Meeting round-up: iHEA Congress 2019

Missed iHEA 2019? Or were you there but could not make it to all of the amazing sessions? Stay tuned for my conference highlights!

iHEA started on Saturday 13th with pre-congress sessions on fascinating research as well as more prosaic topics, such as early-career networking sessions with senior health economists. All attendees got a super useful plastic bottle – great idea iHEA team!

The conference proper launched on Sunday evening with the brilliant plenary session by Raj Chetty from Harvard University.

Monday morning started bright and early with the thought-provoking session on validation of CE models. It was chaired and discussed by Stefan Lhachimi and featured presentations by Isaac Corro Ramos, Talitha Feenstra and Salah Ghabri. I’m pleased to see that validation is coming to the forefront of current topics! Clearly, we need to do better in validating our models and documenting code, but we’re on the right track and engaged in making this happen.

Next up, the superb session on the societal perspective for cost-effectiveness analysis. It was an all-star cast with Mark Sculpher, Simon Walker, Susan Griffin, Peter Neumann, Lisa Robinson, and Werner Brouwer. I’ve live-tweeted it here.

The case was expertly made that taking a single sector perspective can be misleading when evaluating policies with cross-sectoral effects, hence the impact inventory by Simon and colleagues is a useful tool to guide the choice of sectors to include. At the same time, we should be mindful of the requirements of the decision-maker for whom CEA is intended. This was a compelling session, which will definitely set the scene for much more research to come.

After a tasty lunch (well done catering team!), I headed to the session on evaluations using non-randomised data. The presenters included Maninie Molatseli, Fernando Antonio Postali, James Love-Koh and Taufik Hidayat, on case studies from South Africa, Brazil and Indonesia. Marc Suhrcke chaired. I really enjoyed hearing about the practicalities of applying econometric methods to estimate treatment effects of system wide policies. And James’s presentation was a great application of distributional cost-effectiveness analysis.

I was on the presenter’s chair next, discussing the challenges in implementing policies in the southwest quadrant of the CE plane. This session was chaired by Anna Vassall and discussed by Gesine Meyer-Rath. Jack Dowie started by convincingly arguing that the decision rule should be the same regardless of where in the CE plane the policy falls. David Bath and Sergio Torres-Rueda presented fascinating case studies of south west policies. And I argued that the barrier was essentially a problem of communication (presentation available here). An energetic discussion followed and showed that, even in our field, the matter is far from settled.

The day finished with the memorial session for the wonderful Alan Maynard and Uwe Reinhardt, both of whom did so much for health economics. It was a beautiful session, where people got together to share incredible stories from these health economics heroes. And if you’d like to know more, both Alan and Uwe have published books here and here.

Tuesday started with the session on precision medicine, chaired by Dean Regier, and featuring Rosalie Viney, Chris McCabe and Stuart Peacock. Rather than slides, the screen was filled with a video of a cosy fireplace, inviting the audience to take part in the discussion.

Under debate was whether precision medicine is a completely different type of technology, with added benefits over and above improvement to health, and needing a different CE framework. The panellists were absolutely outstanding in debating the issues! Although I understand the benefits beyond health that these technologies can offer, I side with the view that, like with other technologies, value is about whether the added benefits are worth the losses given the opportunity cost.

My final session of the day was by the great Mike Drummond, comparing how HTA has influenced the uptake of new anticancer drugs in Spain versus England (summary in thread below). Mike and colleagues found that positive recommendations do increase utilisation, but the magnitude of change differs by country and region. The work is ongoing in checking that utilisation has been picked up accurately in the routine data sources.

The conference dinner was at the Markthalle, with plenty of drinks and loads of international food to choose from. I had to have an early night given that I was presenting at 8:30 the next morning. Others, though, enjoyed the party until the early hours!

Indeed, Wednesday started with my session on cost-effectiveness analysis of diagnostic tests. Alison Smith presented on her remarkable work on measurement uncertainty while Hayley Jones gave a masterclass on her new method for meta-analysis of test accuracy across multiple thresholds. I presented on the CEA of test sequences (available here). Simon Walker and James Buchanan added insightful points as discussants. We had a fantastically engaged audience, with great questions and comments. It shows that the CEA of diagnostic tests is becoming a hugely important topic.

Sadly, some other morning sessions were not as well attended. One session, also on CEA, was even cancelled due to lack of audience! For future conferences, I’d suggest scheduling the sessions on the day after the conference dinner a bit later, as well as having fewer sessions to choose from.

Next up on my agenda was the exceptional session on equity, chaired by Paula Lorgelly, and with presentations by Richard Cookson, Susan Griffin and Ijeoma Edoka. I was unable to attend, but I have watched it at home via YouTube (from 1:57:10)! That’s right, some sessions were live streamed and are still available via the iHEA website. Do have a look!

My last session of the conference was on end-of-life care, with Charles Normand chairing, discussed by Helen Mason, Eric Finkelstein, and Mendwas Dzingina, and presentations by Koonal Shah, Bridget Johnson and Nikki McCaffrey. It was a really thought-provoking session, raising questions on the value of interventions at the end-of-life compared to at other stages of the life course.

Lastly, the outstanding plenary session by Lise Rochaix and Joseph Kutzin on how to translate health economics research into policy. Lise and Joseph had pragmatic suggestions and insightful comments on the communication of health economics research to policy makers. Superb! Also available on the live stream here (from 06:09:44).

iHEA 2019 was truly an amazing conference. Expertly organised, well thought-out and with lots of interesting sessions to choose from. iHEA 2021 in Cape Town is firmly in my diary!

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.

Credits

Economics of personalised medicine: an introduction

Personalised medicine appears to be an inevitable future of health care, and economists aren’t ready for it.

It has various monikers and related concepts including precision medicine, stratified medicine, pharmacogenomics, pharmacogenetics and predictive medicine. But, whatever you call it, it means big changes in health care. Sociologists, ethicists, medics and others have all been confronting it in recent years. Economists have been relatively slow on the uptake, though some have begun thinking about it (see for example here, here, here)

Some of my current work involves evaluating predictive medicine in the form of a screening intervention, and we have a paper in the pipeline discussing some of the implications for cost-effectiveness analysis. This work is presenting a number of new challenges but is also highlighting some opportunities for the optimisation of health care.

Over the next few months I will be introducing and discussing some of the potential implications of personalised medicine for our discipline. These will include familiar topics in health economics and will probably fall under the following headings:

  1. Demand for health and health care
  2. Need
  3. Supply of health care
  4. Provider behaviour
  5. Health insurance
  6. Costs
  7. Economic evaluation
  8. Decision modelling
  9. Population health
  10. New market failures
  11. Equity

These may merge, change or disappear as I progress, but I hope to cover as many angles as possible. Hopefully, with your feedback, we might be able to help guide future work in this area.