Walid F. Gellad

Background: In the United States, there is well-documented regional variation in prescription drug spending. However, the specific role of physician adoption of brand name drugs on the variation in patient-level prescription drug spending is still being investigated across a multitude of drug classes. Our study aims to add to the literature by determining the association between physician adoption of a first-in-class anti-diabetic (AD) drug, sitagliptin, and AD drug spending in the Medicare and Medicaid populations in Pennsylvania. Methods: We obtained physician-level data from QuintilesIMS Xponent™ database for Pennsylvania and constructed county-level measures of time to adoption and share of physicians adopting sitagliptin in its first year post-introduction. We additionally measured total AD drug spending for all Medicare fee-for-service and Part D enrollees (N = 125,264) and all Medicaid (N = 50,836) enrollees with type II diabetes in Pennsylvania for 2011. Finite mixture model regression, adjusting for patient socio-demographic/clinical characteristics, was used to examine the association between physician adoption of sitagliptin and AD drug spending. Results: Physician adoption of sitagliptin varied from 44 to 99% across the state’s 67 counties. Average per capita AD spending was $1340 (SD $1764) in Medicare and $1291 (SD $1881) in Medicaid. A 10% increase in the share of physicians adopting sitagliptin in a county was associated with a 3.5% (95% CI: 2.0–4.9) and 5.3% (95% CI: 0.3–10.3) increase in drug spending for the Medicare and Medicaid populations, respectively. Conclusions: In a medication market with many choices, county-level adoption of sitagliptin was positively associated with AD spending in Medicare and Medicaid, two programs with different approaches to formulary management.

Background Treatment by high-opioid prescribing physicians in the emergency department (ED) is associated with higher rates of long-term opioid use among Medicare beneficiaries. However, it is unclear if this result is true in other high-risk populations such as Veterans. Objective To estimate the effect of exposure to high-opioid prescribing physicians on long-term opioid use for opioid-naïve Veterans. Design Observational study using Veterans Health Administration (VA) encounter and prescription data. Setting and Participants Veterans with an index ED visit at any VA facility in 2012 and without opioid prescriptions in the prior 6 months in the VA system (“opioid naïve”). Measurements We assigned patients to emergency physicians and categorized physicians into within-hospital quartiles based on their opioid prescribing rates. Our primary outcome was long-term opioid use, defined as 6 months of days supplied in the 12 months subsequent to the ED visit. We compared rates of long-term opioid use among patients treated by high versus low quartile prescribers, adjusting for patient demographic, clinical characteristics, and ED diagnoses. Results We identified 57,738 and 86,393 opioid-naïve Veterans managed by 362 and 440 low and high quartile prescribers, respectively. Patient characteristics were similar across groups. ED opioid prescribing rates varied more than threefold between the low and high quartile prescribers within hospitals (6.4% vs. 20.8%, p < 0.001). The frequency of long-term opioid use was higher among Veterans treated by high versus low quartile prescribers, though above the threshold for statistical significance (1.39% vs. 1.26%; adjusted OR 1.11, 95% CI 0.997–1.24, p = 0.056). In subgroup analyses, there were significant associations for patients with back pain (adjusted OR 1.25, 95% CI 1.01–1.55, p = 0.04) and for those with a history of depression (adjusted OR 1.28, 95% CI 1.08–1.51, p = 0.004). Conclusions ED physician opioid prescribing varied by over 300% within facility, with a statistically non-significant increased rate of long-term use among opioid-naïve Veterans exposed to the highest intensity prescribers.

More than half of Veterans Affairs (VA) enrollees are also covered by Medicare and have the option of receiving their prescriptions from VA or Medicare-participating providers. This study examines ... Background: More than half of enrollees in the U.S. Department of Veterans Affairs (VA) are also covered by Medicare and can choose to receive their prescriptions from VA or from Medicare-participa...

Mitigating the risks of adverse outcomes from opioids is critical. Thus, the Veterans Affairs (VA) Healthcare System developed the Stratification Tool for Opioid Risk Management (STORM), a dashboard to assist clinicians with opioid risk evaluation and mitigation. Updated daily, STORM calculates a “risk score” of adverse outcomes (e.g., suicide-related events, overdoses, overdose death) from variables in the VA medical record for all patients with an opioid prescription and displays this information along with documentation of recommended risk mitigation strategies and non-opioid pain treatments. In March 2018, the VA issued a policy notice requiring VA Medical Centers (VAMCs) to complete case reviews for patients whom STORM identifies as very high-risk (i.e., top 1% of STORM risk scores). Half of VAMCs were randomly assigned notices that also stated that additional support and oversight would be required for VAMCs that failed to meet an established percentage of case reviews. Using a stepped-wedge cluster randomized design, VAMCs will be further randomized to conduct case reviews for an expanded pool of patients (top 5% of STORM risk scores vs. 1%) starting either 9 or 15 months after the notice was released, creating four natural arms. VA commissioned an evaluation to understand the implementation strategies and factors associated with case review completion rates, whose protocol is described in this report. This mixed-method study will include an online survey of all VAMCs to identify implementation strategies and interviews at a subset of facilities to identify implementation barriers and facilitators. The survey is based on the Expert Recommendations for Implementing Change (ERIC) project, which engaged experts to create consensus on 73 implementation strategies. We will use regression models to compare the number and types of implementation strategies across arms and their association with case review completion rates. Using questions from the Consolidated Framework for Implementation Research, we will interview stakeholders at 40 VAMCs with the highest and lowest adherence to opioid therapy guidelines. By identifying which implementation strategies, barriers, and facilitators influence case reviews to reduce opioid-related adverse outcomes, this unique implementation evaluation will enable the VA to improve the design of future opioid safety initiatives. This project is registered at the ISRCTN Registry with number ISRCTN16012111 . The trial was first registered on 5/3/2017.

Overlapping use of opioids and benzodiazepines is associated with increased risk for overdose. Veterans receiving medications concurrently from the U.S. Department of Veterans Affairs (VA) and Medicare may be at higher risk for such overlap. To assess the association between dual use of VA and Medicare drug benefits and receipt of overlapping opioid and benzodiazepine prescriptions. Cross-sectional. VA and Medicare. All veterans enrolled in VA and Medicare Part D who filled at least 2 opioid prescriptions in 2013 (n = 368 891). Outcomes were the proportion of patients with a Pharmacy Quality Alliance (PQA) measure of opioid–benzodiazepine overlap (≥2 filled prescriptions for benzodiazepines with ≥30 days of overlap with opioids) and the proportion of patients with high-dose opioid–benzodiazepine overlap (≥30 days of overlap with a daily opioid dose >120 morphine milligram equivalents). Augmented inverse probability weighting regression was used to compare these measures by prescription drug source: VA only, Medicare only, or VA and Medicare (dual use). Of 368 891 eligible veterans, 18.3% received prescriptions from the VA only, 30.3% from Medicare only, and 51.4% from both VA and Medicare. The proportion with PQA opioid–benzodiazepine overlap was larger for the dual-use group than the VA-only group (23.1% vs. 17.3%; adjusted risk ratio [aRR], 1.27 [95% CI, 1.24 to 1.30]) and Medicare-only group (23.1% vs. 16.5%; aRR, 1.12 [CI, 1.10 to 1.14]). The proportion with high-dose overlap was also larger for the dual-use group than the VA-only group (4.7% vs. 2.3%; aRR, 2.23 [CI, 2.10 to 2.36]) and Medicare-only group (4.7% vs. 2.9%; aRR, 1.06 [CI, 1.02 to 1.11]). Data are from 2013 and cannot capture medications purchased without insurance; unmeasured confounding may remain in this cross-sectional study. Among a national cohort of veterans dually enrolled in VA and Medicare, receiving prescriptions from both sources was associated with greater risk for receiving potentially unsafe overlapping prescriptions for opioids and benzodiazepines. U.S. Department of Veterans Affairs.

Objectives. To estimate the prevalence and consequences of receiving prescription opioids from both the Department of Veterans Affairs (VA) and Medicare Part D. Methods. Among US veterans enrolled in both VA and Part D filling 1 or more opioid prescriptions in 2012 (n = 539 473), we calculated 3 opioid safety measures using morphine milligram equivalents (MME): (1) proportion receiving greater than 100 MME for 1 or more days, (2) mean days receiving greater than 100 MME, and (3) proportion receiving greater than 120 MME for 90 consecutive days. We compared these measures by opioid source. Results. Overall, 135 643 (25.1%) veterans received opioids from VA only, 332 630 (61.7%) from Part D only, and 71 200 (13.2%) from both. The dual-use group was more likely than the VA-only group to receive greater than 100 MME for 1 or more days (34.3% vs 10.9%; adjusted risk ratio [ARR] = 3.0; 95% confidence interval [CI] = 2.9, 3.1), have more days with greater than 100 MME (42.5 vs 16.9 days; adjusted difference = 16.4 days; 95% CI = 15.7, 17.2), and to receive greater than 120 MME for 90 consecutive days (7.8% vs 3.1%; ARR = 2.2; 95% CI = 2.1, 2.3). Conclusions. Among veterans dually enrolled in VA and Medicare Part D, dual use of opioids was associated with more than 2 to 3 times the risk of high-dose opioid exposure.

Background : Buprenorphine is a key tool in the management of opioid use disorder, but there are growing concerns about abuse, diversion, and safety. These concerns are amplified for the Department of Veterans Affairs (VA), whose patients may receive care concurrently from multiple prescribers within and outside VA. To illustrate the extent of this challenge, we examined overlapping prescriptions for buprenorphine, opioids, and benzodiazepines among veterans dually enrolled in VA and Medicare Part D. Methods : We constructed a cohort of all veterans dually enrolled in VA and Part D who filled an opioid prescription in 2012. We identified patients who received tablet or film buprenorphine products from either source. We calculated the proportion of buprenorphine recipients with any overlapping prescription (based on days supply) for a nonbuprenorphine opioid or benzodiazepine, focusing on veterans who received overlapping prescriptions from a different system than their buprenorphine prescription (Part D buprenorphine recipients receiving overlapping opioids or benzodiazepines from VA and vice versa). Results : There were 1790 dually enrolled veterans with buprenorphine prescriptions, including 760 (43%) from VA and 1091 (61%) from Part D (61 veterans with buprenorphine from both systems were included in each group). Among VA buprenorphine recipients, 199 (26%) received an overlapping opioid prescription and 11 (1%) received an overlapping benzodiazepine prescription from Part D. Among Part D buprenorphine recipients, 208 (19%) received an overlapping opioid prescription and 178 (16%) received an overlapping benzodiazepine prescription from VA. Among VA and Part D buprenorphine recipients with cross-system opioid overlap, 25% (49/199) and 35% (72/208), respectively, had >90 days of overlap. Conclusions : Many buprenorphine recipients receive overlapping prescriptions for opioids and benzodiazepines from a different health care system than the one in which their buprenorphine was filled. These findings highlight a previously undocumented safety risk for veterans dually enrolled in VA and Medicare.

Medications to treat and prevent chronic disease have substantially reduced morbidity and mortality; however, their diffusion has been uneven. Little is known about prescribing of chronic disease medications by nurse practitioners (NPs) and physician assistants (PAs), despite their increasingly important role as primary care providers. Thus, we sought to conduct an exploratory analysis to examine prescribing of new chronic disease medications by NPs and PAs compared to primary care physicians (PCPs). We obtained prescribing data from IMS Health’s Xponent™ on all NPs, PAs, and PCPs in Pennsylvania regularly prescribing anticoagulants, antihypertensives, oral hypoglycemics, and/or HMG-Co-A reductase inhibitors pre- and post-introduction of five new drugs in these classes that varied in novelty (i.e., dabigatran, aliskiren, sitagliptin or saxagliptin, and pitavastatin). We constructed three measures of prescriber adoption during the 15-month post-FDA approval period: 1) any prescription of the medication, 2) proportion of prescriptions in the class for the medication, and 3) time to adoption (first prescription) of the medication. From 2007 to 2011, the proportion of antihypertensive prescriptions prescribed by NPs and PAs approximately doubled from 2.0 to 4.2 % and 2.2 to 4.9 %, respectively. Similar trends were found for anticoagulants, oral hypoglycemics, and HMG-Co-A reductase inhibitors. By 2011, more PCPs had prescribed each of the newly approved medications than NPs and PAs (e.g., 44.3 % vs. 18.5 % vs. 20 % for dabigatran among PCPs, NPs, and PAs). Across all medication classes, the newly approved drugs accounted for a larger share of prescriptions in the class for PCPs followed by PAs, followed by NPs (e.g., dabigatran: 4.9 % vs. 3.2 % vs. 2.8 %, respectively). Mean time-to-adoption for the newly approved medications was shorter for PCPs compared to NPs and PAs (e.g., dabigatran, 7.3 vs. 8.2 vs. 8.5 months; P all medications <0.001). PCPs were more likely to prescribe each of the newly approved medications per each measure of drug adoption, regardless of drug novelty. Differences in the rate and speed of drug adoption between PCPs, NPs, and PAs may have important implications for care and overall costs at the population level as NPs and PAs continue taking on a larger role in prescribing.