Jason Shafrin’s journal round-up for 15th July 2019

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Understanding price growth in the market for targeted oncology therapies. American Journal of Managed Care [PubMed] Published 14th June 2019

In the media, you hear that drugs prices—particularly for oncology—are on the rise. With high prices, it makes it difficult for payers to afford effective treatments. For countries where patients bear significant cost, patients may even go without treatment. Are pharmaceutical firms making money hand over fist with these rising prices?

Recent research by Sussell et al. argues that, despite increased drug price costs, pharmaceutical manufacturers are actually making less money on every new cancer drug they produce. The reason? Precision medicine.

The authors use data from both the IQVIA National Sales Perspective (NSP) data set and the Medicare Current Beneficiary Survey (MCBS) to examine changes in the price, quantity, and total revenue over time. Price is measured as episode price (price over a fixed line of therapy) rather than the price per unit of drug. The time period for the core analysis covers 1997-2015.

The authors find that drug prices have roughly tripled between 1997-2015. Despite this price increase, pharmaceutical manufacturers are actually making less money. The number of eligible (i.e., indicated) patients per new oncology drug launch fell between 85% to 90% over this time period. On net, median pharmaceutical manufacturer revenues fell by about half over this time period.

Oncology may be the case where high cost drugs are a good thing; rather than identifying treatments indicated for a large number of people that are less effective on average per patient, develop more highly effective drugs targeted to small groups of people. Patients don’t get unnecessary treatments, and overall costs to payers fall. Of course, manufacturers still need to justify that these treatments represent high value, but some of my research has shown that quality-adjusted cost of care in oncology has remained flat or even fallen for some tumors despite rising drug prices.

Do cancer treatments have option value? Real‐world evidence from metastatic melanoma. Health Economics [PubMed] [RePEc] Published 24th June 2019

Cost effectiveness models done from a societal perspective aim to capture all benefits and costs of a given treatment relative to a comparator. Are standard CEA approaches really capturing all costs and benefits? A 2018 ISPOR Task Force examines some novel components of value that are not typically captured, such as real option value. The Task Force describes real option value as value that is “…generated when a health technology that extends life creates opportunities for the patient to benefit from other future advances in medicine.” Previous studies (here and here) have shown that patients who received treatments for chronic myeloid leukemia and non-small cell lung cancer lived longer than expected since they were able to live long enough to reach the next scientific advance.

A question remains, however, of whether individuals’ behaviors actually take into account this option value. A paper by Li et al. 2019 aims to answer this question by examining whether patients were more likely to get surgical resection after the advent of a novel immuno-oncology treatment (ipilimumab). Using claims data (Marketscan), the authors use an interrupted time series design to examine whether Phase II and Phase III clinical trail read-outs affected the likelihood of surgical resection. The model is a multinomial logit regression. Their preferred specification finds that

“Phase II result was associated with a nearly twofold immediate increase (SD: 0.61; p = .033) in the probability of undergoing surgical resection of metastasis relative to no treatment and a 2.5‐fold immediate increase (SD: 1.14; p = .049) in the probability of undergoing both surgical resection of metastasis and systemic therapy relative to no treatment.”

The finding is striking, but also could benefit from further testing. For instance, the impact of the Phase III results are (incrementally) small relative to the Phase II results. This may be reasonable if one believes that Phase II is a sufficiently reliable indicator of drug benefit, but many people focus on Phase III results. One test the authors could look at is to see whether physicians in academic medical centers are more likely to respond to this news. If one believes that physicians at academic medical centers are more up to speed on the literature, one would expect to see a larger option value for patients treated at academic compared to community medical centers. Further, the study would benefit from some falsification tests. If the authors could use data from other tumors, one would expect that the ipilimumab Phase II results would not have a material impact on surgical resection for other tumor types.

Overall, however, the study is worthwhile as it looks at treatment benefits not just in a static sense, but in a dynamically evolving innovation landscape.

Aggregate distributional cost-effectiveness analysis of health technologies. Value in Health [PubMed] Published 1st May 2019

In general, health economists would like to have health insurers cover treatments that are welfare improving in the Pareto sense. This means, if a treatment provides more expected benefits than costs and no one is worse off (in expectation), then this treatment should certainly be covered. It could be the case, however, that people care who gains these benefits. For instance, consider the case of a new technology that helped people with serious diseases move around more easily inside a mansion. Assume this technology had more benefits than cost. Some (many) people, however, may not like covering a treatment that only benefits people who are very well-off. This issue is especially relevant in single payer systems—like the United Kingdom’s National Health Service (NHS)—which are funded by taxpayers.

One option is to consider both the average net health benefits (i.e., benefits less cost) to a population as well as its effect on inequality. If a society doesn’t care at all about inequality, then this is reduced to just measuring net health benefit overall; if a society has a strong preference for equality, treatments that provide benefits to only the better-off will be considered less valuable.

A paper by Love-Koh et al. 2019 provides a nice quantitative way to estimate these tradeoffs. The approach uses both the Atkinson inequality index and the Kolm index to measure inequality. The authors then use these indices to calculate the equally distributed equivalent (EDE), which is the level of population health (in QALYs) in a completely equal distribution that yields the same amount of social welfare as the distribution under investigation.

Using this approach, the authors find the following:

“Twenty-seven interventions were evaluated. Fourteen interventions were estimated to increase population health and reduce health inequality, 8 to reduce population health and increase health inequality, and 5 to increase health and increase health inequality. Among the latter 5, social welfare analysis, using inequality aversion parameters reflecting high concern for inequality, indicated that the health gain outweighs the negative health inequality impact.”

Despite the attractive features of this approach analytically, there are issues related to how it would be implemented. In this case, inequality is based solely on quality-adjusted life expectancy. However, others could take a more holistic approach and look at socioeconomic status including other factors (e.g., income, employment, etc.). In theory, one could perform the same exercise measuring individual overall utility including these other aspects, but few (rightly) would want the government to assess individuals’ overall happiness to make treatment decisions. Second, the authors qualify expected life expectancy by patients’ sex, primary diagnosis and postcode. Thus, you could have a system that prioritizes treatments for men—since men’s life expectancy is generally less than women. Third, this model assumes disease is exogenous. In many cases this is true, but in some cases individual behavior could increase the likelihood of having a disease. For instance, would citizens want to discount treatments for diseases that are preventable (e.g., lung cancer due to smoking, diabetes due to poor eating habits/exercise), even if treatments for these diseases reduced inequality. Typically, there are no diseases that are fully exogenous or fully at fault of the individual, so this is a slippery slope.

What the Love-Koh paper contributes is an easy to implement method for quantifying how inequality preferences should affect the value of different treatments. What the paper does not answer is whether this approach should be implemented.


Paying for Cures Masterclass: Unlocking the true value of every treatment to improve outcomes for everyone

Want to know how you can help increase access to cutting-edge therapies – and fuel greater innovation? Join us on 4 July at a Central London venue for the second instalment of our Masterclass series, ‘Paying for Cures’, to get essential answers to urgent questions.

Breakthrough medical research is delivering new curative therapies that once seemed out of reach. But despite these significant steps forward, the therapeutic benefits remain unattainable for far too many patients. That’s capping both pharmaceutical revenues and the potential of future research & development.

The advent of cures is raising important questions about affordability and the long-term sustainability of health systems. Simply put, healthcare systems are not set up to pay for cures where the benefits may accrue over many decades.

So how can you help improve outcomes – for patients and industry alike?

Join us on 4 July for the next OHE Masterclass, ‘Paying for Cures’, to discuss:

• How value assessment can be adapted to recognise the full value of a cure
• How innovative financing mechanisms can help ensure that patients benefit from every exciting step forward
• What solutions are acceptable and beneficial to both industry and payers
• What lessons we’ve learned from recent experience in the UK and the rest of the world
• Get access to exciting in-progress research. Put your questions to a panel of OHE and industry experts. And help us move the industry’s pricing frameworks forward – to improve the lives of patients everywhere.

This is a full day masterclass followed by a networking drinks reception until 19:00.

Sam Watson’s journal round-up for 25th February 2019

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Democracy does cause growth. Journal of Political Economy [RePEc] Published January 2019

Citizens of a country with a democratic system of government are able to affect change in its governors and influence policy. This threat of voting out the poorly performing from power provides an incentive for the government to legislate in a way that benefits the population. However, democracy is certainly no guarantee of good governance, economic growth, or population health as many events in the last ten years will testify. Similarly, non-democracies can also enact policy that benefits the people. A benevolent dictator is not faced with the same need to satisfy voters and can enact politically challenging but beneficial policies. People often point to China as a key example of this. So there remains the question as to whether democracy per se has any tangible economic or health benefits.

In a past discussion of an article on democratic reform and child health, I concluded that “Democratic reform is neither a sufficient nor necessary condition for improvements in child mortality.” Nevertheless democracy may still be beneficial, on average, given the in-built safeguards against poor leaders. This paper, which has been doing the rounds for years as a working paper, is another examination of the question of the impact of becoming democratic. Principally the article is focused on economic growth, but health and education outcomes feature (very) briefly. The concern I have with the article mentioned at the beginning of this paragraph and with this newly published article are that they do not consider in great detail why democratisation occurred. As much political science work points out, democratic reform can be demanded in poor economic conditions due to poor governance. For these endogenous changes economic growth causes democracy. Whereas in other countries democracy could come about in a more exogenous manner. Lumping them all in together may be misleading.

While the authors of this paper provide pages after pages of different regression specifications, including auto-regressive models and instrumental variables models, I remain unconvinced. For example, the instrument relies on ‘waves’ of transitions: a country is more likely to shift politically if its regional neighbours do, like the Arab Spring. But neither economic nor political conditions in a given country are independent of its neighbours. In somewhat of a rebuttal, Ruiz Pozuelo and other authors conducted a survey to try to identify and separate out those countries which transitioned to democracy endogenously and exogenously (from economic conditions). Their work suggests that the countries that transitioned exogenously did not experience growth benefits. Taken together this shows the importance of theory to guide empirical work, and not the other way round.

Effect of Novartis Access on availability and price of non-communicable disease medicines in Kenya: a cluster-randomised controlled trial. Lancet: Global Health Published February 2019

Access to medicines is one of the key barriers to achieving universal health care. The cost-effectiveness threshold for many low income countries rules out many potentially beneficial medicines. This is in part driven though by the high prices charged by pharmaceutical countries to purchase medicine, which often do not discriminate between purchasers with high and low abilities to pay. Novartis launched a scheme – Novartis Access – to provide access to medicines to low and middle income countries at a price of US$1 per treatment per month. This article presents a cluster randomised trial of this scheme in eight counties of Kenya.

The trial provided access to four treatment counties and used four counties as controls. Individuals selected at random within the counties with non-communicable diseases and pharmacies were the principal units within the counties at which outcomes were analysed. Given the small number of clusters, a covariate-constrained randomisation procedure was used, which generates randomisation that ensures a decent balance of covariates between arms. However, the analysis does not control for the covariates used in the constrained randomisation, which can lead to lower power and incorrect type one error rates. This problem is emphasized by the use of statistical significance to decide on what was and was not affected by the Novartis Access program. While practically all the drugs investigated show an improved availability, only the two with p<0.05 are reported to have improved. Given the very small sample of clusters, this is a tricky distinction to make! Significance aside, the programme appears to have had some success in improving access to diabetes and asthma medication, but not quite as much as hoped. Introductory microeconomics though would show how savings are not all passed on to the consumer.