Faculty Bibliography

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: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.

Stigma is a known barrier to treating substance use disorders and dramatically diminishes the quality of life of people who use drugs (PWUD) nonmedically. Stigma against PWUD may be especially pronounced in rural areas due to their decreased anonymity and residents' limited access, or resistance, to "neutralizing" information on factors associated with drug use. Stigma often manifests in the attitudes of professionals whom stigmatized individuals regularly interact with and often materially impact. We analyzed interviews conducted between July 2018 and February 2019 with professional stakeholders in rural southern Illinois who interact with PWUD, specifically those who use opioids nonmedically or who inject drugs (n = 30). We further analyzed interview data from a complementary PWUD sample (n = 22). Interviews addressed perspectives around nonmedical drug use and treatment/harm reduction, with analysis centered around the Framework Integrating Normative Influences on Stigma and its focus on micro, meso and macro level stigmatization processes. Stakeholder participants included professionals from local law enforcement, courts, healthcare organizations, emergency management services, and faith-based and social services organizations. Most stakeholders, particularly law enforcement, negatively perceived PWUD and nonmedical drug use in general, questioned the character, agency and extrinsic value of PWUD, and used labels (e.g. "addict," "abuser," etc.) that may be regarded as stigmatizing. Further, most respondents, including PWUD, characterized their communities as largely unaware or dismissive of the bio-medical and sociocultural explanations for opioid use, drug injection and towards harm reduction services (e.g., syringe exchanges) and naloxone, which were frequently framed as undeserved usages of taxpayer funds. In conclusion, rural stigma against PWUD manifested and was framed as a substantial issue, notably activating at micro, meso and macro levels. Stigma prevention efforts in these communities should aim to improve public knowledge on the intricate factors contributing to opioid use and drug injection and harm reduction programming's moral and fiscal value.

Introduction: Although a substantial body of research documents a relationship between traumatic stress in childhood and the initiation of substance use later in the life course, only limited research has examined potential linkages between adverse childhood experiences (ACEs) and the initiation of non-medical prescription opioid use and other opioid use behaviors. The present study contributes to this growing body of work by investigating the association of childhood trauma with early initiation of a series of opioid use behaviors. Methods: New York City young adults (n = 539) ages 18-29 who reported non-medical use of prescription opioids or heroin use in the past 30 days were recruited using Respondent-Driven Sampling in 2014-16. Ten ACEs were assessed via self-report with the ACE Questionnaire. Associations between number of ACEs and self-reported ages of initiating seven opioid use behaviors (e.g., non-medical prescription opioid use, heroin use, heroin injection) were estimated with multivariable logistic regression. Results: Eighty nine percent of participants reported at least one ACE, and 46% reported four or more ACEs, a well-supported threshold indicating elevated risk for negative health consequences. Every increase of one trauma was associated with a 12-23% increase in odds of early initiation across the seven opioid use behaviors. Findings also document that the mean age at initiation increased with increasing risk severity across the behaviors, contributing to evidence of a trajectory from opioid pill misuse to opioid injection. Discussion: Increasing number of childhood traumas was associated with increased odds of earlier initiation of multiple opioid misuse behaviors. In light of prior research linking earlier initiation of substance use with increased substance use severity, present findings suggest the importance of ACEs as individual-level determinants of increased opioid use severity. Efforts to prevent onset and escalation of opioid use among at-risk youth may benefit from trauma prevention programs and trauma-focused screening and treatment, as well as increased attention to ameliorating upstream socio-structural drivers of childhood trauma.

This paper presents a framework that may help explain the interactions among capitalism, infectious disease, and environmental change in the world and in the Mediterranean region. Capitalism and the longevity of national governments require continued investment and economic growth. This disrupts the environment, most urgently in terms of climate change, which is expected to single out the Mediterranean region for droughts and heat and to lead to mass migrations of people and animals. Capitalism also disrupts the habitats of fauna, and creates factory farming and markets for the sale of animals from local ecologies that are being "opened up" for profitable use-all of which leads to increased interaction between potential vectors of infectious diseases and humans. Capitalist globalization has encouraged widespread international and other travel of people and goods, which can carry infectious people or animal vectors across long distances very rapidly. In sum, capitalism requires development, which creates environmental and infectious disease crises. Solving this problem will require profound political and economic change. In the immediate term, public health and medical institutions need to be protected against the pressures of austerity.

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.

Importance/UNASSIGNED:Responding to the opioid crisis requires tools to identify individuals at risk of overdose. Given the expansion of illicit opioid deaths, it is essential to consider risk factors across multiple service systems. Objective/UNASSIGNED:To develop a predictive risk model to identify opioid overdose using linked clinical and criminal justice data. Design, Setting, and Participants/UNASSIGNED:A cross-sectional sample was created using 2015 data from 4 Maryland databases: all-payer hospital discharges, the prescription drug monitoring program (PDMP), public-sector specialty behavioral treatment, and criminal justice records for property or drug-associated offenses. Maryland adults aged 18 to 80 years with records in any of 4 databases were included, excluding individuals who died in 2015 or had a non-Maryland zip code. Logistic regression models were estimated separately for risk of fatal and nonfatal opioid overdose in 2016. Model performance was assessed using bootstrapping. Data analysis took place from February 2018 to November 2019. Exposures/UNASSIGNED:Controlled substance prescription fills and hospital, specialty behavioral health, or criminal justice encounters. Main Outcomes and Measures/UNASSIGNED:Fatal opioid overdose defined by the state medical examiner and 1 or more nonfatal overdoses treated in Maryland hospitals during 2016. Results/UNASSIGNED:There were 2 294 707 total individuals in the sample, of whom 42.3% were male (n = 970 019) and 53.0% were younger than 50 years (647 083 [28.2%] aged 18-34 years and 568 160 [24.8%] aged 35-49 years). In 2016, 1204 individuals (0.05%) in the sample experienced fatal opioid overdose and 8430 (0.37%) experienced nonfatal opioid overdose. In adjusted analysis, the factors mostly strongly associated with fatal overdose were male sex (odds ratio [OR], 2.40 [95% CI, 2.08-2.76]), diagnosis of opioid use disorder in a hospital (OR, 2.93 [95% CI, 2.17-3.80]), release from prison in 2015 (OR, 4.23 [95% CI, 2.10-7.11]), and receiving opioid addiction treatment with medication (OR, 2.81 [95% CI, 2.20-3.86]). Similar associations were found for nonfatal overdose. The area under the curve for fatal overdose was 0.82 for a model with hospital variables, 0.86 for a model with both PDMP and hospital variables, and 0.89 for a model that further added behavioral health and criminal justice variables. For nonfatal overdose, the area under the curve using all variables was 0.85. Conclusions and Relevance/UNASSIGNED:In this analysis, fatal and nonfatal opioid overdose could be accurately predicted with linked administrative databases. Hospital encounter data had higher predictive utility than PDMP data. Model performance was meaningfully improved by adding PDMP records. Predictive models using linked databases can be used to target large-scale public health programs.