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0 comments on “Tackling Rising Drug Costs in the US: Is ERP an Option?”

Tackling Rising Drug Costs in the US: Is ERP an Option?

The rising cost of pharmaceuticals is a global trend and, quite simply, it makes sense considering we are now able to modify a person’s genes to treat disease. And with this great power comes great…prices. However, questions surrounding to what extent such drug price increases are justified are becoming more frequent.

The Institute for Clinical and Economic Review (ICER) has released its first annual Unsupported Price Increase report, which is likely to further fuel the drug pricing debate. After reviewing nine US prescription drugs which had substantial price hikes, the report suggested that for seven out of nine drugs there is no new clinical evidence to support these increases. In the US, the problem with high treatment costs is compounded by the disparity between these prices in the country compared with other developed nations. As drug pricing is very much a political issue, it follows that as the US 2020 elections approach, the two words on every politicians’ lips are “healthcare” and “costs”. The fight between the Republicans and Democrats to prove their commitment to tackling prescription drug costs has intensified, with the Democrats releasing their final bill proposal on the topic.

Ultimately, if this proposal was passed it would give the Secretary of Health and Human Services the capacity to negotiate prices of the top 250 Medicare Part B and D drugs which present the greatest total cost to the US healthcare system and do not have generic competition. According to the plan, an upper limit for a drug price would be based on “no more than 1.2 times the volume-weighted average of the price of six countries (Australia, Canada, France, Germany, Japan and the United Kingdom)”. The price determined by the negotiation process would be available to all beneficiaries – not just beneficiaries of Medicare, which would likely have significant nationwide ramifications.

Since the details of the proposed external reference pricing (ERP) model were provided in the proposal, at Inbeeo we thought it would be interesting to put together a few back-of-the-envelope calculations to test the potential effectiveness of this approach in controlling US drug prices.

Five drugs which cause significant costs to Medicare (and therefore likely the whole US healthcare system) were selected from the Part B and D Drug ‘spending dashboard’. These drugs were selected based on the total spending in 2017 by Medicare and the availability of pricing data (plus partly random choice). A potential maximum ERP price for each drug, based on 1.2 times the volume-weighted average, from the six referenced countries was then calculated. We used publicly available pricing data (see end of article of specific sources) for each country – a mixture of list prices and manufacturer prices, therefore note that the potential ERP is an estimate. The volume-weighted average was based on the relative population sizes of each country. This calculated ERP price was then compared with the US FSS price to get a rough idea of the potential savings that could be made.

FSS prices, which are discounted prices negotiated by the Veterans Association and are available to all direct federal purchasers, are a lower and arguably more realistic benchmark for drug prices compared to list prices or wholesale acquisition costs (WAC) in the US. A report by Mattingly et al. which estimated US drug costs for select drugs based on manufacturer drug price transparency reports, suggested there can be substantial difference between the WAC and FSS prices, with rebates as high as 80-90% in some cases. We used FSS prices in our analysis to see whether referencing external prices could further lower the FSS price benchmark – or maybe the administration would be better off pursuing internal reference pricing?

The analysis demonstrated that for four out of the five prescription drugs the potential maximum ERP was lower than the current US FSS price. On average, the potential ERP was 35% lower than the FSS price, suggesting that reference pricing could be effective at driving down prices. The range of percentage differences was broad, with the highest difference observed for Januvia®, at -91%, and the lowest for Keytruda®, at 2%. Interestingly, estimated ERP prices were lower than current prices for both high-cost drugs, such as Revlimid® (57% difference), and cheaper drugs like Eliquis® (4% difference). Keytruda®, however, was the exception to the trend, with the estimated reference price 2% higher than the current US FSS price suggesting that ERP may not be an effective price control tool for some medicines.  

Translating these percentages into overall cost savings, we can see that certain drugs have the capacity to save Medicare several billions of dollars per year. For Revlimid®, a potential cost saving per year of almost USD 1.9 billion highlights the tremendous savings which could be made by Medicare as a result of implementing ERP in the US. Lower cost drugs, such as Januvia®, could also bring huge overall cost savings (an estimated USD 2.5 billion) given large prescribed patient populations.

ERP could be an effective cost-control tool for drugs causing some of the biggest dints in healthcare budgets, but is it the right approach to tackle the drug cost issue? The basket countries proposed by the Democrats to utilize in ERP have been criticized for their lack of comparability in terms of relative market orientation. Also, how well the model works to control individual drug prices is important, but the wider implications of this for the industry and across countries must be considered. Market launch delays, price convergence and price instability have been cited in the literature as potentially harmful outcomes of ERP on patient access. The other concern is that cutting pharmaceutical prices may decrease manufacturer revenues, which could lead to reduced innovation and development of new pharmaceuticals.

Overall, the Democrats proposal to use ERP to contain prescription drug costs in the US may be an effective solution for both high and low-cost therapies. Although, the degree to which ERP reduces individual drug prices is likely to vary on a case-by-case basis. Our analysis demonstrated that estimated maximum ERP prices for four out of five Medicare Part B and D drugs were considerably lower than current US FSS prices, with an average price difference of 35%. Patients who currently face excessive patient co-pays for prescriptions could be set to benefit from reduced prices for certain drugs, which also would fulfil politicians’ intentions to get voters on side. However, there are gaps in the evidence surrounding the overall quantifiable impact of ERP on health policy objectives. If the bill proposal in question is to be approved, I think a closer look at the impact of ERP beyond its effect on drug prices would be essential to assess the risk of compromising patient access in the long-term.

Price sources

US: Veteran’s Association FSS Price Database; UK: NICE BNF; Germany: G-BA Module 3A documents; France: here, Drug Basis and Pricing Information; Canada: CADTH Pharmacoeconomic Review and Economic Guidance documents; Japan: IHS Markit; Australia: Pharmaceutical Benefits Scheme website

References

Summary of the drug pricing proposal. Full version here.

CMS Office of Enterprise Data and Analytics (2018) Medicare Part D and Part B Drugs Spending Dashboard.

ICER (2019) Unsupported Price Increase Report.

Mattingly TJ et al. (2018) Estimating Drug Costs: How do Manufacturer Net Prices Compare with Other Common US Price References? Pharmacoeconomics. Available here.

Office of Health Economics (2019) Press Release. Available here.

Further Reading

Fontrier A. et al. (2019) International Impact of External Reference Pricing: Should National Policy-Makers Care? The European Journal of Health Economics.

Kang S. et al. (2019) Using External Reference Pricing in Medicare Part D to reduce drug price differentials with other Countries. Health Affairs.

Kanavos P. et al. (2017) The Impact of External Reference Pricing within and across Countries. London School of Economics.

Young K. et al. (2017) The perverse impact of external reference pricing: a comparison of orphan drugs affordability in 12 European countries. A call for policy change. Journal of Market Access and Health Policy.

0 comments on “4 Take-Away Messages From The Zolgensma® Pricing Storm”

4 Take-Away Messages From The Zolgensma® Pricing Storm

I’m sure by now everyone has seen the headlines –

“world’s most expensive therapy”, “astronomical”, “casino culture” 

The recent announcement by Novartis of the list price of its new gene therapy for Spinal Muscular Atrophy, Zolgensma®, has sparked a plethora of heated reactions over the past few weeks. $2.15 million was the figure. The story has definitely dragged the pricing and affordability debate back to the fore. One month later, it is time to look back at the story and try to answer one simple question – was this a storm in a teacup or a tropical cyclone?

Here are my 4 take-away messages from this story.

  • Time is of the essence

The sticker-shock that stemmed from the Zolgensma® list price reveal has eclipsed the question that really matters – the question of time. A 1, 2, 3 or 10 million investment doesn’t mean much to me if I have zero information regarding the timespan of my expected return. We have to state the obvious here that on the one hand, we have a gene therapy that is a one-time treatment requiring a considerable upfront investment, and on the other hand, we have a host of chronic therapies that require smaller investments spread over a period of time. Taken separately, those chronic treatments might seem more affordable, but cumulated over time, they can become far more expensive than Zolgensma®’s single shot. To me, the question of

‘for how long should we cumulate those chronic treatment costs to make a fair comparison between Zolgensma® and the current standard of care?’

trumps the question of the price tag. Novartis has opted for 10 years. In their press release, they claimed that the set price was “50% below 10-year treatment costs for genetic pediatric ultra-rare diseases (estimated at USD 4.4 million to USD 5.7 million)”. A bit of a cryptic claim in the absence of information relating to the products included in the analysis. So we mined Inbeeo’s pricing database to extract a sample of 10 products that might actually meet the criteria of comparability, including Biogen’s Spinraza®, as the only other disease-modifying therapy approved in SMA.

Orphan-Designated Products Selected from Inbeeo’s Pricing Database

We then estimated the cumulative treatment cost they would generate over different time periods –

Cumulative Cost of Treatment with Selected Orphan-Designated Products in the US

Broadly speaking, our analysis confirms Novartis’s claim of a 50% discount vs. other treatment costs for genetic pediatric rare diseases over 10 years. However, it also reveals that the variability of – and thus the uncertainty around – treatment costs grows considerably after 10 years. A 5-year comparison seems more reasonable, with Zolgensma®’s acquisition price being close to the median cost in our sample. Anything shorter than that puts Zolgensma® under a less favorable light. This is why time is of the essence – a gene therapy price tag is only relevant in the context of how durable will its benefit for the patient be.

  • Cash doesn’t grow on trees

The question of the considerable upfront investment that will bring benefits over time is not a trivial one. In business, it is called ‘cash flow management’, and there are companies going bankrupt every day from doing it poorly.

Novartis has somewhat anticipated the issue by announcing the possibility to fund the product by installments. But installments are a mere payment convenience and payers will need more reassurance before making the decision to fund Zolgensma®. Novartis anticipated that too and announced they were in advanced discussions with multiple payers to design outcomes-based agreements. But with a thin evidence package, this promises to be quite the Herculean work. Insurers are not known for designing insurance schemes that might actually generate a loss for them – not to mention the cost of opportunity at a time when many more advanced therapies are expected.

  • Pricing communication is a thing

Mobile phone networks, energy providers, automakers, gyms, software developers, accounting firms, consultants, and many more have long understood this one key success factor in business: the way you communicate the price of your products is as important as their actual price level.

Paying as little as $5 for a power yoga class sounds like a wiser investment than a $2,000 annual gym membership that includes ‘all-you-can-eat’ classes

and driving around town in the newest German car for $20 a day must be a bargain as compared to cutting a $40,000 check. They are the same product and at the end of the day will cost the customer the same amount of money, but the narrative affects the value perception.  Permanently. This is known as price anchoring, and it is pretty powerful. From the moment you have established a $2.15 million price anchor in the mind of your customers, it is difficult to move away from it.

  • No one is ready for what’s coming

With the not-so-great price anchoring, I suggested that Novartis was not fully prepared for the Zolgensma® story. But who is? Surely not payers. Let’s take a look at the dominant pricing frameworks in the US and the EU and try and assess their level of readiness.

In the US, the dominant model remains utilization management via brand tier placement, using negative financial incentives to nudge patients and prescribers towards preferred brands and generics. Co-insurance continues to be on the rise, meaning it becomes customary to charge patients 10%, 20% or more of the final bill. The sums at play with Zolgensma® are of course mind-boggling. And with Pharma companies blaming insurers for not passing the negotiated rebates on to the consumer, you can be sure this issue will not go unnoticed with a treatment like Zolgensma®. Of course, co-pay cards will come in as short-term fixes, but they cannot hide the deficiencies of a system at risk of losing all connection to the reality of value.

In Europe, the dominant framework remains that of the ICER, the incremental cost-effectiveness ratio. It is affected by some well-documented methodological issues, but if I oversimplify them to what are in my view the most acute ones, they are 2-fold. Firstly, the desire to summarize in one universal measure – the QALY – something which is incredibly personal and individual – outcomes related to chronic disease. And secondly, the way time and uncertainty are handled in cost-effectiveness models. Some recent product launches have clearly put those weaknesses in the spotlight, in particular in the orphan drug space. But assessing the lifetime cost-effectiveness of a $2.15 million product based on two single-arm open-label studies of 35 patients combined promises to be quite the challenge. I can only imagine the dramatic effects of the sensitivity analyses when varying the proportion of patients being able to walk or sit thanks to the treatment could take the ICER down from several million to virtually zero.

In a (difficult) attempt to wrap it up, it is clear that this is just the beginning of a story that is unfolding before our eyes, as confirmed by the recent list price disclosure of Zynteglo®, a gene therapy against transfusion-dependent β-thalassemia. Claiming that Zolgensma® is the most expensive treatment in the world is misleading. But claiming that it is ‘expected to save costs to the healthcare system’ in its approval press release is a leap of faith given how little we know about its long-term benefit-risk profile. The only thing which is certain is that pricing frameworks on both sides of the Atlantic need a profound change to be ready for gene and other advanced therapies. And by profound change, I mean a rework from the ground up, not applying band-aids through installments or ICER threshold modulations. With an estimated 10,000 monogenic diseases (WHO), the likes of Zolgensma® and Zynteglo® are prototypes of what payers are truly facing – not a few isolated storms, but an irreversible climate change.

#genetherpay #drugpricing #valuebasedpricing

0 comments on “Decoding the data: Twitter Analytics”

Decoding the data: Twitter Analytics

Around this time last year Hervé shared his dabbling in Google trends leading to a retrospective identification of pharma bro being the most significant driver of pharmaceutical pricing searches in 2017. This sparked a lot of internal interest and prompted a pet project to see if we could do some deeper analysis on societies engagement with pharma pricing by flexing our proverbial machine learning and Natural Language Processing (NLP) muscles. While search trends can take you so far, for 2018 we wanted to get our teeth into something with a bit more substance and Twitter duly obliged. 

True to our business line, Chris spent four months mining tweets from August to November this year, using ‘drug pricing’ as a search term. Then, it was time to get analysing. As Inbeeo’s new analytical intern, I took up the challenge to make sense of this data and while I don’t have Herve’s years industry experience, I wanted to use my data science experience to let the data do the talking. Initial processing removed retweets resulting in a dataset containing 24,131 original (unique) tweets, the vast majority of the these coming from the United States (no real surprises there). However, our real interest was in the content of the tweets, and for this we needed to crack out the algorithms.

I chose two algorithms, K-means and LDA, both considered to be generally good at analysing textual data by grouping similar strings (tweets in this case) into clusters. Running K-means and LDA produced the following plots, where points in the same colour are of the same cluster:

Left: Clusters generated by K-means. Right: Clusters generated by LDA

Both of these clustering algorithms have worked well as we do see defined clusters. However, in the K-means graph we see that the algorithm hasn’t worked as well as LDA. In K-means we have this sea of blue extending across the plot containing most of the tweets, whereas in LDA we have smaller clusters. On cluster inspection, the large blue cluster contains tweets relating to a recent news story about Nirmal Mulye, the CEO of Nostrum Pharmaceuticals, raising drug prices by 400%. Interestingly, (or perhaps unfortunately) K-means hasn’t aggregated all tweets on this story into one cluster, instead we have five. However, LDA has been more successful at aggregation, as we have two clusters relating to this story.

Next, I wondered about the timing of these tweets, did they spike when a news story occurred? Or were they random opinions? Hence, I decided to take the results of the better algorithm, LDA, and plot the tweets of each cluster over time, as shown below.

Graph showing tweet volume over time, indexed by topic. In the infographic on the top right, the size of the box is proportional to the number of tweets per topic

The results confirmed my hunch – people tweeted as a reaction to news. I was then able to identify news stories associated with each cluster based on spikes in time. The most impactful stories discovered were:

  • 12th September, Topic 1 and 10: Nirmal Mulye raising drug prices (this is somewhat unsurprising) 
  • 25th August, Topic 2: The Senate working on solutions to end secrecy around drug pricing
  • 11th September, Topic 4: Pharmaceutical middlemen rake in millions 
  • 10th October, Topic 5: Trump signs a bill on drug price 
  • 7th September and 25th August, Topic 6: The pay raise of the Pfizer CEO, interestingly this story was initially announced in March, but residual spikes were seen 
  • 8th September, Topic 7: Trump ends foreign price controls 
  • 27th August, Topic 13: A drug price hike in the Netherlands 
  • 10th October, Topic 14: Trump lifts a gag clause on drug pricing

Interestingly, running the LDA algorithm multiple times yielded slightly different outputs. LDA was discovering new stories with each run, implying that multiple runs of the algorithm may be required to find all news stories that were sparking debate on twitter. The additional stories included Oklahoma Medicaid testing a new tactic to curb US drug costs and a tweet by AARP Advocates, a group of advocates for health care, social security and older workers, encouraging people to contact their representative about rising prescription costs for seniors. Returning to the K-means analysis I found a cluster of tweets on a news story about the price of a heroin withdrawal drug increasing, a story not found by LDA so maybe both algorithms are needed in order to identify all news stories gaining twitter traction.

Interestingly, and perhaps a comment on human nature, it seemed people were more inclined to comment on negative stories, such as the Nirmal Mulye story, compared to positive stories, for instance Trump lifting the gag clause. Are these trends the sign of a broken system (with unaffordable prices and inadequate protection of vulnerable citizens, such as the elderly and heroin addicts) or do people just like to moan? Additionally, most of these tweets are from the US and are influenced heavily by politics highlighted by Donald Trump’s appearance in topics relating to lowering pricing and increasing transparency as well as AARP Advocates’ petition to Congress. This also begs the question as to some of the motivations behind these tweets. Are they from people with a genuine concern about drug pricing, or opportunistic posters using the stories to push a political agenda?  However, though small in proportion compared to negative tweets, there are signs of positive engagement. Donald Trump and the Senate making headway to increase transparency and lower drug costs, and Oklahoma Medicaid working on curbing drug costs both got people talking. 

To get an idea of public opinion on a topic, be it a particular drug or pharmaceutical company, NLP is the way to go. Using fairly simple (as far as machine learning goes anyway) algorithms I was able to quickly identify news events concerning drug pricing that resonated with the Twitterati and gauge opinions and even more is possible! Pharmaceutical companies can analyse twitter to assess if their product was a success, for instance, are people reporting side effects? Real time dashboards can mine data from twitter and run sentiment analysis to discover how people feel about a company or the market in general. And what about marketing a product? When is the best time of day to post about it? All this and more can be discovered using Machine Learning, the possibilities are immense! 

0 comments on “Four Definitions of Value-Based Pricing and Counting”

Four Definitions of Value-Based Pricing and Counting

I just got back from the annual ISPOR conference in Barcelona after a few years of absence (my absence, not ISPOR’s; it turns out they kept hosting the conference without me #weird). And I still feel energized by the quality of the scientific exchanges I had the chance to participate in (it also turns out the overall quality of the conference went up in my absence #weirder). At Inbeeo, we got an energy boost from all the buzz around ‘value-based pricing’, or VBP. My VBP’o’Meter counted an average of 24.6±2.5 mentions across the three plenary sessions. Quite a good sign as we are rolling-out our very-own i-vbp®. At the same time, if like me you are more on the tangible application side of things, you might have returned from the conference with a hint of frustration. There was no hands-on session on VBP, not even a presentation on how it is defined.  A formula maybe? Nope. By contrast, if you are new to the field and this was your first ISPOR conference, chances are you now have a good sense of what is an Incremental Cost-effectiveness Ratio (ICER) or a number of Quality-Adjusted Life-Years (QALY). Not so much of what is a value-based price.

It left me with a paradoxical feeling and many interrogations. Why would such an important topic not receive the clarity it deserves? Is it because it is so obvious, no one would want to waste the attendees’ time with some form of description? And no one would dare to ask, of course. A bit like ten years ago everybody would mention ‘hashtag’ and you would not dare to ask, because you knew it would have made you look bad. Or is it because it is not so clear? Or even worth, not clear at all? Maybe this is because there are 5,485 definitions of value-based pricing, i.e. one per ISPOR attendee? That would be extreme. More reasonably, the only plausible explanation I can find is that there are a few definitions of value-based pricing that co-exist at the moment. And they mainly depend on one thing – perspective.

  • The health-economist’s definition

If you are a health-economist, chances are you have developed expert skills at estimating the incremental cost-effectiveness ratio of medical innovations vs. the standard of care in a given indication. You do this by applying your craftsmanship to cost-effectiveness model building. Whether you are addicted to Excel, R, or TreeAge, you have also become an expert at “stochasticizing” your otherwise largely deterministic models. For you, value-based pricing is straightforward. You plug a monetary threshold representing the willingness-to-pay of the payer you are building your analyses for in your model, and you perform a ‘break-even analysis’ of the price of the new treatment. You literally push the treatment price up to the point that will break the bank. Or more precisely, you look for the lowest price point that will push the ICER of the new treatment just above the set threshold.

  • The US Pharmacy Benefit Manager’s definition

As a PBM, you know very well about value-based price. It is your job to obtain value-based prices from pharmaceutical companies to get the best deals for your clients. Whatever is the way to get there – rebates, value-based-contracts, market-share based agreements – value-based price can have only one definition: the cheapest, with cheapness being measured as the gap between the product price tag and the one you have skilfully negotiated. And yes, you have recently started to embrace the health-economist’s definition (see above) by leveraging the reports from the Institute for Clinical and Economic Review (ICER, but not the same as above ICER) in price negotiation. After all, it’s leverage.

  • The European public payer’s definition

As a European payer, you have a lot of respect for the health economist’s definition of value-based pricing. You employ a few health-economists yourself and you have put a lot of energy in the development of your very own guidelines for health-economic evaluation. But after careful consideration of the health-economist’s value-based price, you have no other option than applying a price point that is a tiny bit lower – the affordable price. Literally the price you can afford given your budgetary constraint in the given therapy area and the pressure from your boss. Whether you get there by playing with the thresholds like in England, imposing pre-historical price comparators like in Germany, or ‘just because’ like in France, it does not matter. You don’t have a choice.

  • The pharmaceutical company’s definition

As a pharmaceutical leader, you are fully committed to your mission of providing value for your end-customer – the patient #wewontrest. There is no doubt in your mind that payers around the world demonstrate a very fragmented view of the value of your product. You have decided to embark on a journey to educate them on the many dimensions of the value they are missing out on, blinkered by their restrictive value assessment frameworks. Value-based-price for you is a multiple of what any breakeven analysis will produce, and it finds its justification also in the considerable investments you are making in Research and Development.

  •  Inbeeo’s definition

Spoiler alert! There is a clue in the blog’s picture. We believe it is pricing based on value. Or if I elaborate,

true value-based pricing is realized at a point where the value provided by a new product matches the differentiated worth the company is claiming for it.

This definition is freely inspired by Harvard’s Strategy Professor Utpal M. Dholakia’s, and frankly, we believe it says things in a way that sums it all up nicely.

Having said this, because this definition is largely driven by consumer goods pricing, it requires a bit of extra work for health technologies pricing. Because the demand for drugs and medical devices is intermediated, and because the realization of value cannot be seen at the level of one agent in isolation of the others, value-based pricing must span across several perspectives to reach an elusive goal of reliability. To be fair, it means that there are bits and parts of the four definitions of VBP described above in our definition of value-based pricing. It is all a question of how you weight them against each other. Surely a hot topic for many more posts to come. Watch this space!

0 comments on “Inbeeo at ISPOR 2018, Barcelona: Value-Based Pricing and Global Market Access Strategy”

Inbeeo at ISPOR 2018, Barcelona: Value-Based Pricing and Global Market Access Strategy

Inbeeo is attending ISPOR 2018!

The Inbeeo team is excited to be joining the ISPOR 2018 conference alongside healthcare leaders at Europe’s leading conference for Health Economics and Outcomes Research. We look forward to discussing our vision to transform healthcare consultancy to demonstrate the full value of health technologies and to share insights to help you reach your global goals. Wishing our clients a great weekend at ISPOR Barcelona.

Through our mission of pricing right, defining the right strategies, understanding the data, and operationalizing the value, Inbeeo is with you every step of the way.

Join us at ISPOR Europe in Barcelona. To arrange a meeting, please contact us at contact@inbeeo.com

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0 comments on “In France, the value equation may change (a little)”

In France, the value equation may change (a little)

A milestone event took place in France on July 10, when France’s healthcare industry strategic council CSIS met an agreement with the French Government to strengthen France’s appeal for HealthCare industries. The Government announced a long series of measures, some of them will impact the market access and pricing of pharmaceuticals and other healthcare products.

They may amount to anything between marginal but highly expected improvements of the system and a mini-revolution, like the much-debated reform of the evaluation methods carried out by the health technology assessment body HAS’s Transparency Commission (CT) – which still requires more work and was not part of the decisions.

Let’s focus on one specific aspect, which may modify the value equation in the short term.

The cheapest comparator in the therapeutic class will no longer be the sole price benchmark for ASMR IV products.

The Government is working on a new mission statement for the Pricing Committee (CEPS). In particular, they want to change the committee’s policies for products whose additional therapeutic value is found to be minor (ASMR IV). The objective is to allow the benefits of these products to be better taken into account, and the cheapest product will no longer necessarily be the only reference point in the committee’s negotiations.

What does this mean?

One of the well-known features of how authorities are setting the prices of new pharmaceutical products in France is the pricing according to medical value rule. In summary, it is a 2-step process.

  • At first, an HTA body, the CT grades the medical value of the product by way of stringent HTA review.  The grade is called “ASMR” which more or less translates into clinical/therapeutic improvement versus already existing alternatives, between level I (major improvement) and V (absence of medical improvement). The levels II to IV in-between relate to various levels of clinical improvements: important, moderate, minor.
  • The second step is the pricing negotiation with the CEPS, where the ASMR plays a major role.

Level V products get a price lower or at best on a par with products in the same therapeutic class (including generics).

ASMR IV products get a parity price or a very limited premium (depending on factors other than ASMR) over the cheapest available product.

One of the key issues for the industry is the disincentive to incremental innovation (e.g. new high performing galenic forms of already existing active ingredients). For manufacturers of these products, the choice lies between the worst of 2 evils:

  • Either heavily invest in comparative clinical trials, with the inherent risk of failure, in hope of getting the ASMR IV level, and be rewarded by … a very limited price reward
  • Or limit the clinical research investment, and be assured of getting a very disappointing price, despite the effort to invest in improving the product and the life of patients.

Observers of the French pharma pricing scene have noticed over the years a growing trend to squash the ASMR grades in the lower end, a majority of products getting the grades IV or V, few innovations getting III and the TC almost denying entirely access to the levels I and II. The year 2017 TC activity report shows that over 53 new medicines, only 13 were considered a therapeutic improvement: 11 minor (ASMR IV) and 2 moderate (ASMR III). This also means that 75% of the new products got the infamous “lack of improvement” grading.

Screen Shot 2018-07-31 at 19.00.35
ASMR Ratings Granted by France’s Transparency Commission since 2008 as part of the Complete Appraisal Process – source: https://www.has-sante.fr/portail/upload/docs/application/pdf/2018-07/rapport_activite_commission_transparence_2017.pdf

Observers have also noticed the parallel trend in CEPS pricing practices over years, with average price premiums per ASMR level going downward, kind of a convergence to the bottom.

The Industry has long advocated for a better reward to incremental innovation products that have been completely crushed between the premiums granted to a small selection of highly innovative new chemical entities and the price erosion affecting older products, a way to generate funding for the newest most expensive innovations. It seems that they have now been heard… to some extent.

The experience with the CSIS has shown that there may be an ocean between announcements and the reality. Whether and to what extent the improved pricing situation for ASMR IV products – as well as the many other changes – will be enforced makes the industry cautious.

0 comments on “Six Drugs that Cost a Medicare Part D Beneficiary more than $10,000 a Year”

Six Drugs that Cost a Medicare Part D Beneficiary more than $10,000 a Year

No one will argue that Medicare Part D met a genuine need at its inception in the mid-2000s – offering US Seniors an affordable voluntary health insurance plan that would cover their outpatient prescription needs. Fast forward 10 years. Has Part D met its objective? Looking at the front-end of things in terms of the availability of a wide range of plans at an affordable premium in all parts of the nation, yes, definitely. Looking at the back-end of things in terms of actual payments made to beneficiaries, this is a question mark.

The good thing about CMS is that they are true to their commitment to data transparency and availability. Seventy percent of all claims leading to a Medicare Part D reimbursement event are available on their website for anyone to download and analyze. Which I did and which I will keep doing regularly given the wealth of insight laying in that goldmine for anyone interested in value-based healthcare. And because this is individual claims data, you can aggregate them by any variable you are interested in – product if you are a pharma company, region if you are a policymaker, indication if you are a physician, etc. I have looked at it through the lens of the beneficiary. More precisely, by asking the data

“How much could the maximum financial burden for a patient be in one year and for one single medicine?”

The short answer is “a lot”. In more details, there are 6 prescription drugs that will, on average, cost a patient more than US$10,000 a year

  • Cinryze® – a C1 Esterase Inhibitor indicated for the prevention of swelling and/or painful attacks in teenagers and adults with Hereditary Angioedema (HAE)
  • Berinert® – a C1 Esterase Inhibitor indicated for the on-demand treatment of swelling and/or painful attacks of hereditary angioedema (HAE) in adults and children
  • Actiimune® – an Interferon Gamma-1B indicated for the treatment of chronic granulomatous disease and severe malignant osteoporosis
  • Gattex® – a glucagon-like peptide-2 (GLP-2) analog indicated for the treatment of adult patients with Short Bowel Syndrome (SBS)
  • Kalbitor® – a plasma kallikrein inhibitor indicated for treatment of acute attacks of hereditary angioedema (HAE)

Now, the purpose of this post is not to discuss the merit of these products from the point of view of value-based pricing. Because value-based pricing is my day job and I somewhat consider this blog to be my night job. No, the purpose of this post, also because it is getting late in the day and I am craving sugar, is to write about donuts. More specifically what is usually in the middle of it – a big hole.

Somewhere in the thick piece of legislation that provides the framework for Part D, lawmakers have designed a sophisticated scheme that would make the beneficiaries financially accountable for a portion of their drugs payment. The definition of “portion” has been the subject of intense Experts meetings and has led to this –

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source: Center for Medicare and Medicaid Sevices / Inbeeo’s analysis

I have put this schematic together because trying to write about it would have led to something like-

The beneficiary funds the first tens of dollars of the cost – except in the case of $0-deductible plans – and then splits the cost 25-75 with the plan, before paying a bigger portion of the cost in something called the “donut hole” – portion that becomes smaller and smaller as manufacturers are required to chip in as part of the Affordable Care Act – for finally paying a small portion of the highest tranche of the cost – for which the manufacturer is not required to chip in – although it is aptly named the “catastrophic coverage portion”.

And there goes the rule to write short sentences in articles – down the toilet!

So, how big is the problem? Surely small in absolute terms. Surely big if you ask the affected patients. And there are worryingly more and more of them-

part D copay greater than 10K evolution
source: Center for Medicare and Medicaid Sevices / Inbeeo’s analysis (based on 70% of all claims; beneficiaries of Low-Income Subsidies were excluded)

Putting the initial legislation aside, what stuns me the most in this story is the level of sophistication that has been applied to the progressive closing of the donut hole, while the Catastrophic Coverage Portion was ignored altogether. It inevitably reminds me of one of my all-time favorite scenes from The Simpsons-

 

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The Dust Never Settled after Drug Pricing 9/22

Google is powerful. You knew that, I knew that. It really is. But it keeps amazing me when it differentiates a church from a cathedral in my photos while Adobe can’t recognize my face, when it sends me a weather alert before I get on my bicycle while my iPhone thinks I’m driving when I’m on the tube, or when it perfectly filters all the spams from my Gmail account while Outlook keeps trashing emails from clients. In a recent acute distraction episode, I started reading on the hot topic of how investors were increasingly leveraging Google’s analytical tools to make investment decisions. You may have heard for instance that the correlation between the price of bitcoin and google searches of the term was at an astonishing 91%. Enlightened by my discoveries, I tried to bring them back home and started to analyze how search topics such as “pharmaceutical market access”, “drug pricing” or “value-based pricing” were trending.

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Google Search Index for “value-based pricing”, “pharmaceutical market access” and “drug pricing” over the past 5 years – source Google trends

The first piece of insight I gained from my exploration was that my line of business was not exactly booming, at least not the bitcoin-like booming. But it was stable. The second – and way more striking fact – was that something happened around September 2015. Something that had had a sustained effect on how many people around the world were genuinely interested in how prescription drugs are being priced. The data looked like a textbook example of a structural change in a time series. Now, when things get really interesting with Google’s analytical tools, is that you can dig deeper into regions, associated search terms, time frames, type of searches e.g. web, news, blogs etc. Trying to put my finger on the event(s) that triggered the shift, my first list of suspects was without surprise:

  • Hot topics in the US included Medicare Part D, 340B federal discount program, drug coverage, and Valeant
  • US elections also came up quite significantly with the usual pharma-targeting Constant Gardner-inspired evil corporation rhetoric
  • Other regions on the heat map included Canada, Western Europe, and India

All of those were interesting, but nothing really coincided with that week of September 20th, 2015 when the surge happened. Slicing the data like a salami, I zoomed into that week and split the data into search types. And then it was apparent “like the nose in the middle of the face” (you don’t really say that in English, do you?). On Tuesday the 22nd of September the news of Turing Pharmaceuticals jacking up the price of Daraprim by 50x broke globally. However, searches of “Turing Pharmaceuticals” were insignificant. Instead, all the noise was crystallizing around the name of the company’s founder, Martin Shkreli. So how does “Martin Shkreli” search term correlates with “Drug Pricing”? Well, it’s bitcoin-like correlation.

googletrends1
Google Search Index for “drug pricing” and “Martin Shkreli” over the past 5 years – source Google trends

To be complete, the only other search term providing a similar level of correlation was “pharma bro”. If you’re having doubts about one man significantly and durably raising the profile of prescription drug pricing like this (or if you’re a statistician about to add a “you forgot to account for the confounding factors” comment), here is the order of magnitude between him and the other associated search terms.

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Google Search Index for “Martin Shkreli”, “Turing Pharmaceuticals”, “Valeant”, “Medicare Part D”, and “304B” over the past 5 years – source Google trends

So what did we learn? I’m not sure, to be honest. This piece of data left me voiceless, and I wanted to share it. It saddens me to think that the incredibly important topic of drug pricing has been brought to light by this individual. Taking a step back, there must be a silver lining here

  • People are watching – and that’s a good thing. In the end, pharmaceutical companies and payers are both accountable to the people they serve. Pricing, discounting, coverage, and co-pay decisions must be based on patient value
  • It is personal – corporations can be detestable. Remember Enron? But nothing matches the  power of a bad guy a la Joker in Batman, or more so a la Pellegrin in The Constant Gardener, to mobilize the masses
  • Google is powerful, use it! – I mean not for googling. For everything else. Companies are spending millions in market research while they could reinvest that money in R&D and start leveraging the data they have free of charge at their fingertips
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Why I Prefer a Value-Based Healthcare Mayhem over the Status Quo

“Value-based pricing”, “value-based assessment”, “value-based healthcare”, “value-based insurance design”, “value-based purchasing”… I recently saw a “value-based marketplace” although I’m unsure about this one. OK, we get the message, healthcare funding decisions must be based on value. But doesn’t it sound like a bombardment of truisms? Yes, but sometimes truisms are worth saying, Jacques de La Palice would surely agree with me. If he weren’t dead, of course.

In the case of pharmaceutical pricing, I see the “VBP invasion” as a balancing act. A call for action to move away from all the other idiotic pharmaceutical pricing techniques which are enforced around the world and across segments of public and private care. If like me you have been involved with pharmaceutical pricing for some years, there must have been plenty of diners with friends or relatives asking you to explain why and how is drug pricing so different than the pricing of any other good. When I’m asked, I am trying to pick an example people can relate to – ” See the new iPhone for example. There must have been some meeting at Apple to decide if the larger screen and extended battery life justify a price tag crossing the $1,000 line, right? Well, that is the exact opposite of what is happening in the pharma industry.”

So what is really happening the often-fantasized world of pharmaceutical pricing? Here is a short list, surely not exhaustive, in plain language, of the things that can only be seen in drug pricing, sorted by random order of value-denial:

  • External Reference Pricing (ERP) – a mechanism by which country A sets the price  of product X at the exact same level as seen in country B. Just like for their UEFA Euro 2004 surprise triumph, the Greeks are the undisputed champions at ERP, more agile and more creative than anyone else to constantly check that no product is more expensive in Greece than anywhere else. It is a bit like saying that as of today, by national decree, parmesan will replace feta in all greek salads by virtue of external referencing to Italy. Weird.
  • Internal Reference Pricing (IRP) – a mechanism by which product X gets the same price as product Y because it belongs – more or less – to the same therapeutic class. Important methodological note: the fact that product Y launched in 1946 is linked to often fatal renal toxicity is not factored in the decision, which is made by the country’s top-notch chemical engineers. As occasionally seen in Germany.
  • cost-based methods – the price of product X is set at the level of its cost plus a little something. The definition of what is included in “cost” varies. Less and less used by governments with some resistance in Asia, this heating fuel-inspired pricing method is oddly more and more self-inflicted by the pharmaceutical industry in a desperate attempt to use R&D costs to justify drug prices. See here.
  • PBM target rebate-based pricing – a sophisticated retro-engineered pricing mechanism by which the price of product X is set high enough so that Pharmacy Benefit Manager A can negotiate a substantial rebate from drug company B that he can pass on to his commercial Health Plan client C who will in return show their appreciation to PBM A. A US-only scheme and a strong competitor for the all-time winner of the value-denial grand prize, now that it’s getting more publicity. See here for instance.
  • CPI-based pricing – a mechanism that limits year-on-year price increases of Product X to the same level as the Consumer Price Index increase, also called “inflation”. Introduced by payers, this rule is being reinterpreted as we speak by the pharma industry as the “10% pricing pledge”. Another self-inflicted PR faux-pas from the industry ignoring the fact that this represents around 5x the inflation.
  • Profit-regulation pricing – a complex multi-year mechanism by which the price of product X will be cut by a factor Y if its marketing company’s profit exceeds a pre-agreed level Z, regardless of any benefit or harm to the patient. A European technocratic masterpiece and for all rugby fans, another England vs. France crunch. I’ve been trying to explain their respective “PPRS” and “accord-cadre” to my US colleagues forever.

Why a pharmaceutical product, instead of being priced based on its features and the value it provides to its customers, has to be priced according to these rules? If I believe that seeing my pictures in a larger format, and not having to worry about charging for a full day, justifies spending more money on a new smartphone, then so be it.  That’s value-based pricing.

The only silver-lining I can see in this mayhem is that the value-denial syndrome used to be broadly shared between payers and the industry, and now they both seem to come to the same realization that value-based pricing is the only way. Easier said than done, but that’s a start.