 2014 for individuals aged 15 to 24 years. For children aged 0 to 5 years dispensed an opioid, annual increases from 2011 to 2014 were noted for the mean amount of opioids dispensed (annual percent change [APC], 10.58%; 95% CI, 1.77% to 20.16%) and rates of long-duration (APC, 30.42%; 95% CI, 14.13% to 49.03%), high-dosage (APC, 31.27%; 95% CI, 16.81% to 47.53%), and ER/LA formulation (APC, 27.86%; 95% CI, 12.04% to 45.91%) prescriptions, although the mean amount dispensed and rate of high-dosage prescriptions decreased from 2014 to 2018. Conclusions and Relevance/UNASSIGNED:These findings suggest that opioid dispensing rates decreased for patients younger than 25 years, with decreasing rates of high-dosage and long-duration prescriptions for adolescents and younger adults. However, opioids remain readily dispensed, and possible high-risk prescribing practices appear to be common, especially in younger children.

BACKGROUND:Maintenance treatments with medications for opioid use disorder (MOUD) are highly effective at reducing overdose risk while patients remain in care. However, few patients initiate medication and retention remains a critical challenge across settings. Much remains to be learned about individual and structural factors that influence successful retention, especially among populations dispensed MOUD in outpatient settings. METHODS:We examined individual and structural characteristics associated with MOUD treatment retention among a national sample of adults seeking MOUD treatment in outpatient substance use treatment settings using the 2017 Treatment Episode Dataset-Discharges (TEDS-D). The study assessed predictors of retention in MOUD using multivariate logistic regression and accelerated time failure models. RESULTS:Of 130,300 episodes of MOUD treatment in outpatient settings, 36% involved a duration of care greater than six months. The strongest risk factors for treatment discontinuation by six months included being of younger age, ages 18-29 ((OR):0.52 [95%CI:0.50-0.54]) or 30-39 (OR:0.57 [95%CI:0.55-0.59); experiencing homelessness (OR: 0.70 [95%CI:0.66-0.73]); co-using methamphetamine (OR:0.48 [95%CI:0.45-0.51]); and being referred to treatment by a criminal justice source (OR:0.55 [95%CI:0.52-0.59) or by a school, employer, or community source (OR:0.71 [95%CI:0.66-0.76). CONCLUSIONS:Improving retention in treatment is a pivotal stage in the OUD cascade of care and is critical to reducing overdose deaths. Efforts should prioritize interventions to improve retention among patients who are both prescribed and dispended MOUD, especially youth, people experiencing homelessness, polysubstance users, and people referred to care by the justice system who have especially short stays in care.

BACKGROUND:The rise in opioid-related mortality and opioid-related emergency department (ED) visits has stimulated research on whether broader economic declines, such as the Great Recession, affect opioid-related morbidity. We examine in New York City whether one measure of morbidity-opioid-related ED visits-responded acutely to the large negative "shock" of the Great Recession. METHODS:Data comprise outpatient "treat and release" opioid-related ED visits in New York City for the 72 months spanning January 2006 to December 2011, taken from the Statewide Emergency Department Database (n = 150,246). We modeled the monthly incidence of opioid-related ED visits using Autoregressive, Integrated, Moving Average (ARIMA) time-series methods to control for patterning in ED visits before examining its potential association with the economic shock of the Great Recession. RESULTS:New York City shows a mean of 1761 outpatient ED visits per month for opioid dependence and abuse. Unexpectedly large drops in employment coincide with fewer than expected opioid dependence and abuse ED visits in that same month. The result (coefficient = 0.046, 95% Confidence Interval [CI]: 0.002, 0.090) represents a 0.8% drop in overall incidence of opioid dependence and abuse ED visits during the Great Recession. We, however, observe no association between the Great Recession and ED visits for prescription opioid overdose or heroin overdose, or with inpatient ED visits for opioid dependence and abuse. CONCLUSIONS:Findings, if replicated, indicate distinct short-term reductions in opioid-related morbidity following the Great Recession. This result diverges from previous findings of increased opioid use following extended economic downturns.

Opioid-related mortality increased by nearly 400% between 2000 and 2018. In response, federal, state, and local governments have enacted a heterogeneous collection of opioid-related policies in an effort to reverse the opioid crisis, producing a policy landscape that is both complex and dynamic. Correspondingly, there has been a rise in opioid-policy related evaluation studies, as policymakers and other stakeholders seek to understand which policies are most effective. In this paper, we provide an overview of methodological challenges facing opioid policy researchers when evaluating the effects of opioid policies using observational data, as well as some potential solutions to those challenges. In particular, we discuss the following key challenges: (1) Obtaining high-quality opioid policy data; (2) Appropriately operationalizing and specifying opioid policies; (3) Obtaining high-quality opioid outcome data; (4) Addressing confounding due to systematic differences between policy and non-policy states; (5) Identifying heterogeneous policy effects across states, population subgroups, and time; (6) Disentangling effects of concurrent policies; and (7) Overcoming limited statistical power to detect policy effects afforded by commonly-used methods. We discuss each of these challenges and propose some ways forward to address them. Increasing the methodological rigor of opioid evaluation studies is imperative to identifying and implementing opioid policies that are most effective at reducing opioid-related harms.

Big Events are periods during which abnormal large-scale events like war, economic collapse, revolts, or pandemics disrupt daily life and expectations about the future. They can lead to rapid change in health-related norms, beliefs, social networks and behavioural practices. The world is undergoing such Big Events through the interaction of COVID-19, a large economic downturn, massive social unrest in many countries, and ever-worsening effects of global climate change. Previous research, mainly on HIV/AIDS, suggests that the health effects of Big Events can be profound, but are contingent: Sometimes Big Events led to enormous outbreaks of HIV and associated diseases and conditions such as injection drug use, sex trading, and tuberculosis, but in other circumstances, Big Events did not do so. This paper discusses and presents hypotheses about pathways through which the current Big Events might lead to better or worse short and long term outcomes for various health conditions and diseases; considers how pre-existing societal conditions and changing 'pathway' variables can influence the impact of Big Events; discusses how to measure these pathways; and suggests ways in which research and surveillance might be conducted to improve human capacity to prevent or mitigate the effects of Big Events on human health.

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.