The Impact of MFN on Oncology and Hematology Treatments
Bowen, H. P.; O'Loughlin, G.; Drake, C.; Schleicher, C.; Schulthess, D.
Show abstract
BackgroundThe Most Favored Nation (MFN) policy is a mechanism that incorporates foreign prices to determine the maximum allowable net price for any branded drug within US government-funded healthcare. Two proposed rules, the Global Benchmark for Efficient Drug Pricing ("GLOBE") (90 Fed. Reg. 60,244) for Medicare Part B and the Guarding US Medicare Against Rising Drug Costs ("GUARD") (90 Fed. Reg. 60,338) for Medicare Part D, invoke the Center for Medicare and Medicaid Innovation Centers payment and service model demonstration and waiver authority, under Section 1115A of the Social Security Act (42 U.S.C. [§] 1315a), to calculate the US MFN price which is the lowest average price within a basket of specified foreign countries. Unlike voluntary manufacturer agreements, GLOBE and GUARD would mandate participation from all applicable manufacturers. MethodsWe derive MFNs potential impact on Medicare pricing from a proprietary dataset provided by IQVIA which contained net prices for the top 37 oncology products by total US sales from January 1, 2019 through June 30, 2025 ranked by total US sales in the following countries: Australia, Belgium, France, Germany, Ireland, Italy, South Africa, Spain, Switzerland, the UK, and the US. For each drug, we select the lowest GDP-adjusted international price from a basket of those countries within 60% of the US GDP per capita, adjusted for purchasing power parity, and calculate the reduction in US price required to match its MFN price, and hence the corresponding reduction in revenues under MFN. A retrospective Net Present Value (NPV) analysis is then used to address the counterfactual question of whether each drug would have been developed had MFN pricing been in place at the time of its FDA approval. ResultsUnder MFN, the average reduction in US prices across our drug cohort was 67%. Eighty-four percent of the 37 cancer drugs in our cohort evidenced a negative NPV if MFN had been in place at the time of their FDA approval and the commercial market is impacted. When the analysis is restricted to MFNs impact on Medicare, the indications for these lost drugs have a total US population of 2.4 million patients. When the analysis is combined across the Medicare and commercial markets, the loss of lead indications impacts over 15 million US patients. ConclusionsMandatory MFN policies reduce the financial incentives required to develop cancer medicines; our projections show a substantial decline in new cancer drug launches and will likely lead companies to pursue indications for populations outside Medicares authority. If so, MFN will reduce the number of new therapies for the very population the Executive Orders are allegedly designed to aid: the Medicare-aged population who require effective new therapies in areas of high unmet medical need, such as late-stage cancers. This creates the perverse outcome of a policy nominally designed to help Medicare beneficiaries by instead redirecting innovation away from their most urgent therapeutic needs.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Comparison of healthcare resource use and cost between influenza and COVID-19 vaccine coadministration and influenza vaccination only 91%
- The Potential Clinical Impact of Implementing Different COVID-19 Boosters in Fall 2022 in the United States 88%
- Clinical Benefits and Budget Impact of Lenzilumab plus Standard of Care Compared with Standard of Care Alone for the Treatment of Hospitalized Patients with COVID-19 in the United States from the Hospital Perspective 88%
Similar papers in this journal
- Time trends between vaccination coverage and voting patterns before and during the COVID-19 pandemic: analysis of COVID-19 and flu surveys in the United States 88%
- Geospatial disparities in federal COVID-19 test-to-treat program 87%
- Lost Opportunity in Tobacco Cessation Care: The Impact of Underbilling in a Large Health System 87%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.