Publications

BACKGROUND: The availability of Medicare Part D pharmacy coverage may increase veterans' options for obtaining medications outside of the Department of Veterans Affairs (VA) pharmacies. However, availability of Part D coverage raises the potential that veterans may be receiving similar medications from VA and non-VA pharmacies. The VA's personal health record portal, My HealtheVet, allows veterans to self-enter the non-VA medications that they obtained from community-based pharmacies, including those reimbursed by Medicare Part D. The Blue Button medication view feature of My HealtheVet allows veterans to view and download their VA and self-entered non-VA medication history. OBJECTIVE: To examine whether the use by veterans of the Blue Button feature of My HealtheVet was associated with less acquisition of similar medications from VA and community-based pharmacies reimbursed by Medicare Part D. METHODS: This study included a national sample of veterans who were new My HealtheVet users during fiscal year 2013 (October 1, 2012-September 30, 2013) and who used the Blue Button medication view feature of My HealtheVet at least once (users). We compared these veterans with a random sample of veterans who were not registered to use My HealtheVet (nonusers). From these groups, we identified veterans who were enrolled in Part D. We used multiple logistic regression analysis to assess the association of Blue Button medication view use with obtaining medications from the same drug classes (with overlap of 7 or more days) from VA and Part D-reimbursed pharmacies. RESULTS: There were 7,973 My HealtheVet medication view users and 65,985 nonusers. During a 12-month period, medication view users received more 30-day supplies of medications (one 90-day supply equals three 30-day supplies) than nonusers, on average (152.1 vs 71.3, P < 0.001). A larger percentage of users than nonusers obtained medications from VA and Part D-reimbursed pharmacies with overlapping days supply from the same drug classes (30% vs 23%, P < 0.001). However, for veterans who obtained greater numbers of 30-day supplies (82 or more), a significantly smaller percentage of users than nonusers obtained overlapping medications from VA and Part D-reimbursed pharmacies. Moreover, controlling for the total number of 30-day supplies that veterans received, the odds of obtaining medications from VA and Part D-reimbursed pharmacies with days supply that overlapped by at least 7 days for the same drug classes was 18% lower for users than nonusers (P=0.002). CONCLUSIONS: Veterans who used the Blue Button medication view feature of My HealtheVet obtained a larger number of 30-day supplies of medications from VA pharmacies than nonusers. For veterans who obtained a larger number of 30-day supplies of medications, use of the Blue Button medication view feature of My HealtheVet was associated with less overlap in days supply of medication from the same drug class from VA and Part D-reimbursed pharmacies. DISCLOSURES: This study was funded by the Department of Veterans Affairs, Office of Research and Development, Health Services Research and Development Service project IIR 14-041-2. The sponsor provided funding but was not involved in the development of the manuscript. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the Health Services Research and Development Service. All authors are employed in some capacity with the Department of Veterans Affairs and have no conflicts of interest to disclose.

BACKGROUND: Medicare Part B pharmaceutical spending has increased rapidly, more than doubling in 2006-2017. Yet, it is unclear whether this increase was driven by increased utilization or increased cost per claim. OBJECTIVE: To evaluate the relative impact of changes in drug utilization and cost per claim on changes in Medicare Part B pharmaceutical spending in 2008-2016 overall, by drug type (specialty and nonspecialty) and therapeutic category. METHODS: In this retrospective descriptive study, we extracted all claims in 2008-2016 for separately payable Part B drugs from a 5% random sample of Medicare beneficiaries. Our study included 3 outcomes calculated annually for all included drugs: (1) spending, defined as the sum of total payments; (2) utilization, defined as total number of claims; and (3) cost per claim, defined as spending divided by the number of claims. Estimates of spending and utilization were expressed per beneficiary-year. Spending and cost per claim were adjusted for inflation. For each outcome, we calculated relative changes in 2008-2016. We repeated analyses stratifying by drug type (specialty and nonspecialty) and therapeutic class. RESULTS: Pharmaceutical spending in Medicare Part B increased by 34% from 2008-2016, driven by a 53% increase in the cost per claim. Utilization decreased by 12%. Spending on specialty drugs increased by 56%, driven by a 48% increase in the cost per claim and a 6% utilization increase. Spending on nonspecialty drugs decreased by 32% driven by an 18% reduction in the cost per claim and a 17% reduction in utilization. Spending on ophthalmic preparations increased by 281%, driven by a 238% utilization increase and a 13% increase in the cost per claim. Spending on antiarthritic and immunologic agents increased by 159%, driven by a 117% increase in the cost per claim and a 19% utilization increase. CONCLUSIONS: Medicare Part B pharmaceutical spending grew in recent years, despite decreased utilization, driven by an overall increase in the cost per claim. This was a product of rising drug prices and increased utilization of more expensive specialty drugs. These findings support the development of policies that aim to spur competition and control price growth of provider-administered drugs. DISCLOSURES: The authors acknowledge funding from the Myers Family Foundation. Hernandez was funded by the National Heart, Lung and Blood Institute (grant number K01HL142847). Shrank is an employee of Humana. Good is an employee of the UPMC Health Plan Insurance Services Division. There are no other potential conflicts of interest to disclose.

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.