John Kautter

Impact of Multitiered Copayments on the Use and Cost of Prescription Drugs Among Medicare Beneficiaries

OBJECTIVES To assess the impact of multitiered copayments on the cost and use of prescription drugs among Medicare beneficiaries. DATA SOURCES Marketscan 2002 Medicare Supplemental and Coordination of Benefits database and Plan Benefit Design database. STUDY DESIGN The study uses cross-sectional variation in copayment structures among firms with a self-insured retiree health plan to measure the impact of number of copayment tiers on total and enrollee drug payments, number of prescriptions filled, and generic substitution. The study also assesses the effect of enrollee cost sharing on the cost and use of prescription medications for the long-term treatment of chronic conditions. DATA COLLECTION METHODS We linked plan enrollment and benefit data with medical and drug claims for 352,760 Medicare beneficiaries with employer-sponsored retiree drug coverage. PRIMARY FINDINGS Medicare beneficiaries in three-tiered plans had 14.3 percent lower total drug expenditures, 14.6 percent fewer prescriptions filled, and 57.6 percent higher out-of-pocket costs than individuals in lower tiered plans. They also had fewer brand name and generic prescriptions filled, and a higher percentage of generics. The estimated price elasticity of demand for prescription drug expenditures was -0.23. Finally, for maintenance medications used for the long-term treatment of chronic conditions, members in three-tiered plans had 11.5 percent fewer prescriptions filled. CONCLUSIONS Higher tiered drug plans reduce overall expenditures and the number of prescriptions purchased by Medicare beneficiaries. Beneficiaries are less responsive to cost sharing incentives when using drugs to treat chronic conditions.

Improvements in Medicare Part D risk adjustment: Beneficiary access and payment accuracy:

Introduction:The continued success of the Medicare Part D program is contingent on appropriate Medicare payment adjustments for the projected drug costs of Part D plan enrollees. This article describes a major revision of these “risk adjustments,” intended to more accurately match payments to costs, especially for high-cost, disadvantaged populations. Methods:For the first time actual Part D data are used to calibrate risk adjustment. The sample is Medicare beneficiaries with fee-for-service enrollment in 2007 and Part D standalone prescription drug plan enrollment in 2008 (N=14,224,301). Part D plan liability expenditures are predicted using demographic and diagnostic factors in a weighted least squares regression. Models for Medicare subpopulations are analyzed. The predictive accuracy of risk adjustment models is evaluated using R2 and predictive ratio statistics. Results:Based on differences in both mean expenditures and incremental expenditures by diagnosis, separate Part D risk adjustment models are calibrated for 5 Medicare subpopulations: aged not low income; aged low income; nonaged not low income; nonaged low income; and institutionalized. The variation in plan liability drug expenditures (R2) explained by these models ranges from 13% to 29%. The 5 separate models accurately predict mean plan liability expenditures ranging from

Financial and Quality Impacts of the Medicare Physician Group Practice Demonstration

967 to

Utilization of genetic tests: analysis of gene-specific billing in Medicare claims data

1762 across subpopulations and account for differences in incremental disease coefficients by subpopulation. Conclusions:The refined Part D risk adjustment model represents a significant improvement in the accuracy and fairness of payment to Part D plans. The new model provides greater incentives for drug plans to compete for low-income and institutionalized enrollees.

Risk Transfer Formula for Individual and Small Group Markets Under the Affordable Care Act

OBJECTIVE To examine the impact of the Medicare Physician Group Practice (PGP) demonstration on expenditure, utilization, and quality outcomes. DATA SOURCE Secondary data analysis of 2001-2010 Medicare claims for 1,776,387 person years assigned to the ten participating provider organizations and 1,579,080 person years in the corresponding local comparison groups. STUDY DESIGN We used a pre-post comparison group observational design consisting of four pre-demonstration years (1/01-12/04) and five demonstration years (4/05-3/10). We employed a propensity-weighted difference-in-differences regression model to estimate demonstration effects, adjusting for demographics, health status, geographic area, and secular trends. PRINCIPAL FINDINGS The ten demonstration sites combined saved

The national market for Medicare clinical laboratory testing: implications for payment reform.

171 (2.0%) per assigned beneficiary person year (p<0.001) during the five-year demonstration period. Medicare paid performance bonuses to the participating PGPs that averaged

MINIMUM SAVINGS REQUIREMENTS IN SHARED SAVINGS PROVIDER PAYMENT

102 per person year. The net savings to the Medicare program were

21-Gene recurrence score testing among Medicare beneficiaries with breast cancer in 2010–2013

69 (0.8%) per person year. Demonstration savings were achieved primarily from the inpatient setting. The demonstration improved quality of care as measured by six of seven claims-based process quality indicators. CONCLUSIONS The PGP demonstration, which used a payment model similar to the Medicare Accountable Care Organization (ACO) program, resulted in small reductions in Medicare expenditures and inpatient utilization, and improvements in process quality indicators. Judging from this demonstration experience, it is unlikely that Medicare ACOs will initially achieve large savings. Nevertheless, ACOs paid through shared savings may be an important first step toward greater efficiency and quality in the Medicare fee-for-service program.

Competitive bidding for Medicare Part B clinical laboratory services

Purpose:We examined the utilization of precision medicine tests among Medicare beneficiaries through analysis of gene-specific tier 1 and 2 billing codes developed by the American Medical Association in 2012.Methods:We conducted a retrospective cross-sectional study. The primary source of data was 2013 Medicare 100% fee-for-service claims. We identified claims billed for each laboratory test, the number of patients tested, expenditures, and the diagnostic codes indicated for testing. We analyzed variations in testing by patient demographics and region of the country.Results:Pharmacogenetic tests were billed most frequently, accounting for 48% of the expenditures for new codes. The most common indications for testing were breast cancer, long-term use of medications, and disorders of lipid metabolism. There was underutilization of guideline-recommended tumor mutation tests (e.g., epidermal growth factor receptor) and substantial overutilization of a test discouraged by guidelines (methylenetetrahydrofolate reductase). Methodology-based tier 2 codes represented 15% of all claims billed with the new codes. The highest rate of testing per beneficiary was in Mississippi and the lowest rate was in Alaska.Conclusions:Gene-specific billing codes significantly improved our ability to conduct population-level research of precision medicine. Analysis of these data in conjunction with clinical records should be conducted to validate findings.Genet Med advance online publication 26 January 2017

Underutilization and disparities in access to EGFR testing among Medicare patients with lung cancer from 2010-2013

The Affordable Care Act provides for a program of risk adjustment in the individual and small group health insurance markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the third of three in this issue of the Medicare & Medicaid Research Review that describe the ACA risk adjustment methodology and focuses on the risk transfer formula. In our first companion article, we discussed the key issues and choices in developing the methodology. In our second companion paper, we described the risk adjustment model that is used to calculate risk scores. In this article we present the risk transfer formula. We first describe how the plan risk score is combined with factors for the plan allowable premium rating, actuarial value, induced demand, geographic cost, and the statewide average premium in a formula that calculates transfers among plans. We then show how each plan factor is determined, as well as how the factors relate to each other in the risk transfer formula. The goal of risk transfers is to offset the effects of risk selection on plan costs while preserving premium differences due to factors such as actuarial value differences. Illustrative numerical simulations show the risk transfer formula operating as anticipated in hypothetical scenarios.