Faculty Bibliography

BACKGROUND:Big events (i.e., unique historical disruptions) like the COVID-19 epidemic and its associated period of social distancing can transform social structures, social interactions, and social norms. Social distancing rules and the fear of infection have greatly reduced face-to-face interactions, increased loneliness, reduced ties to helping institutions, and may also have disrupted the opioid use behaviors of people who use drugs. This research used Reddit to examine the impact of COVID-19 on the social networks and social processes of people who use opioids. METHODS:Data were collected from the social media forum, Reddit.com. At the beginning of the COVID-19 pandemic in the U.S. (March 5, 2020, to May 13, 2020), 2,000 Reddit posts were collected from the two most popular opioid subreddits (r/OpiatesRecovery, r/Opiates). Posts were reviewed for relevance to COVID-19 and opioid use resulting in a final sample of 300. Thematic analysis was guided by the Big Events framework. RESULTS:The COVID-19 pandemic was found to create changes in the social networks and daily lives among persons who use opioids. Adaptions to these changes shifted social networks leading to robust social support and mutual aid on Reddit, including sharing and seeking advice on facing withdrawal, dealing with isolation, managing cravings, and accessing recovery resources. CONCLUSIONS:Reddit provided an important source of social support and mutual aid for persons who use opioids. Findings indicate online social support networks are beneficial to persons who use opioids, particularly during big events where isolation from other social support resources may occur.

BACKGROUND:The COVID-19 pandemic poses significant challenges to people with opioid use disorder (OUD). As localities enforce lockdowns and pass emergency OUD treatment regulations, questions arise about how these changes will affect access and retention in care. In this study, we explore the influence of COVID-19 on access to, experiences with, and motivations for OUD treatment through a qualitative analysis of public discussion forums on Reddit. METHODS:We collected data from Reddit, a free and international online platform dedicated to public discussions and user-generated content. We extracted 1000 of the most recent posts uploaded between March 5th and May 13th, 2020 from each of the two most popular opioid subreddits "r/Opiates" and "r/OpiatesRecovery" (total 2000). We reviewed posts for relevance to COVID-19 and opioid use and coded content using a hybrid inductive-deductive approach. Thematic analysis identified common themes related to study questions of interest. RESULTS:Of 2000 posts reviewed, 300 (15%) discussed topics related to the intersection of opioid use and COVID-19. Five major themes related to OUD treatment were identified: Concern about closure of OUD treatment services; transition to telehealth and virtual care; methadone treatment requirements and increased exposure to COVID-19; reactions to changing regulations on medications for OUD; and influences of the pandemic on treatment motivation and progress. CONCLUSION/CONCLUSIONS:In the face of unprecedented challenges due to COVID-19, reactions of Reddit opioid forum users ranged from increased distress in accessing and sustaining treatment, to encouragement surrounding new modes of treatment and opportunities to engage in care. New and less restrictive avenues for treatment were welcomed by many, but questions remain about how new norms and policy changes will be sustained beyond this pandemic and impact OUD treatment access and outcomes long-term.

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.

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.

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.

BACKGROUND AND AIMS/OBJECTIVE:Evidence from randomized controlled trials establishes that medication treatment with methadone and buprenorphine reduces opioid use and improves treatment retention. However, little is known about the role of such medications compared with non-medication treatments in mitigating overdose risk among US patient populations receiving treatment in usual care settings. This study compared overdose mortality among those in medication versus non-medication treatments in specialty care settings. DESIGN/METHODS:Retrospective cohort study using state-wide treatment data linked to death records. Survival analysis was used to analyze data in a time-to-event framework. SETTING/METHODS:Services delivered by 757 providers in publicly funded out-patient specialty treatment programs in Maryland, USA between 1 January 2015 and 31 December 2016. PARTICIPANTS/METHODS:A total of 48 274 adults admitted to out-patient specialty treatment programs in 2015-16 for primary diagnosis of opioid use disorder. MEASUREMENTS/METHODS:Main exposure was time in medication treatment (methadone/buprenorphine), time following medication treatment, time exposed to non-medication treatments and time following non-medication treatment. Main outcome was opioid overdose death during and after treatment. Hazard ratios were calculated using Cox proportional hazard regression. Propensity score weights were adjusted for patient information on sex, age, race, region of residence, marital and veteran status, employment, homelessness, primary opioid, mental health treatment, arrests and criminal justice referral. FINDINGS/RESULTS:The study population experienced 371 opioid overdose deaths. Periods in medication treatment were associated with substantially reduced hazard of opioid overdose death compared with periods in non-medication treatment [adjusted hazard ratio (aHR) = 0.18, 95% confidence interval (CI) = 0.08-0.40]. Periods after discharge from non-medication treatment (aHR = 5.45, 95% CI = 2.80-9.53) and medication treatment (aHR = 5.85, 95% CI = 3.10-11.02) had similar and substantially elevated risks compared with periods in non-medication treatments. CONCLUSIONS:Among Maryland patients in specialty opioid treatment, periods in treatment are protective against overdose compared with periods out of care. Methadone and buprenorphine are associated with significantly lower overdose death compared with non-medication treatments during care but not after treatment is discontinued.

INTRODUCTION/BACKGROUND:Qualitative analysis of Twitter posts reveals key insights about user norms, informedness, perceptions, and experiences related to opioid use disorder (OUD). This paper characterizes Twitter message content pertaining to medications for opioid use disorder (MOUD) and Naloxone. METHODS:In-depth thematic analysis was conducted of 1,010 Twitter messages collected in June 2019. Our primary aim was to identify user perceptions and experiences related to harm reduction (e.g., Naloxone) and MOUD (e.g., sublingual and Extended-release buprenorphine, Extended-release naltrexone, Methadone). RESULTS:Tweets relating to OUD were most commonly authored by general Twitter users (43.8%), private residential or detoxification programs (24.6%), healthcare providers (e.g., physicians, first responders; 4.3%), PWUOs (4.7%) and their caregivers (2.9%). Naloxone was mentioned in 23.8% of posts and authored most commonly by general users (52.9%), public health experts (7.4%), and nonprofit/advocacy organizations (6.6%). Sentiment was mostly positive about Naloxone (73.6%). Commonly mentioned MOUDs in our search consisted of Buprenorphine-naloxone (13.8%), Methadone (5.7%), Extended-release naltrexone (4.1%), and Extended-release buprenorphine (0.01%). Tweets authored by PWUOs (4.7%) most commonly related to factors influencing access to MOUD or adverse events related to MOUD (70.8%), negative or positive experiences with illicit substance use (25%), policies related to expanding access to treatments for OUD (8.3%), and stigma experienced by healthcare providers (8.3%). CONCLUSION/CONCLUSIONS:Twitter is utilized by a diverse array of individuals, including PWUOs, and offers an innovative approach to evaluate experiences and themes related to illicit opioid use, MOUD, and harm reduction.