Faculty Bibliography

OBJECTIVES:The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature. METHODS:A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study. RESULTS:The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs. CONCLUSION:The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

BACKGROUND:The rapid growth of opioid abuse and related mortality across the United States has spurred the development of predictive models for the allocation of public health resources. These models should characterize heterogeneous growth across states using a drug epidemic framework that enables assessments of epidemic onset, rates of growth, and limited capacities for epidemic growth. METHODS:We used opioid overdose mortality data for 146 North and South Carolina counties from 2001 through 2014 to compare the retrodictive and predictive performance of a logistic growth model that parameterizes onsets, growth, and carrying capacity within a traditional Bayesian Poisson space-time model. RESULTS:In fitting the models to past data, the performance of the logistic growth model was superior to the standard Bayesian Poisson space-time model (deviance information criterion: 8088 vs. 8256), with reduced spatial and independent errors. Predictively, the logistic model more accurately estimated fatality rates 1, 2, and 3 years in the future (root mean squared error medians were lower for 95.7% of counties from 2012 to 2014). Capacity limits were higher in counties with greater population size, percent population age 45 to 64, and percent white population. Epidemic onset was associated with greater same-year and past-year incidence of overdose hospitalizations. CONCLUSION/CONCLUSIONS:Growth in annual rates of opioid fatalities was capacity limited, heterogeneous across counties, and spatially correlated, requiring spatial epidemic models for the accurate and reliable prediction of future outcomes related to opioid abuse. Indicators of risk are identifiable and can be used to predict future mortality outcomes.

Coronavirus disease 2019 (COVID-19) will have a lasting impact on public health. In addition to the direct effects of COVID-19 infection, physical distancing and quarantine interventions have indirect effects on health. While necessary, physical distancing interventions to control the spread of COVID-19 could have multiple impacts on people living with opioid use disorder, including impacts on mental health that lead to greater substance use, the availability of drug supply, the ways that people use drugs, treatment-seeking behaviors, and retention in care. The degree to which COVID-19 will impact the opioid epidemic and through which of the possible mechanisms that we discuss is important to monitor. We employed simulation modeling to demonstrate the potential impact of physical distancing on overdose mortality.

BACKGROUND:In 2016, California updated its prescription drug monitoring program (PDMP), adding two key features: automated proactive reports to prescribers and mandatory registration for prescribers and pharmacists. The effects of these changes on prescribing patterns have not yet been examined. We aimed to evaluate the joint effect of these two PDMP features on county-level prescribing practices in California. METHODS:Using county-level quarterly data from 2012 to 2017, we estimated the absolute change associated with the implementation of these two PDMP features in seven prescribing indicators in California versus a control group comprising counties in Florida and Washington: opioid prescription rate per 1000 residents; patients' mean daily opioid dosage in milligrams of morphine equivalents[MME]; prescribers' mean daily MME prescribed; prescribers' mean number of opioid prescriptions per day; percentage of patients getting >90 MME/day; percentage of days with overlapping prescriptions for opioids and benzodiazepines; multiple opioid provider episodes per 100,000 residents. RESULTS:Proactive reports and mandatory registration were associated with a 7.7 MME decrease in patients' mean daily opioid dose (95 %CI: -11.4, -2.9); a 1.8 decrease in the percentage of patients prescribed high-dose opioids (95 %CI: -2.3, -0.9); and a 6.3 MME decrease in prescribers' mean daily dose prescribed (95 %CI: -10.0, -1.3). CONCLUSIONS:California's implementation of these two PDMP features was associated with decreases in the total quantity of opioid MMEs prescribed, and indicators of patients prescribed high-dose opioids compared to states that had PDMP's without these features. Rates of opioid prescribing and other high-risk prescribing patterns remained unchanged.

BACKGROUND AND AIMS/OBJECTIVE:Uruguay and Chile have the highest levels of marijuana use in Latin America and experienced consistent increases over the last 2 decades. We aim to calculate separate age-period-cohort (APC) effects for past-year marijuana use in Uruguay and Chile, which have similar epidemiologic and demographic profiles, but diverging paths in cannabis regulation. DESIGN/METHODS:Age, period and cohort study in which period and cohort effects were estimated as first derivative deviations from their linear age trend, separately by country and gender. SETTING/METHODS:Uruguay and Chile. PARTICIPANTS/METHODS:General population between 15 and 64 years of age. MEASUREMENTS/METHODS:Past-year marijuana use from household surveys with five repeated cross-sections between 2001-2018 in Uruguay (median n=4,616) and 13 between 1994-2018 in Chile (median n=15,895). FINDINGS/RESULTS:Marijuana use prevalence in both countries peaked at 20-24 years of age and increased consistently across calendar years. Period effects were strong and positive, indicating that increases in use were evident across age groups. Relative to 2006 (reference year), Chilean period effects were about 48% lower in 1994 and about four times higher in 2018; in Uruguay, these effects were about 56% lower in 2001 and almost quadrupled in 2018. We observed non-linear cohort effects in Chile, and similar patterns in Uruguay for the overall sample and women. In both countries, marijuana use increased for cohorts born between the mid-1970s and early 1990s even in the context of rising period effects. Prevalence was consistently larger for men, but period increases were stronger in women. CONCLUSIONS:Age-period-cohort effects on past-year marijuana use appear to have been similar in Chile and Uruguay, decreasing with age and increasing over time at heterogeneous growth rates depending on gender and cohort. Current levels of marijuana use, including age and gender disparities, seem to be associated with recent common historical events in these two countries.

BACKGROUND AND AIMS/OBJECTIVE:Medical marijuana law (MML) enactment in the US has been associated with increased cannabis use but lower traffic fatality rates. We assessed the possible association of MML and individual-level driving under the influence of cannabis (DUIC), and also under the influence of alcohol (DUIA). DESIGN AND SETTING/METHODS:Three cross-sectional U.S. adult surveys: The National Longitudinal Alcohol Epidemiologic Survey (NLAES; 1991-1992), the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; 2001-2002), and the NESARC-III (2012-2013). PARTICIPANTS/METHODS:The total n was 118,497: 41,764, 41,184 and 35,549 from NLAES, NESARC, and NESARC-III. MEASUREMENTS/METHODS:Across the three surveys, similar questions in the Alcohol Use Disorder and Associated Disabilities Interview Schedule assessed DUIC and DUIA. Ever-MML states enacted MML between 1991-1992 and 2012-2013 (overall period). Early-MML states enacted MML between 1991-1992 and 2001-2002 (early period). Late-MML states enacted MML between 2001-2002 and 2012-2013 (late period). MML effects on change in DUIC and DUIA prevalence were estimated using a difference-in-differences specification to compare changes in MML and other states. FINDINGS/RESULTS:From 1991-1992 to 2012-2013, DUIC prevalence nearly doubled (from 1.02% to 1.92%), increasing more in states that enacted MML than other states (difference-in-differences [DiD]=0.59%; 95%CI=0.06%-1.12%). Most change in DUIC prevalence occurred between 2001-2002 and 2012-2013. DUIC prevalence increased more in states that enacted MML 2001-2002 to 2012-2013 than in never-MML states (DiD=0.77%; 95%CI= -0.05%-1.59%), and in two early-MML states, California (DiD=0.82; 95%CI=0.06-1.59) and Colorado (DiD=1.32; 95%CI=0.11-2.53). In contrast, DUIA prevalence appeared unrelated to MML enactment. CONCLUSIONS:Medical marijuana law enactment in US states appears to have been associated with increased prevalence of driving under the influence of cannabis, but not alcohol.

PURPOSE/OBJECTIVE:Adolescents with opioid use disorder are less likely than adults to receive medications for opioid use disorder (MOUD), yet we know little about facilities that provide addiction treatment for adolescents. We sought to describe adolescent-serving addiction treatment facilities in the U.S. and examine associations between facility characteristics and offering MOUD, leading to informed recommendations to improve treatment access. METHODS:This cross-sectional study used the 2017 National Survey of Substance Abuse Treatment Services. Facilities were classified by whether they offered a specialized adolescent program. Covariates included facility ownership, hospital affiliation, insurance/payments, government grants, accreditation/licensure, location, levels of care, and provision of MOUD. Descriptive statistics and logistic regression compared adolescent-serving versus adult-focused facilities and identified characteristics associated with offering maintenance MOUD. RESULTS:Among 13,585 addiction treatment facilities in the U.S., 3,537 (26.0%) offered adolescent programs. Adolescent-serving facilities were half as likely to offer maintenance MOUD as adult-focused facilities (odds ratio, .53; 95% confidence interval, .49-.58), which was offered at 23.1% (816) of adolescent-serving versus 35.9% (3,612) of adult-focused facilities. Among adolescent-serving facilities, characteristics associated with increased unadjusted odds of offering maintenance MOUD were nonprofit status, hospital affiliation, accepting insurance (particularly, private insurance), accreditation, Northeastern location, or offering inpatient services. CONCLUSIONS:The one-quarter of U.S. addiction treatment facilities that serve adolescents are half as likely to provide MOUD as adult-focused facilities, which may explain why adolescents are less likely than adults to receive MOUD. Strategies to increase adolescent access to MOUD may consider insurance reforms/incentives, facility accreditation, and geographically targeted funding.

INTRODUCTION/BACKGROUND:Special populations, including veterans, pregnant and postpartum women, and adolescents, benefit from opioid use disorder treatment tailored to their specific needs, but access to such services is poorly described. This study identifies the availability of opioid use disorder treatment facilities that use medications and have special programming and contextualizes facilities amid counties' opioid-related overdose mortality. METHODS:Data were compiled on 15,945 U.S. treatment facilities using medications for opioid use disorder listed in the Behavioral Health Services Treatment Locator in 2018. Facilities with programs tailored to special populations (veterans, pregnant and postpartum women, and adolescents) were identified and geocoded. Counties with such facilities were characterized. Cold spots (county clusters with poor treatment availability) were identified using Getis-Ord Gi* statistics. Data were extracted in October 2018 and analyzed from October 2018 to May 2019. RESULTS:Of all 3,142 U.S. counties, 1,889 (60.1%) had opioid use disorder treatment facilities. Facilities with tailored programs for veterans, pregnant and postpartum women, and adolescents were located in 701 (22.3%), 918 (29.2%), and 1,062 (33.8%) of the counties, respectively. Specific medications provided for opioid use disorder varied, with only a minority of facilities offering methadone (among facilities with tailored programs for veterans, 6.0%; pregnant and postpartum women, 13.2%; adolescents, 1.3%). Many counties reporting opioid-related overdose deaths lacked programs for special populations (veterans, 72.6%; pregnant and postpartum women, 54.8%; adolescents, 30.6%). Cold spots were located throughout the Midwest, U.S. Southeast, and portions of Texas. CONCLUSIONS:Facilities using medications for opioid use disorder with tailored programs for veterans, pregnant and postpartum women, and adolescents are limited. There is a need for improved access to evidence-based programs that address the unique treatment needs of special populations.

SARS-CoV-2, the virus that causes COVID-19, has changed the world as we know it, and continues to do so. How COVID-19 affects people who use drugs, the environments in which they live, and capacities of response, warrants immediate attention. This special issue begins to map how COVID-19 is altering the health of people who use drugs, including in relation to patterns of drug use, service responses, harms that may relate to drug use, interventions to reduce risk of harms, COVID-19 health, and drug policies. We emphasise the need to envisage COVID-19 and its effects as a matter of intersecting 'complex adaptive systems': that is, the impacts of COVID-19 extend beyond the virus and related illness conditions to encompass multiple social, cultural, economic, policy and political effects; and these affect the health of people who use drugs directly as well as indirectly by altering the risk and enabling environments in which they live. We synthesize emergent evidence on the impact of COVID-19 on the health of people who use drugs. A key concern we identify is how to sustain policy and service delivery improvements prompted by COVID-19. We need to maintain an ethos of emergent adaptation and experimentation towards the creation of safer environments in relation to the health of people who use drugs.