Part D Redesign Would Reduce OOP Costs Across Non-LIS Beneficiaries
Summary
New Avalere analysis finds Part D redesign via PDPRA would reduce average OOP costs by 23% for non-LIS beneficiaries, especially for certain drug classes and beneficiary groups by race and reason for entitlement.In July 2020, the Senate Finance Committee reintroduced the Prescription Drug Price Reduction Act (PDPRA), which included provisions that would redesign the Medicare Part D benefit. This redesign addressed non-low-income subsidy (non-LIS) beneficiary out-of-pocket (OOP) costs in several ways: it would eliminate the coverage gap; reduce coinsurance in the initial coverage phase from 25% to 20%; allow smoothing for beneficiaries to distribute OOP costs throughout the year; and create a cap on annual beneficiary OOP costs at $3,100.
Avalere analysis indicates that the changes to the Part D benefit design would reduce OOP costs for non-LIS beneficiaries by an average of 23% compared to current law. This reduction was seen across non-LIS beneficiary groups by race, reason for entitlement, and urban/rural households. For example, when stratifying the reductions by race, the analysis found that minority groups would also have OOP reductions of 22% for Asian, 25% for Black, 25% for Hispanic, and 26% for North American Native beneficiaries. In addition, reductions in annual non-LIS OOP costs for drugs in certain therapeutic areas would be even greater.
For many therapeutic areas, a larger-than-proportional share of the reductions in non-LIS OOP costs under PDPRA would be received by Black and Hispanic beneficiaries. Though approximately 6% of non-LIS beneficiaries are Black, these beneficiaries would receive a 21% share of the total OOP cost reductions for antiretrovirals, 9% for antineoplastics and adjuvants, and 8% for MS agents. Similarly, Hispanic beneficiaries would receive a higher share of OOP cost reductions for antiretrovirals (3%) than their share of non-LIS Part D enrollment (1%).
In addition, beneficiaries originally entitled to Medicare due to disability also disproportionately benefit from reductions in non-LIS OOP costs. Despite representing 13% of non-LIS enrollment, disabled beneficiaries would receive 19% of the share of reductions in non-LIS OOP for all drugs, 18% for antidiabetics, 15% for antineoplastics, 62% for antiretrovirals, and 71% for MS agents.
A large body of research has identified relationships between OOP costs for prescription drugs, treatment adherence, and health outcomes.1 In addition, non-adherence to treatment can have a significant impact on patient outcomes, resulting in higher costs of care, disease progression, and adverse events.2 As policymakers further consider reforms to Part D, assessing the impact of reforms on different patient populations, based on disease/condition, race, and reason for entitlement is an essential step to understanding all the possible impacts on access, affordability, health outcomes, and health disparities.
Funding for this research was provided by Bristol Myers Squibb. Avalere Health retained full editorial control.
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Methodology
Avalere used 2019 Medicare Part D Drug Event data accessed under a research-focused data use agreement (DUA) with the Centers for Medicare & Medicaid Services (CMS) to simulate the Part D benefit for 2023 under both current law and the PDPRA. Avalere did not include the impact of the Office of Inspector General’s Revisions to the Safe Harbors Under the Anti-Kickback Statute final rule due to pending litigation and uncertainty of implementation under the Biden administration.
Per the DUA’s requirement, Avalere identified a random sample representing less than 20% of the total Part D population. Avalere excluded the following populations from the analysis: those for which benefit design data was missing, including those with claims paid by an Employer Group Waiver Plan; those residing outside the 50 states and DC; and those enrolled in the Limited Income Newly Eligible Transition program.
Avalere arrayed each beneficiary’s Part D claims chronologically based on prescription fill dates. We then used Part D formulary and benefit design data to identify formulary coverage for each claim based on National Drug Codes (NDCs) and determined tier placement and cost sharing for each drug on formulary. In addition, we identified the LIS status for each claim based on the monthly LIS cost-sharing group.
To model the current law benefit design, Avalere deflated 2023 benefit parameters to 2019 dollars using annual percentage increases in Part D per capita benefits to account for policy changes to the calculation of the catastrophic threshold under current law. Avalere used 2019 negotiated prices and assumed scripts filled at in-network preferred pharmacies in determining beneficiary OOP costs. Avalere also assumed 2019 plan-specific formulary coverage, tier placement, and cost sharing. For non-formulary drugs, Avalere assumed placement on the non-specialty tier with the highest cost sharing (often non-preferred tier). LIS status for each member month and cost sharing for scripts filled in those member months were reduced to statutory limits.
To model the PDPRA, Avalere applied plan-specific deductibles, continued initial coverage benefit design to an OOP cap, set the OOP cap equal to $3,100 in 2023 dollars, and changed cost sharing in initial coverage to 20%.
Avalere did not model behavioral changes that may result from these 3 proposals. Avalere used 2-digit Generic Product Identifiers from the Medispan® database to identify products in each drug grouping by NDC. Results from this sample are weighted to reflect the broader Part D population based on their share of total enrolled member months. Avalere inflated all outputs to reflect drug cost and enrollment growth using data from CMS’s Final Call Letters and the 2020 Medicare Trustees Report.
Notes
- Chandra A, Flack E, and Obermeyer Z. The health costs of cost sharing. NBER Working Paper Series 2021.2. doi:10.3386/w28439.
- Cutler RL, Fernandez Llimos F, Frommer M, et al. Economic impact of medication non-adherence by disease groups: a systematic review. BMJ Open 2018;8:e016982. doi:10.1136/ bmjopen-2017-016982
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