Faculty Bibliography

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.

Over the last decade, Baltimore has become a non-traditional sanctuary city, receiving an unprecedented influx of Latino immigrants, mostly from Central America's Northern Triangle, who are often fleeing violence in their home countries. This study explored the nature and frequency of healthcare utilization for mental health problems among uninsured/uninsurable Latinos who received outpatient care between 2012 and 2015 through an academic hospital-affiliated program that covers primary and specialty services to uninsured patients without regard to documentation status. Encounters for mental health disorders were the most common category, accounting for 14.88% of all visits. Mood (78%) and anxiety disorders (16%) were the most prevalent mental health diagnoses. The most frequent reason to seek care was symptom, signs, and ill-defined conditions (37.47%), and within this subgroup, pain was the leading cause of seeking care (88%), which may indicate high rates of somatization of mental health distress. This study presents a unique opportunity to explore the burden and nature of mental health needs among a population for which healthcare information is rarely attainable and highlights the need for culturally competent screening mechanisms and interventions to address the stressors faced by emergent communities.

BACKGROUND:Persons who interact with criminal justice and hospital systems are particularly vulnerable to negative health outcomes, including overdose. However, the relationship between justice involvement, healthcare utilization and overdose risk is not well-understood. This data linkage study seeks to improve our understanding of the link between different types of justice involvement as well as hospital interaction and risk of fatal opioid overdose among persons with incarcerations, arrests and parole/probation records for drug and property crimes in Maryland. METHODS:Maryland statewide criminal justice records were obtained for 2013-2016. Data were linked at the person-level to an all-payer hospitalization database and overdose death records for the same years. Logistic regression was performed to determine which criminal justice and hospital characteristics were associated with greatest risk of overdose death. RESULTS:89,591 adults had criminal-justice records and were included in the study. During the 2013-2016 study period, 4108 (4.59 %) were hospitalized for a non-fatal opioid overdose, and 519 (0.58 %) died of opioid overdose. Strongest risk factors for death included being older, being white, having had an inpatient or emergency hospitalization, having had more arrests, having been arrested for a drug charge (vs. property charge), having a misdemeanor drug charge (vs. a felony charge), and having been released from incarceration during the study period. CONCLUSION/CONCLUSIONS:Linking corrections and healthcare information can help advance understanding of risk and target overdose prevention interventions directed at justice-involved individuals with greatest need.

OBJECTIVE:To examine the impact of Section 1115 waivers on Medicaid coverage and opioid agonist therapy (OAT) utilization among substance use treatment admissions. DATA SOURCE/METHODS:Treatment Episode Data Set-Admissions (TEDS-A) (2001-2012). STUDY DESIGN/METHODS:We examined effects of 1115 waiver implementation on proportions of substance use treatment admissions with Medicaid and receiving OAT, using random intercept linear regression. PRINCIPAL FINDINGS/RESULTS:1115 waiver implementation was associated with an average of a 6 percentage point increase in proportion of all admissions with Medicaid, and 4 percentage point increase among opioid outpatient admissions. Implementation was not associated with change in proportion of opioid outpatient admissions receiving OAT. CONCLUSIONS:1115 waivers influence Medicaid coverage among substance use treatment admissions. The findings improve our understanding of how state policies impact substance use treatment utilization.

STUDY OBJECTIVE/OBJECTIVE:Persons with substance use disorders frequently utilize emergency department (ED) services, creating an opportunity for intervention and referral to addiction treatment and harm-reduction services. However, EDs may not have the appropriate tools to distinguish which patients are at greatest risk for negative outcomes. We link hospital ED and medical examiner mortality databases in one state to identify individual-level risk factors associated with overdose death among ED patients with substance-related encounters. METHODS:This retrospective cohort study linked Maryland statewide ED hospital claims records for adults with nonfatal overdose or substance use disorder encounters in 2014 to 2015 with medical examiner mortality records in 2015 to 2016. Logistic regression was used to identify factors in hospital records associated with risk of opioid overdose death. Predicted probabilities for overdose death were calculated for hypothetical patients with different combinations of overdose and substance use diagnostic histories. RESULTS:A total of 139,252 patients had substance-related ED encounters in 2014 to 2015. Of these patients, 963 later experienced an opioid overdose death, indicating a case fatality rate of 69.2 per 10,000 patients, 6 times higher than that of patients who used the ED for any cause. Factors most strongly associated with death included having both an opioid and another substance use disorder (adjusted odds ratio 2.88; 95% confidence interval 2.04 to 4.07), having greater than or equal to 3 previous nonfatal overdoses (adjusted odds ratio 2.89; 95% confidence interval 1.54 to 5.43), and having a previous nonfatal overdose involving heroin (adjusted odds ratio 2.24; 95% confidence interval 1.64 to 3.05). CONCLUSION/CONCLUSIONS:These results highlight important differences in overdose risk among patients receiving care in EDs for substance-related conditions. The findings demonstrate the potential utility of incorporating routine data from patient records to assess risk of future negative outcomes and identify primary targets for initiation and linkage to lifesaving care.