Faculty Bibliography

BACKGROUND:American Indians and Alaska Natives (AI/ANs) are disproportionately affected by the opioid overdose crisis. Treatment with medications for opioid use disorder (MOUD) can significantly reduce overdose risk, but no national studies to date have reported on the extent to which AI/ANs access these treatments overall and in relation to other groups. METHODS:The current study used two national databases - the 2018 National Survey on Substance Abuse Treatment Services and the 2017 Treatment Episode Dataset - to estimate the extent to which MOUD is available and used among AI/ANs across the U.S. RESULTS:We found that facilities serving AI/ANs (N = 1,532) offered some MOUD at similar rates as other facilities (N = 13,277) (39.6 vs. 40.6 %, p = 0.435) but were less likely to offer the standard of care with buprenorphine or methadone maintenance (22.4 % vs. 27.6 %, p < 0.001). AI/AN clients in specialty treatment (N = 8,136) exhibited slightly higher MOUD use (40.0 % vs. 38.6 %, p = 0.009) relative to other race groups (N = 673,938). AI/AN clients were also more likely to exhibit greater prescription opioid use and methamphetamine co-use relative to other groups. AI/AN clients in the South (aOR:0.23[95 %CI: 0.19-0.28] or referred by criminal justice sources (aOR:0.13[95 %CI: 0.11-0.16] were least likely to receive MOUD. CONCLUSIONS:We conclude that most AI/ANs in specialty treatment do not receive medications for opioid use disorder, and that rates of MOUD use are similar to those of other race groups. Efforts to expand MOUD among AI/ANs that are localized and cater to unique characteristics of this population are gravely needed.

BACKGROUND:Evidence suggests that individuals with history of substance use disorder (SUD) are at increased risk of COVID-19, but little is known about relationships between SUDs, overdose and COVID-19 severity and mortality. This study investigated risks of severe COVID-19 among patients with SUDs. METHODS:We conducted a retrospective review of data from a hospital system in New York City. Patient records from 1 January to 26 October 2020 were included. We assessed positive COVID-19 tests, hospitalizations, intensive care unit (ICU) admissions and death. Descriptive statistics and bivariable analyses compared the prevalence of COVID-19 by baseline characteristics. Logistic regression estimated unadjusted and sex-, age-, race- and comorbidity-adjusted odds ratios (AORs) for associations between SUD history, overdose history and outcomes. RESULTS:Of patients tested for COVID-19 (n = 188 653), 2.7% (n = 5107) had any history of SUD. Associations with hospitalization [AORs (95% confidence interval)] ranged from 1.78 (0.85-3.74) for cocaine use disorder (COUD) to 6.68 (4.33-10.33) for alcohol use disorder. Associations with ICU admission ranged from 0.57 (0.17-1.93) for COUD to 5.00 (3.02-8.30) for overdose. Associations with death ranged from 0.64 (0.14-2.84) for COUD to 3.03 (1.70-5.43) for overdose. DISCUSSION/CONCLUSIONS:Patients with histories of SUD and drug overdose may be at elevated risk of adverse COVID-19 outcomes.

More than 750,000 people in the United States died from an overdose between 1999 and 2018; two-thirds of those deaths involved an opioid. In this review, we present trends in opioid overdose rates during this period and discuss how the proliferation of opioid prescribing to treat chronic pain, changes in the heroin and illegally manufactured opioid synthetics markets, and social factors, including deindustrialization and concentrated poverty, contributed to the rise of the overdose epidemic. We also examine how current policies implemented to address the overdose epidemic may have contributed to reducing prescription opioid overdoses but increased overdoses involving illegal opioids. Finally, we identify new directions for research to understand the causes and solutions to this critical public health problem, including research on heterogeneous policy effects across social groups, effective approaches to reduce overdoses of illegal opioids, and the role of social contexts in shaping policy implementation and impact. Expected final online publication date for the Annual Review of Public Health, Volume 42 is April 1, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

OBJECTIVES/OBJECTIVE:To examine patient characteristics and outcomes among opioid use disorder patients enrolled in low-threshold buprenorphine treatment during the COVID-19 pandemic. METHODS:This paper describes the adaptation of the Project Connections (PC) program, a low-threshold buprenorphine program in Baltimore, in response to the COVID-19 pandemic. This paper examines patient characteristics and initial outcomes of patients served during a rapid protocol shift to telehealth that allowed buprenorphine initiation without an in-person encounter following a state-mandated stay-at-home order. Patient characteristics were compared to a subsample of patients enrolled in the program before the COVID-19 pandemic. RESULTS:In March 2020, there was a sharp increase in new enrollments to the PC program. A total of 143 patients completed an intake assessment between March and May 2020 and 140 began treatment with buprenorphine/naloxone. Those who completed an intake assessment were primarily male (68.5%), Black (83.2%), had a mean age of 43.2 years (SD = 11.7), and reported a mean of 17.0 years of opioid use (SD = 12.9). The majority of patients were unemployed (72.7%) and reported previous criminal justice involvement (69.2%). Of those who completed an intake assessment, 96.5% returned for a second visit. Among those for whom 30-day retention data was available (n = 113), 63.7% were engaged for 30 days or longer. CONCLUSIONS:The PC program illustrates that offering on-demand, flexible treatment is an opportunity to increase opioid use disorder treatment access, even during a public health emergency that disrupted access to services. Relaxation of buprenorphine telehealth regulations allowed for flexibility in treatment and benefits vulnerable populations.

The opioid/overdose crisis in the United States and Canada has claimed hundreds of thousands of lives and has become a major field for research and interventions. It has embroiled pharmaceutical companies in lawsuits and possible bankruptcy filings. Effective interventions and policies toward this and future drug-related outbreaks may be improved by understanding the sociostructural roots of this outbreak. Much of the literature on roots of the opioid/overdose outbreak focuses on (1) the actions of pharmaceutical companies in inappropriately promoting the use of prescription opioids; (2) "deaths of despair" based on the deindustrialization of much of rural and urban Canada and the United States, and on the related marginalization and demoralization of those facing lifetimes of joblessness or precarious employment in poorly paid, often dangerous work; and (3) increase in occupationally-induced pain and injuries in the population. All three of these roots of the crisis-pharmaceutical misconduct and unethical marketing practices, despair based on deindustrialization and increased occupational pain-can be traced back, in part, to what has been called the "one-sided class war" that became prominent in the 1970s, became institutionalized as neo-liberalism in and since the 1980s, and may now be beginning to be challenged. We describe this one-sided class war, and how processes it sparked enabled pharmaceutical corporations in their misconduct, nurtured individualistic ideologies that fed into despair and drug use, weakened institutions that created social support in communities, and reduced barriers against injuries and other occupational pain at workplaces by reducing unionization, weakening surviving unions, and weakening the enforcement of rules about workplace safety and health. We then briefly discuss the implications of this analysis for programs and policies to mitigate or reverse the opioid/overdose outbreak.

BACKGROUND:Despite evidence of socio-demographic disparities in outcomes of COVID-19, little is known about characteristics and clinical outcomes of patients admitted to public hospitals during the COVID-19 outbreak. OBJECTIVE:To assess demographics, comorbid conditions, and clinical factors associated with critical illness and mortality among patients diagnosed with COVID-19 at a public hospital in New York City (NYC) during the first month of the COVID-19 outbreak. DESIGN/METHODS:Retrospective chart review of patients diagnosed with COVID-19 admitted to NYC Health + Hospitals / Bellevue Hospital from March 9th to April 8th, 2020. RESULTS:A total of 337 patients were diagnosed with COVID-19 during the study period. Primary analyses were conducted among those requiring supplemental oxygen (n = 270); half of these patients (135) were admitted to the intensive care unit (ICU). A majority were male (67.4%) and the median age was 58 years. Approximately one-third (32.6%) of hypoxic patients managed outside the ICU required non-rebreather or non-invasive ventilation. Requirement of renal replacement therapy occurred in 42.3% of ICU patients without baseline end-stage renal disease. Overall, 30-day mortality among hypoxic patients was 28.9% (53.3% in the ICU, 4.4% outside the ICU). In adjusted analyses, risk factors associated with mortality included dementia (adjusted risk ratio (aRR) 2.11 95%CI 1.50-2.96), age 65 or older (aRR 1.97, 95%CI 1.31-2.95), obesity (aRR 1.37, 95%CI 1.07-1.74), and male sex (aRR 1.32, 95%CI 1.04-1.70). CONCLUSION/CONCLUSIONS:COVID-19 demonstrated severe morbidity and mortality in critically ill patients. Modifications in care delivery outside the ICU allowed the hospital to effectively care for a surge of critically ill and severely hypoxic patients.

BACKGROUND:An individual's risk for future opioid overdoses is usually assessed using a 12-month "lookback" period. Given the potential urgency of acting rapidly, we compared the performance of alternative predictive models with risk information from the past 3, 6, 9, and 12 months. METHODS:We included 1,014,033 Maryland residents aged 18-80 with at least 1 opioid prescription and no recorded death in 2015. We used 2015 Maryland prescription drug monitoring data to identify risk factors for nonfatal opioid overdoses from hospital discharge records and investigated fatal opioid overdose from medical examiner data in 2016. Prescription drug monitoring program-derived predictors included demographics, payment sources for opioid prescriptions, count of unique opioid prescribers and pharmacies, and quantity and types of opioids and benzodiazepines filled. We estimated a series of logistic regression models that included 3, 6, 9, and 12 months of prescription drug monitoring program data and compared model performance, using bootstrapped C-statistics and associated 95% confidence intervals. RESULTS:For hospital-treated nonfatal overdose, the C-statistic increased from 0.73 for a model including only the fourth quarter to 0.77 for a model with 4 quarters of data. For fatal overdose, the area under the curve increased from 0.80 to 0.83 over the same models. The strongest predictors of overdose were prescription fills for buprenorphine and Medicaid and Medicare as sources of payment. CONCLUSIONS:Models predicting opioid overdose using 1 quarter of data were nearly as accurate as models using all 4 quarters. Models with a single quarter may be more timely and easier to identify persons at risk of an opioid overdose.