Publications

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.

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.

IMPORTANCE: There is limited information about trends in the treatment of opioid use disorder (OUD) among Medicaid enrollees.

OBJECTIVE: To examine the use of medications for OUD and potential indicators of quality of care in multiple states.

DESIGN, SETTING, AND PARTICIPANTS: Exploratory serial cross-sectional study of 1 024 301 Medicaid enrollees in 11 states aged 12 through 64 years (not eligible for Medicare) with International Classification of Diseases, Ninth Revision (ICD-9 or ICD-10) codes for OUD from 2014 through 2018. Each state used generalized estimating equations to estimate associations between enrollee characteristics and outcome measure prevalence, subsequently pooled to generate global estimates using random effects meta-analyses.

EXPOSURES: Calendar year, demographic characteristics, eligibility groups, and comorbidities.

MAIN OUTCOMES AND MEASURES: Use of medications for OUD (buprenorphine, methadone, or naltrexone); potential indicators of good quality (OUD medication continuity for 180 days, behavioral health counseling, urine drug tests); potential indicators of poor quality (prescribing of opioid analgesics and benzodiazepines).

RESULTS: In 2018, 41.7% of Medicaid enrollees with OUD were aged 21 through 34 years, 51.2% were female, 76.1% were non-Hispanic White, 50.7% were eligible through Medicaid expansion, and 50.6% had other substance use disorders. Prevalence of OUD increased in these 11 states from 3.3% (290 628 of 8 737 082) in 2014 to 5.0% (527 983 of 10 585 790) in 2018. The pooled prevalence of enrollees with OUD receiving medication treatment increased from 47.8% in 2014 (range across states, 35.3% to 74.5%) to 57.1% in 2018 (range, 45.7% to 71.7%). The overall prevalence of enrollees receiving 180 days of continuous medications for OUD did not significantly change from the 2014-2015 to 2017-2018 periods (-0.01 prevalence difference, 95% CI, -0.03 to 0.02) with state variability in trend (90% prediction interval, -0.08 to 0.06). Non-Hispanic Black enrollees had lower OUD medication use than White enrollees (prevalence ratio [PR], 0.72; 95% CI, 0.64 to 0.81; P < .001; 90% prediction interval, 0.52 to 1.00). Pregnant women had higher use of OUD medications (PR, 1.18; 95% CI, 1.11-1.25; P < .001; 90% prediction interval, 1.01-1.38) and medication continuity (PR, 1.14; 95% CI, 1.10-1.17, P < .001; 90% prediction interval, 1.06-1.22) than did other eligibility groups.

CONCLUSIONS AND RELEVANCE: Among US Medicaid enrollees in 11 states, the prevalence of medication use for treatment of opioid use disorder increased from 2014 through 2018. The pattern in other states requires further research.

STUDY OBJECTIVE: More than 17 million people have gained health insurance coverage through the Patient Protection and Affordable Care Act's Medicaid expansion. Few studies have examined heterogeneity within the Medicaid expansion population. We do so based on time-varying patterns of emergency department (ED) and ambulatory care use, and characterize diagnoses associated with ED and ambulatory care visits to evaluate whether certain diagnoses predominate in individual trajectories.

METHOD: We used group-based multitrajectory modeling to jointly estimate trajectories of ambulatory care and ED utilization in the first 12 months of enrollment among Pennsylvania Medicaid expansion enrollees from 2015 to 2017.

RESULTS: Among 601,877 expansion enrollees, we identified 6 distinct groups based on joint trajectories of ED and ambulatory care use. Mean ED use varied across groups from 3.4 to 48.7 visits per 100 enrollees in the first month and between 2.8 and 44.0 visits per 100 enrollees in month 12. Mean ambulatory visit rates varied from 0.0 to 179 visits per 100 enrollees in the first month and from 0.0 to 274 visits in month 12. Rates of ED visits did not change over time, but rates of ambulatory care visits increased by at least 50% among 4 groups during the study period. Groups varied on chronic condition diagnoses, including mental health and substance use disorders, as well as diagnoses associated with ambulatory care visits.

CONCLUSION: We found substantial variation in rates of ED and ambulatory care use across empirically defined subgroups of Medicaid expansion enrollees. We also identified heterogeneity among the diagnoses associated with these visits. This data-driven approach may be used to target resources to encourage efficient use of ED services and support engagement with ambulatory care clinicians.