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Specialty Pharmaceuticals in Health Affairs, Part 1

10.cover The October issue of Health Affairs is on speciality pharmaceuticals - those expensive prescription drugs that often find themselves in the news due to the financial burden they put on patients and payers while offering the promise of healing severe medical conditions. Here are a few articles that caught my attention. (Also, given the topic of this post, maybe my readers will be interested in a whitepaper I wrote about the Minimum Viable Market Share (MVMS) of stratified medicine in various market settings, to extend work by Mark Trusheim and co-authors in Personalized Medicine. Even if you don't like math, the tables should be relatively easy to understand since I've color-coded the MVMS based on how easy or difficult it would be for a pharmaceutical company to reach that threshold in market share.)

The impact of speciality pharmaceuticals as drivers of health care costs (B. Hirsch, S. Balu, K. Schulman, all from Duke). Excerpt: "Currently, 86 percent of prescriptions in the US market for small-molecule agents are for generic medications. This is an astonishing change from 1995, when only 40 percent of retail prescriptions were for generic medications. The industry therefore sees speciality pharmaceuticals as a way to offset losses from brand-name small molecules' losing patent protection." This paper is very good and very interesting for many reasons. Below, I'll mostly talk about the very basic mathematical model and Health Affairs's policy of relegating even the simplest math statement in its dreadful online appendix system, but it is only a small part of the paper. If you don't care about that, you can skip to the paragraph that starts with the header: "Going back to the paper".

The authors describe a simple economic model they created to evaluate the impact of specialty pharmaceuticals. In their words: "Let us assume that every person covered by a hypothetical insurance plan had a yearly out-of-pocket medical expense of $3,500 to cover his or her premiums, absent the use of any specialty pharmaceutical. The derivation of this value is presented in the online Appendix, as are the underlying assumptions of the cost model."

What you have to understand about Health Affairs (which is the leading journal in health policy that everyone dreams to publish in) is that it puts everything that remotely smacks of data or (always basic) models in an appendix, so that the paper itself only contains the high-level setup of the study, some tables and figures, attempts at analysis, and conclusions. But the online Appendix is, per Health Affairs guidelines (not the authors' fault), a double-spaced Word file formatted in 12pt Courier font, aka typewriter font, and if you've never seen what it looks like you really have to give it a try right now to get an idea of the ugliness of it. When you see a file formatted like that, you really don't want to read it. Health Affairs's idea of data is usually R-square values for regression or some really basic statistic concept that everybody in the health policy sphere seems to agree shouldn't be in the main paper, thus making it harder to discuss the authors' basic models once they have been green-lighted for publication.

Keeping the quantitative details of a study away from the main paper and in a format that only the most dedicated readers like yours truly will want to put up with helps feed the idea to non-quantitative policy-makers that anything about math is better left unseen (and frankly, as much as I do love Health Affairs and have learned a lot from reading it, which I can't say of any other journal, the mathematical models in the journal are extremely simple, usually focused on data analysis, sometimes using regression tools. The hardest part always seems to get your hands on the data. This is not to say that I don't like the authors' models, in fact I find them very interesting. But I think Health Affairs should make its policy to include more details about the models in the paper itself to foster discussions within the community. If this sounds anathema to policy-makers, then maybe the abstract can be extended for their sake.

In the present case, let's first talk about the $3,500 value, which the authors justify in the online appendix rather than the paper itself. It turns out they took a number from the 2013 Kaiser Family Foundation survey of $16,351 per family, assumed an average family size of 4 (which, I think, is an overestimate, since the US Census puts the average family size at 2.58, but maybe the authors assume insured people are more likely to have children? they never explain that), and a medical loss ratio of 0.85. We have 16,351*0.85/4=3,474 rounded up to $3,500. (And if they had picked 2.58, it would have been almost $5,400.) When you reach a situation where the authors feel compelled somehow to put 16,351*0.85/4=3,474 (which technically they don't really put, they just give the 16,351 and the 0.85 and the 4 separately) in an online appendix, you know you have a long way to go before data is accepted by policymakers, although this is not the authors' fault.

The authors assume that cost to payers and patients is $100,000 per patient and enrollment drops by 1% for every 10% increase in the cost. (It would have been good if they could have justified this number, which they never do.) The statement that "health care costs would be expected to increase by $250 for every o.25 percent of the population using the speciality pharmaceutical or $1,000 for every 1 percent increase in utilization" follows from the fact that the costs would increase by $100,000 times the prevalence in the population. If you inject prevalence = 0.25% you get $250 in cost increase or 7.14% compared to the initial $3,500 amount, although the authors curiously put the percentage increase at 6%. (This may be a typo, because the numbers for 1% prevalence do amount to a 29% cost increase as stated.) And, that's it for the online appendix!

Because there is a 29% cost increase at 1% prevalence, enrollment drops by 2.9% given the authors' assumption of 1% enrollment drop per 10% cost increase. This means that premiums must increase by 2.9% to cover the enrollment drop. In the article, the authors then show Exhibit 2 (Rate and Percent Increase in Insurance Premiums for a New Specialty Drug Costing $100,000 per Treated Patient Depending on Disease Prevalence.) So while the authors had warned their model was "intentionally simplistic", the online appendix ended up a little underwhelming, to say the least.

Again, that's not a comment on the authors, but on Health Affairs's philosophy. (Basic models can provide very valuable insights into a topic. There's no need to go right away to a complex model if a basic model can easily give us the big picture.) Realistically, the math involved is really very basic multiplication and division of numbers (not unknowns or parameters) of the type you learn in, what, middle school. That sort of math should not be relegated to an online appendix in Courier font that doesn't even look like it belongs to the same journal, not being typeset in the journal's template.

And what happens if we use the average family size of 2.58? $1,000 is then only 19% of the baseline cost, leading to a premium increase of 1.9% or about one third lower than what the authors state. But since the authors forgot to reference their "1% dropout per 10% cost increase" number, it doesn't mean the 1.9% premium increase is any more meaningful than the 2.9% in Exhibit 2 of the paper.

Going back to the paper

The authors also point out that those increased costs could be mitigated by decreased hospital admissions. They then investigate cost-sharing through copayments and co-insurance and discuss the influence of section 340B of the Veterans Health Care Act of 1992. They explain: "The purpose of the [340B] program is to ensure access to pharmaceutical agents at safety-net hospitals." Following the Omnibus Budget Reconciliation Act (OBRA) of 1990, "pharmaceutical companies were less willing to provide aggressive discounts to safety-net hospitals" because the prices those hospitals received were now used to establish benchmarks, for the rebates that OBRA required pharmaceutical companies to offer state Medicaid programs. The 340B program ensured that "prices under this program would not be considered in establishing Medicaid rebates under OBRA" for hospitals providing high levels of uncompensated care. This has led to 30-50 percent discounts off the market price. There is thus an "enormous financial benefit" in being enrolled, and in fact, one-third of all US hospitals now participate in this program. 

Yet, the authors state, "The 340B regulations do not limit the application of discounts received by hospitals to medications used in the care of indigent patients, nor do they require hospitals to pass their cost savings along to payers or patients." The program is now expected to cost $12 billion by 2016 and may lead to manufacturers increasing their prices "to compensate for the loss in revenues related to the program." The authors provide convincing examples as to why this "may have negative economic effects on patient care."

Then they discuss biosimilars and price competition, especially given the expected loss of patent protection for several biologic agents in 2013-18. They observe that: "This approval pathway [for specialty pharmaceuticals] is not subject to the generic drug provisions of the Drug Price Competition and Patent Term Restoration Act of 1984... so there is not a standard pathway for competitors to enter the market once a specialty pharmaceutical's patent expires." They point out, however, that the Affordable Care Act "included provisions for the approval of biosimilar products" but also that "in 2010 the Congressional Budget Office estimated that biosimilars would yield only a 2 percent reduction in pharmaceutical costs by 2019."

If there is only one message to keep from this well-written and well-researched paper, it is that specialty pharmaceuticals present valuable opportunities for new innovation pathways but there is an urgent need for far-ranging discussions on their implications in the health care market.


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