Publications

OBJECTIVE: To identify sociodemographic profiles of patients prescribed high-dose opioids.

DESIGN: Cross-sectional cohort study.

SETTING/PATIENTS: Veterans dually-enrolled in Veterans Health Administration and Medicare Part D, with ≥1 opioid pre-scription in 2012.

MAIN OUTCOME MEASURES: We identified five patient-level demographic characteristics and 12 community variables re-flective of region, socioeconomic deprivation, safety, and internet connectivity. Our outcome was the proportion of vet-erans receiving >120 morphine milligram equivalents (MME) for ≥90 consecutive days, a Pharmacy Quality Alliance measure of chronic high-dose opioid prescribing. We used classification and regression tree (CART) methods to identify risk of chronic high-dose opioid prescribing for sociodemographic subgroups.

RESULTS: Overall, 17,271 (3.3 percent) of 525,716 dually enrolled veterans were prescribed chronic high-dose opioids. CART analyses identified 35 subgroups using four sociodemographic and five community-level measures, with high-dose opioid prescribing ranging from 0.28 percent to 12.1 percent. The subgroup (n = 16,302) with highest frequency of the outcome included veterans who were with disability, age 18-64 years, white or other race, and lived in the Western Census region. The subgroup (n = 14,835) with the lowest frequency of the outcome included veterans who were with-out disability, did not receive Medicare Part D Low Income Subsidy, were >85 years old, and lived in communities within the second and sixth to tenth deciles of community public assistance.

CONCLUSIONS: Using CART analyses with sociodemographic and community-level variables only, we identified sub-groups of veterans with a 43-fold difference in chronic high-dose opioid prescriptions. Interactions among disability, age, race/ethnicity, and region should be considered when identifying high-risk subgroups in large populations.

BACKGROUND: Cash prices for prescription drugs vary widely in the United States.

OBJECTIVE: To describe cash price variation by retail pharmacy type for 10 generic and 6 brand-name drugs throughout the United States and stratified by ZIP code.

DESIGN: Cross-sectional study.

SETTING: Drug pricing data from GoodRx, an online tool for comparing drug prices, representing more than 60 000 U.S. pharmacies (fall 2015).

MEASUREMENTS: Cash prices for a 1-month supply of generic and brand-name drugs were ascertained. Stratified by ZIP code, relative cash prices for groups of generic and brand-name drugs were estimated for big box, grocery-based, small chain, and independent pharmacies compared with a reference group of large chain pharmacies.

RESULTS: Across 16 325 ZIP codes, 68 353 unique pharmacy stores contributed cash prices. When stratified by 5-digit ZIP code, the relative cash prices for generic drugs at big box, grocery-based, small chain, and independent pharmacies compared with those at large chain pharmacies were 0.52 (95% CI, 0.51 to 0.53), 0.82 (CI, 0.81 to 0.83), 1.51 (CI, 1.45 to 1.56), and 1.61 (CI, 1.58 to 1.64), respectively. The relative cash prices for brand-name drugs were 0.97 (CI, 0.96 to 0.97), 1.00 (CI, 0.99 to 1.00), 1.06 (CI, 1.05 to 1.08), and 1.03 (CI, 1.02 to 1.04), respectively.

LIMITATION: Results may not reflect current drug prices and do not account for point-of-sale discounts or price matching that may be offered by smaller pharmacies.

CONCLUSION: Compared with large chains, independent pharmacies and small chains had the highest cash prices for generic drugs and big box pharmacies the lowest. Relative differences in cash prices for brand-name drugs were modest across types of retail pharmacies.

PRIMARY FUNDING SOURCE: Arnold Ventures.

PURPOSE OF REVIEW: Educating clinicians on how to improve the medical management of type 2 diabetes in the modern pharmacologic era represents an enormous challenge given the number of medications available and the diversity across guideline recommendations. Academic detailing uses active social marketing techniques to deliver in-office, face-to-face educational encounters between a trained clinical educator (academic detailer) and a primary care clinician and can improve the quality of prescribing and management decisions, leading to better patient outcomes.

RECENT FINDINGS: This updated review provides context on how academic detailing programs can improve diabetes-related clinical knowledge and practice among primary care providers, incorporating the perspective of a field-based academic detailer. It also profiles 4 diabetes-specific academic detailing programs varying in geographic scope and detailing approach, based in Massachusetts, Pennsylvania, Vermont, and Saskatchewan Province (Canada). Academic detailing can effectively overcome challenges to increasing the evidence-based use of newer glucose-lowering medications in primary care settings.

BACKGROUND: Hospitalizations contribute significantly to the annual health care expenditure for inflammatory bowel disease (IBD), and reducing cost of care without compromising outcomes is a rising priority. Teaching hospitals (THs) have higher costs and utilize trainees in care to a greater extent than community hospitals, and it is unknown how hospital teaching status (HTS) affects outcomes. We therefore sought to investigate the impact of HTS on IBD hospitalization outcomes.

METHODS: We used the Vizient clinical database to identify patients hospitalized between October 1, 2014, and March 31, 2018, for IBD. Vizient hospitals were divided into major THs, minor THs, and non-THs. We used multivariable linear regression of aggregated discharge data to assess the association of HTS with mean length of stay (LOS), mean direct cost (DC), 30-day readmission rate (RR), and in-hospital mortality rate (MR), while adjusting for demographics and disease complexity.

RESULTS: Vizient included 29,863 discharges among 291 hospitals for ulcerative colitis (UC) and 62,698 discharges among 314 hospitals for Crohn's disease (CD) between October 1, 2014, and March 31, 2018. Unadjusted mean LOS, mean DC, and 30-day RR were greater among THs for both UC and CD. Unadjusted MR was greater among major THs for UC but not CD. After multivariable analysis, only 30-day RR for UC was increased in major THs relative to non-THs (1.98%; 95% confidence interval, 0.33%-3.61%).

CONCLUSIONS: Differences in metrics of cost-effective hospital care for patients with IBD appear to be driven by disease severity rather than HTS. Future research should attempt to better characterize factors driving resource utilization for IBD hospitalizations.

It is unknown to what extent rising drug costs are due to inflation in the prices of existing drugs versus the entry of new products. We used pricing data from First Databank and pharmacy claims from UPMC Health Plan to quantify the contribution of new versus existing drugs to the changes in costs of oral and injectable drugs used in the outpatient setting in 2008-16. The costs of oral and injectable brand-name drugs increased annually by 9.2 percent and 15.1 percent, respectively, largely driven by existing drugs. For oral and injectable specialty drugs, costs increased 20.6 percent and 12.5 percent, respectively, with 71.1 percent and 52.4 percent of these increases attributable to new drugs. Costs of oral and injectable generics increased by 4.4 percent and 7.3 percent, respectively, driven by new drug entry. The rising costs of generic and specialty drugs were mostly driven by new product entry, whereas the rising costs of brand-name drugs were due to existing drug price inflation.

Polypharmacy has been linked to adverse outcomes including increased risk of hospitalization, falls, and death and contributes to unnecessary healthcare spending. Deprescribing efforts aim to reduce medication burden while improving or maintaining patients' quality of life. While the practice of deprescribing is gaining momentum, quality measurement and provider reimbursement are barriers that must be addressed for deprescribing to achieve widespread adoption. Because many quality measures are focused on medication use and adherence, deprescribing efforts may negatively impact primary care provider and health plan quality ratings and value-based reimbursement. In addressing this conflict, there are opportunities to proactively align the priorities and incentives of patients, providers, and plans to promote deprescribing. In this report, we propose several actionable steps to address quality and reimbursement-based barriers such as facilitating the exclusion of those engaged in deprescribing efforts from quality measures and the development of deprescribing-based quality measures.

BACKGROUND: Value-based contracts (VBCs) between payers and pharmaceutical manufacturers link drug payments to predefined performance measures and require shared risk between both entities. It is unclear how outcome measures were selected in previously reported VBCs, and many VBCs have focused on surrogate endpoints often used in the conduct of clinical trials, which may not be valued by or of importance to patients.

OBJECTIVE: To identify outcome measures that are meaningful to key stakeholders and feasibly measured to inform VBCs for diabetes medications.

METHODS: We conducted a modified Delphi survey to incorporate views from patients (n = 9), endocrinologists (n = 5), primary care physicians (n = 4), payers (n = 3), pharmacy benefit managers (n = 3), and pharmaceutical company representatives (n = 2). A list of 12 diabetes-related outcome measures was generated from the literature and consultations with subject matter experts. Participants rated the importance of each outcome on a 5-point Likert scale and selected the 3 most meaningful outcomes. Nonpatient participants then used a Likert scale to rate the feasibility of collecting each outcome. Consensus was defined as ≥ 75% agreement on the importance and feasibility of an outcome (Likert scores 4 or 5 or selection of an outcome as most meaningful). A 2-sample test of proportions was performed to examine differences between patient and nonpatient stakeholder rankings of outcomes.

RESULTS: All 12 outcomes reached consensus for importance on the Likert scale. The measure "reducing risk of heart attacks" was the most meaningful outcome (84%), while "reducing A1c levels" ranked second (68%). The 2 measures rated as most feasibly collected were "reducing A1c levels" and "reducing risk of hospitalizations from diabetes" (93.8% each). The measures "weight loss," "reducing risk of diabetes-related kidney disease," "reducing risk of emergency room visits from diabetes," and "reducing risk of diabetes-related amputations and foot ulcers" also reached consensus for feasibility. There were statistically significant differences between patient and nonpatient stakeholders in the selection of "reducing A1c levels" (37.5% vs. 82.3%, respectively; P = 0.03) and "reducing risk of diabetes-related kidney disease" (50.0% vs. 11.8%, respectively; P = 0.03) as most meaningful outcomes.

CONCLUSIONS: The measures "reducing risk of heart attacks" and "reducing A1c levels" were identified as top priority diabetes outcome measures.

DISCLOSURES: Express Scripts provided research funding for this study to the UPMC Center for Value-Based Pharmacy Initiatives. Henderson is employed by Express Scripts and was involved in the conception and design of the study and manuscript approval. The other authors are employed by the UPMC Center for Value-Based Pharmacy Initiatives and have nothing to disclose.

PURPOSE: Adverse drug events (ADEs) in the U.S. Department of Veterans Affairs (VA) were evaluated, and differences in age group report rates and reported medications in different age groups were assessed.

METHODS: We utilized the VA Adverse Drug Event Reporting System (ADERS) to assess 10-year age groups regarding ADE reporting rates, event severity, and associated reported medications. Data were derived from 484,351 ADE reports from 395,703 patients included in VA ADERS from 2009 through 2016.

RESULTS: Reported rates of ADEs per 10,000 unique users demonstrated a nonlinear relationship with age, peaking in the group aged 60-69 years (148.6 reports/10,000 unique users) and declining thereafter. However, the percentage of adverse events reported as severe consistently rose with age group (3% in patients age 20-29 years versus 6% in patients older than 90 years). The types of medications reported as causative agents shifted over time from predominantly mental health and pain medications in younger veterans (e.g., age 20-29 years) to medications for chronic diseases in older cohorts (e.g., age 60-69 years).

CONCLUSION: An analysis of VA ADE reports revealed a nonlinear relationship between age and events, with events peaking at age 60-69 years. Rates of severe ADEs increased in older age groups. Drugs commonly associated with ADEs tended to be those primarily used for mental health and pain treatment in younger patients and those used to address chronic disease states in older patients.