Publications

OBJECTIVE: To determine internal medicine (IM) residents' knowledge of, attitudes towards, and barriers to prescribing buprenorphine for opioid use disorder (OUD).

METHODS: We conducted a cross-sectional study of IM residents across all 35 Accreditation Council for Graduate Medical Education (ACGME) accredited Florida IM residency programs. We used an online survey to collect information about resident demographics, substance use curriculums, career interests, content knowledge about diagnosing and managing OUD, and attitudes about and barriers to prescribing buprenorphine for OUD. We used Chi-square test to explore differences in interest in prescribing buprenorphine. We created a composite knowledge score and investigated distribution of knowledge among characteristics via Mann-Whitney U test.

RESULTS: There were 161 participants (response rate 16.0%, n = 1008) across 35 programs Seventy-seven percent of residents provided care for patients with OUD more than once per month. Seventy-four percent report no buprenorphine prescribing training. Higher knowledge scores, interest in primary care, being an intern, and caring for patients with OUD more than monthly were associated with interest in obtaining a buprenorphine waiver (P < 0.05). Limited knowledge about OUD was the most important barrier to prescribing buprenorphine. Eighty-nine percent support legislation to deregulate buprenorphine.

CONCLUSIONS: Knowledge about managing OUD was poor and represented the most commonly cited barrier to prescribing buprenorphine. Residents want to expand their role in treating OUD. Our findings warrant incorporating addiction medicine into residency curriculum standards. Legislation removing the buprenorphine waiver requirement may increase the number of resident buprenorphine prescribers and improve treatment options for patients with opioid addiction.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algorithm to predict opioid overdose risk by integrating human services and criminal justice data with health claims data to capture the social determinants of overdose risk. This prognostic study included Medicaid beneficiaries (n = 237,259) in Allegheny County, Pennsylvania enrolled between 2015 and 2018, randomly divided into training, testing, and validation samples. We measured 290 potential predictors (239 derived from Medicaid claims data) in 30-day periods, beginning with the first observed Medicaid enrollment date during the study period. Using a gradient boosting machine, we predicted a composite outcome (i.e., fatal or nonfatal opioid overdose constructed using medical examiner and claims data) in the subsequent month. We compared prediction performance between a Medicaid claims only model to one integrating human services and criminal justice data with Medicaid claims (i.e., integrated model) using several metrics (e.g., C-statistic, number needed to evaluate [NNE] to identify one overdose). Beneficiaries were stratified into risk-score decile subgroups. The samples (training = 79,087, testing = 79,086, validation = 79,086) had similar characteristics (age = 38±18 years, female = 56%, white = 48%, having at least one overdose = 1.7% during study period). Using the validation sample, the integrated model slightly improved on the Medicaid claims only model (C-statistic = 0.885; 95%CI = 0.877-0.892 vs. C-statistic = 0.871; 95%CI = 0.863-0.878), with small corresponding improvements in the NNE and positive predictive value. Nine of the top 30 most important predictors in the integrated model were human services and criminal justice variables. Using the integrated model, approximately 70% of individuals with overdoses were members of the top risk decile (overdose rates in the subsequent month = 47/10,000 beneficiaries). Few individuals in the bottom 9 deciles had overdose episodes (0-12/10,000). Machine-learning algorithms integrating claims and social service and criminal justice data modestly improved opioid overdose prediction among Medicaid beneficiaries for a large U.S. county heavily affected by the opioid crisis.

BACKGROUND: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose.

OBJECTIVE: To predict risk of death after a nonfatal opioid overdose.

DESIGN AND PARTICIPANTS: This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period.

EXPOSURES, MAIN OUTCOME, AND MEASURES: Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup.

KEY RESULTS: Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001).

CONCLUSIONS: A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.

BACKGROUND: Most states have recently passed laws requiring prescribers to use prescription drug monitoring programs (PDMPs) before prescribing opioid medications. The impact of these mandates on discontinuing chronic opioid therapy among Veterans managed in the Veterans Health Administration (VA) is unknown. We assess the association between the earliest of these laws and discontinuation of chronic opioid therapy in Veterans receiving VA health care.

METHODS: We conducted a comparative interrupted time-series study in the 5 states mandating PDMP use before August 2013 (Ohio, West Virginia, Kentucky, New Mexico, and Tennessee), adjusting for trends in the 17 neighboring control states without such mandates. We modeled 25 months of prescribing for each state centered on the month the mandate became effective. We included Veterans prescribed long-term outpatient opioid therapy (305 of the preceding 365 d). Our outcomes were discontinuation of chronic opioid therapy (primary outcome) and the average daily quantity of opioids per Veteran over the following 6 months (secondary outcome).

RESULTS: We included 250 monthly cohorts with 225,665 unique Veterans and 3.4 million Veteran-months. Baseline discontinuation rates before the PDMP mandates were 0.4%-2.7% per month. Kentucky saw a discontinuation increase of 1 absolute percentage point following its PDMP mandate which decreased over time. The other 4 states had no significant association between their mandates and change in opioid discontinuation. There was no evidence of decreasing opioid quantities following PDMP mandates.

CONCLUSION: We did not find consistent evidence that state laws mandating provider PDMP use were associated with the discontinuation of chronic opioid therapy within the VA for the time period studied.

BACKGROUND: Health systems are increasingly implementing interventions to reduce older patients' use of low-value medications. However, prescribers' perspectives on medication value and the acceptability of interventions to reduce low-value prescribing are poorly understood.

OBJECTIVE: To identify the characteristics that affect the value of a medication and those factors influencing low-value prescribing from the perspective of primary care physicians.

DESIGN: Qualitative study using semi-structured interviews.

SETTING: Academic and community primary care practices within University of Pittsburgh Medical Center health system.

PARTICIPANTS: Sixteen primary care physicians.

MEASUREMENTS: We elicited 16 prescribers' perspectives on definitions and examples of low-value prescribing in older adults, the factors that incentivize them to engage in such prescribing, and the characteristics of interventions that would make them less likely to engage in low-value prescribing.

RESULTS: We identified three key themes. First, prescribers viewed low-value prescribing among older adults as common, characterized both by features of the medications themselves and of the particular patients to whom they were prescribed. Second, prescribers described the causes of low-value prescribing as multifactorial, with factors related to patients, prescribers, and the health system as a whole, making low-value prescribing a default practice pattern. Third, interventions addressing low-value prescribing must minimize the cognitive load and time pressures that make low-value prescribing common. Interventions increasing time pressure or cognitive load, such as increased documentation, were considered less acceptable.

CONCLUSIONS: Our findings demonstrate that low-value prescribing is a well-recognized phenomenon, and that interventions to reduce low-value prescribing must consider physicians' perspectives and address the specific patient, prescriber and health system factors that make low-value prescribing a default practice.

BACKGROUND: Many persons with opioid use disorder (OUD) initiate medication for opioid use disorder (MOUD) with one clinic and switch to another clinic during their course of treatment. These switches may occur for referrals or for unplanned reasons. It is unknown, however, what effect switching MOUD clinics has on continuity of MOUD treatment or on overdoses.

OBJECTIVE: To examine patterns of switching MOUD clinics and its association with the proportion of days covered (PDC) by MOUD, and opioid-related overdose.

DESIGN: Cross-sectional retrospective analysis of Pennsylvania Medicaid claims data.

MAIN MEASURES: MOUD clinic switches (i.e., filling a MOUD prescription from a prescriber located in a different clinic than the previous prescriber), PDC, and opioid-related overdose.

RESULTS: Among 14,107 enrollees, 43.2 % switched clinics for MOUD at least once during the 270 day period. In multivariate regression results, enrollees who were Non-Hispanic black (IRR = 1.43; 95 % CI = 1.24-1.65; p < 0.001), had previous methadone use (IRR = 1.32; 95 % CI = 1.13-1.55; p < 0.001), and a higher total number of office visits (IRR = 1.01; CI = 1.01-1.01; p < 0.001) had more switches. The number of clinic switches was positively associated with PDC (OR = 1.12; 95 % CI = 1.10-1.13). In secondary analyses, we found that switches for only one MOUD fill were associated with lower PDC (OR = 0.97; 95 % CI = 0.95-0.99), while switches for more than one MOUD fill were associated with higher PDC (OR = 1.40; 95 % CI = 1.36-1.44). We did not observe a relationship between opioid-related overdose and clinic switches.

CONCLUSIONS: Lack of prescriber continuity for receiving MOUD may not be problematic as it is for other conditions, insofar as it is related to overdose and PDC.

BACKGROUND: Survivors of opioid overdose have substantially increased mortality risk, although this risk is not evenly distributed across individuals. No study has focused on predicting an individual's risk of death after a nonfatal opioid overdose.

OBJECTIVE: To predict risk of death after a nonfatal opioid overdose.

DESIGN AND PARTICIPANTS: This retrospective cohort study included 9686 Pennsylvania Medicaid beneficiaries with an emergency department or inpatient claim for nonfatal opioid overdose in 2014-2016. The index date was the first overdose claim during this period.

EXPOSURES, MAIN OUTCOME, AND MEASURES: Predictor candidates were measured in the 180 days before the index overdose. Primary outcome was 180-day all-cause mortality. Using a gradient boosting machine model, we classified beneficiaries into six subgroups according to their risk of mortality (< 25th percentile of the risk score, 25th to < 50th, 50th to < 75th, 75th to < 90th, 90th to < 98th, ≥ 98th). We then measured receipt of medication for opioid use disorder (OUD), risk mitigation interventions (e.g., prescriptions for naloxone), and prescription opioids filled in the 180 days after the index overdose, by risk subgroup.

KEY RESULTS: Of eligible beneficiaries, 347 (3.6%) died within 180 days after the index overdose. The C-statistic of the mortality prediction model was 0.71. In the highest risk subgroup, the observed 180-day mortality rate was 20.3%, while in the lowest risk subgroup, it was 1.5%. Medication for OUD and risk mitigation interventions after overdose were more commonly seen in lower risk groups, while opioid prescriptions were more likely to be used in higher risk groups (both p trends < .001).

CONCLUSIONS: A risk prediction model performed well for classifying mortality risk after a nonfatal opioid overdose. This prediction score can identify high-risk subgroups to target interventions to improve outcomes among overdose survivors.