Thread by @wbfeldman

📄📄New article in @JAMAHealthForum 📄📄

We take a deep dive into the question of how to estimate rebates and other discounts received by Medicare #PartD.

With @bnrome, #VeroniqueRaimond, #JoshGagne, @akesselheim @PORTAL_Research

Thread below.

ja.ma/3z5mAx6

1/ First some background.

There are 2 broad categories of discounts in Medicare #PartD: direct and indirect remuneration (#DIR) and coverage gap discounts (#CGD).

2/ #DIR includes confidential rebates that drug manufacturers pay to #PartD plans and #PBMs, often to secure favorable formulary placement. #DIR also includes discounts paid by pharmacies to plans and #PBMs—e.g., to be in the plan’s preferred network.

3/ #CGD, by contrast, refers to statutorily required price reductions that drug manufacturers must pay when beneficiaries are in the coverage gap (i.e., “the donut hole”). Beneficiaries reach the coverage gap when their drug costs in a year exceed a threshold (currently $4130).

4/ Why should we care so much about these confidential discounts?

Because they are sizeable--and getting bigger.

Failure to account for them in analyses of Medicare #PartD leads to an overestimation of spending.

5/ The problem is that there has been little agreement in the literature on how to handle confidential discounts. We review existing methods and identify problems with each.

6/ We settle on an approach—which we dub the brand-name summed discounts approach—that relies on publicly available data and a few simple steps.

Step 1: We obtain #DIR from annual Medicare Trustees Reports and #CGD from CMS reports and/or @medicarepayment reports.

7/ Step 2: We assume that these discounts are for brand-name drugs only (not a perfect assumption, but close).

But we must then figure out the denominator for this calculation: namely, gross brand-name spending in Medicare #PartD.

8/ We can do this the hard way—by manually adding up all spending on brand-name drugs reported by CMS.

cms.gov/Research-Stati…

9/ Or we can do this an easier (but still accurate) way by adding up total gross spending and then determining the % gross brand-name spending from Trustees Reports.

We show both ways in the paper.

10/ Step 3: Divide the sum of #DIR + #CGD over gross brand-name spending.

11/ Here, you can see how our brand-name summed discounts approach compares to other methods that have been used in the literature.

ja.ma/3z4p2ns

12/ The validity of this approach is perhaps best appreciated via comparison to a recent @USCBO report. The @USCBO had access to confidential information in Medicare #PartD and reported discounts of 35% for 2017, compared to 34% in our paper.

cbo.gov/publication/56…

13/ But, of course, not all drugs get the same rebates. Using data from the @USGAO on class-specific #DIR from 2016, we also show how #CGD may be added to reported class-specific #DIR to estimate class-specific discounts.

14/ The brand-name summed discounts approach is by no means a cure-all. It does not provide drug-specific discounts, b/c no publicly available data exist for doing so. But it helps provide a clearer picture of net Medicare #PartD spending.

15/ Using this approach, we see that total gross #PartD spending increased 38% from $121 billion in 2014 to $168 billion in 2018, while net spending increased just 21% from $99 billion to $119 billion (compared with 6% inflation during the time period).

16/ This gap between gross and net spending was due to rising discounts (#DIR + #CGD), which increased 117% from $22.5 billion in 2014 to $48.9 billion in 2018.

17/ With an aging US population, the number of beneficiaries covered by Medicare #PartD increased from 37.7 million in 2014 to 44.2 million in 2018.

Net spending per beneficiary thus remained flat, increasing just 3% from $2622 to $2694, which was below the rate of inflation.

18/ We hope that this paper will be useful for other groups working on Medicare #PartD—at least until there is more transparency in the drug-pricing system!

19/ A big thanks to my co-authors @bnrome, #VeroniqueRaimond, #JoshGagne, @akesselheim, to #JerryAvorn for comments on an earlier draft, and to all the great folks in @PORTAL_Research for many helpful discussions on this.