Publications

BACKGROUND: Chronic liver disease (CLD) is among the strongest risk factors for adverse prescription opioid-related events. Yet, the current prevalence and factors associated with high-risk opioid prescribing in patients with chronic liver disease (CLD) remain unclear, making it challenging to address opioid safety in this population. Therefore, we aimed to characterize opioid prescribing patterns among patients with CLD.

METHODS: This retrospective cohort study included patients with CLD identified at a single medical center and followed for one year from 10/1/2015-9/30/2016. Multivariable, multinomial regression was used identify the patient characteristics, including demographics, medical conditions, and liver-related factors, that were associated with opioid prescriptions and high-risk prescriptions (≥90mg morphine equivalents per day [MME/day] or co-prescribed with benzodiazepines).

RESULTS: Nearly half (47%) of 12,425 patients with CLD were prescribed opioids over a one-year period, with 17% of these receiving high-risk prescriptions. The baseline factors significantly associated with high-risk opioid prescriptions included female gender (adjusted incident rate ratio, AIRR = 1.32, 95% CI = 1.14-1.53), Medicaid insurance (AIRR = 1.68, 95% CI = 1.36-2.06), cirrhosis (AIRR = 1.22, 95% CI = 1.04-1.43) and baseline chronic pain (AIRR = 3.40, 95% CI = 2.94-4.01), depression (AIRR = 1.93, 95% CI = 1.60-2.32), anxiety (AIRR = 1.84, 95% CI = 1.53-2.22), substance use disorder (AIRR = 2.16, 95% CI = 1.67-2.79), and Charlson comorbidity score (AIRR = 1.27, 95% CI = 1.22-1.32). Non-alcoholic fatty liver disease was associated with decreased high-risk opioid prescriptions (AIRR = 0.56, 95% CI = 0.47-0.66).

CONCLUSION: Opioid medications continue to be prescribed to nearly half of patients with CLD, despite efforts to curtail opioid prescribing due to known adverse events in this population.

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.

INTRODUCTION: Antibiotics and opioids are targeted by public health and stewardship communities for reductions in prescribing across the country. This study evaluates trends and factors associated with outpatient prescribing by dental and medical providers in a large integrated health system.

METHODS: This was a cross-sectional study of national dental and medical outpatient visits from Department of Veterans Affairs facilities in 2015-2017; analyzed in 2019-2020. Antibiotic and opioid prescribing rates were assessed by provider and facility characteristics. Multivariable Poisson regression adjusted for repeated measures by the provider was used to assess the independent association between facility and provider characteristics and rate of prescribing.

RESULTS: Over the study period, 4,625,840 antibiotic and 10,380,809 opioid prescriptions were identified for 115,625,890 visits. Physicians prescribed most antibiotics (67%). Dentists prescribed 6% of the antibiotics but had the highest per-visit antibiotic prescribing rate compared to medical providers (6.75 vs 3.90 prescriptions per 100 visits, p<0.0001), which was largely driven by dental specialists. By contrast, dentists had lower opioid prescribing than medical providers (3.02 vs 9.20 prescriptions per 100 visits, p<0.0001). Overall, antibiotic and opioid prescribing decreased over time, with opioids having the greatest decreases (-28.0%). In multivariable analyses, U.S. geographic region, rurality, and complexity were associated with prescribing for both drug classes. Opioid and antibiotic prescribing were positively correlated.

CONCLUSIONS: Although antibiotic and opioid prescribing has decreased, there are still important target areas for improvement. Interventions need to be tailored to community characteristics such as rurality and provider type.

Cost sharing in traditional Medicare can consume a substantial portion of the income of beneficiaries who do not have supplemental insurance from Medicaid, an employer, or a Medigap plan. Near-poor Medicare beneficiaries (with incomes more than 100 percent but less than 200 percent of the federal poverty level) are ineligible for Medicaid but frequently lack alternative supplemental coverage, resulting in a supplemental coverage "cliff" of 25.8 percentage points just above the eligibility threshold for Medicaid (100 percent of poverty). We estimated that beneficiaries affected by this supplemental coverage cliff incurred an additional $2,288 in out-of-pocket spending over the course of two years, used 55 percent fewer outpatient evaluation and management services per year, and filled fewer prescriptions. Lower prescription drug use was partly driven by low take-up of Part D subsidies, which Medicare beneficiaries automatically receive if they have Medicaid. Expanding eligibility for Medicaid supplemental coverage and increasing take-up of Part D subsidies would lessen cost-related barriers to health care among near-poor Medicare beneficiaries.

INTRODUCTION: The objective of this study is to identify county-level characteristics that may be high-impact targets for opioid and antibiotic interventions to improve dental prescribing.

METHODS: Prescriptions during 2012-2017 were extracted from the IQVIA Longitudinal Prescription database. Primary outcomes were yearly county-level antibiotic and opioid prescribing rates. Multivariable negative binomial regression identified associations between prescribing rates and county-level characteristics. All analyses occurred in 2020.

RESULTS: Over time, dental opioid prescribing rates decreased by 20% (from 4.02 to 3.22 per 100 people), whereas antibiotic rates increased by 5% (from 6.85 to 7.19 per 100 people). Higher number of dentists per capita, higher proportion of female residents, and higher proportion of residents aged <65 years were associated with increased opioid rates. Relative to location in the West, location in the Northeast (59%, 95% CI=52, 65) and Midwest (64%, 95% CI=60, 70) was associated with lower opioid prescribing rates. Higher clinician density, median household income, proportion female, and proportion White were all independently associated with higher antibiotic rates. Location in the Northeast (149%, 95% CI=137, 162) and Midwest (118%, 95% CI=111, 125) was associated with higher antibiotic rates. Opioid and antibiotic prescribing rates were positively associated.

CONCLUSIONS: Dental prescribing of opioids is decreasing, whereas dental antibiotic prescribing is increasing. High prescribing of antibiotics is associated with high prescribing of opioids. Strategies focused on optimizing dental antibiotics and opioids are needed given their impact on population health.

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.