CMS Says AMP is Coming; Acquisition Cost Data Reduce Generic Costs 37% in ‘Bama; Why Ortho Demand May Slow in the Mid to Longer-Term


CMS confirmed to us their intention to publish average manufacturer price (AMP) data to the general public; this is in addition to Secretary Sebelius’ February 2011 commitment to provide average acquisition cost (AAC) survey data to the states

We analyze Alabama’s publicly available AAC dataset, and show that AAC values are on average 87% below AWP, and 74% below Federal upper limits (FUL)

By switching to AAC, we show that Alabama saved an average of 37% per generic prescription, even after nearly doubling pharmacists’ dispensing fees. Potential savings are comparable in other states, all of whom are likely to move away from AWP

AWP’s relevance is fading; no state is likely to use the benchmark for much longer, and we expect the two major providers of pricing data to stop publishing AWP – First Databank in September of this year, and Medi-Span as soon as either AAC or AMP is published by CMS

The fading of AWP may force the re-benchmarking of commercial (i.e. PBM) contracts, practically all of which currently are benchmarked to AWP.  We’re increasingly convinced that PBM contracts will shift to acquisition cost benchmarks, and that generic dispensing margins compress as a result

We analyze hospital discharge data over a 7 year period, and show that much of ortho demand growth – especially for hips – came from growing numbers of elective implants in ever younger patients. We also show that like spinal surgery, where payors are tightening eligibility standards, that knee and hip implant rates vary significantly across regions. At a minimum patients cannot keep getting younger indefinitely, and there is a non-trivial risk of payor tightening. Cardio demand faces neither of these pressures; nevertheless cardio and ortho expectations and valuations are nearly identical

Correction: At the end of our March 2, 2011 call “Post-2014 Reform-Related Volume Gains” we misidentified our “median case” as an uninsured rate of 18 percent. In fact, our median case should have been 15.7 percent uninsurance. Correcting this error shifts the modeled median case gross profit by less than 0.5 percent (from 5.6 percent to 6.0 percent) and does not change any of our conclusions

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