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.

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Simon McNamara’s journal round-up for 21st January 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.

Assessing capability in economic evaluation: a life course approach? The European Journal of Health Economics [PubMed] Published 8th January 2019

If you have spent any time on social media in the last week there is a good chance that you will have seen the hashtag #10yearchallenge. This hashtag is typically accompanied by two photos of the poster; one recent, and one from 10 years ago. Whilst the minority of these posts suggest that the elixir of permanent youth has been discovered and is being hidden away by a select group of people, the majority show clear signs of ageing. As time passes, we change. Our skin becomes wrinkled, our hair may become grey, and we may become heavier. What these pictures don’t show, is how we change internally – and I don’t mean biologically. As we become older, and we experience life, so the things we think are important change. Our souls become wrinkled, and our minds become heavier.

The first paper in this week’s round-up is founded on this premise, albeit grounded in the measurement of capability well-being across the life course, rather than a hashtag. The capabilities approach is grounded in the normative judgement that the desirability of policy outcomes should be evaluated by what Sen called the ‘capabilities’ they provide – “the functionings, or the capabilities to function” they give people, where functionings for a person are defined as “the various things that he or she manages to do or be in leading a good life” (Sen, 1993). The author (Joanna Coast) appeals to her, and others’, work on the family of ICECAP measures (capability measures), in order to argue that the capabilities we value changes across the stage of life we are experiencing. For example, she notes that the development work for the ICECAP-A (adults) resulted in the choice of an ‘achievement’ attribute in that instrument, whilst for ICECAP-O (older people) an alternative ‘role’ attribute was used – with the achievement attribute primarily linked to having the ability to make progress in life, and the role attribute linked to having the ability to do things that make you feel valued. Similarly, she notes that the attributes that emerged from development work on the ICECAP-SCM (supportive care – a term for the end of life) are different to those from ICECAP-A (adults), with dignity coming to the forefront as a valued attribute towards the end of life. The author then goes on to suggest that it would be normatively desirable to capture how the capabilities we value changes over the life-course, suggests this could be done with a range of different measures, and highlights a number of problems associated with this (e.g. when does a life-stage start and finish?).

You should read this paper. It is only four pages long and definitely worth your time. If you have spent enough time on social media to know what the #10yearchallenge is, then you definitely have time to read it. I think this is a really interesting topic and a great paper. It has certainly got me thinking more about capabilities, and I will be keeping an eye out for future papers on this in future.

Future directions in valuing benefits for estimating QALYs: is time up for the EQ-5D? Value in Health Published 17th January 2019

If EQ-5D were a person, I think I would be giving it a good hug right now. Every time my turn to write this round-up comes up there seems to be a new article criticising it, pointing out potential flaws in the way it has been valued, or proposing a new alternative. If it could speak, I imagine it would tell us it is doing its best – perhaps with a small tear in its eye. It has done what it can to evolve, it has tried to change, but as we approach its 30th birthday, and exciting new instruments are under development, the authors of the second paper in this week’s round-up question – “Is time up for the EQ-5D?”

If you are interested in the valuation of outcomes, you should probably read this paper. It is a really neat summary of recent developments in the assessment and valuation of the benefits of healthcare, and gives a good indication of where the field may be headed. Before jumping into reading the paper, it is worth dwelling on its title. Note that the authors have used the term “valuing benefits for estimating QALYs” and not “valuing health states for estimating QALYs”. This is telling, and reflects the growing interest in measuring, and valuing, the benefits of healthcare based upon a broader conception of well-being, rather than simply health as represented by the EQ-5D. It is this issue that rests at the heart of the paper, and is probably the biggest threat to the long-term domination of EQ-5D. If it wasn’t designed to capture the things we are now interested in, then why not modify it further, or go back to the drawing board and start again?

I am not going to attempt to cover all the points made in the paper, as I can’t do it justice in this blog; but in summary, the authors review a number of ways this could be done, outline recent developments in the way the subsequent instrument could be valued, and detail the potential advantages, disadvantages, and challenges of moving to a new instrument. Ultimately, the authors conclude that the future of the valuation of outcomes – be that with EQ-5D or something else, depends upon a number of judgements, including whether non-health factors are considered to be relevant when valuing the benefits of healthcare. If they are then EQ-5D isn’t fit for purpose, and we need a new instrument. Whilst the paper doesn’t provide a definitive answer to the question “Is Time Up for the EQ-5D?”, the fact that NICE, the EuroQol group, two of the authors of this paper, and a whole host of others, are currently collaborating on a new measure, which captures both health and non-health outcomes, indicates that EQ-5D may well be nearing the end of its dominance. I look forward to seeing how this work progresses over the next few years.

The association between economic uncertainty and suicide in the short-run. Social Science and Medicine [PubMed] [RePEc] Published 24th November 2018

As I write this, the United Kingdom is 10 weeks away from the date we are due to leave the European Union, and we are still uncertain about how, and potentially even whether, we will finally leave. The uncertainty created by Brexit covers both economic and social spheres, and impacts many of those in the United Kingdom, and many beyond who have ties to us. I am afraid the next paper isn’t a cheery one, but given this situation, it is a timely one.

In the final paper in this round-up, the authors explore the link between economic uncertainty and short-term suicide rates. This is done by linking the UK EPU index of economic uncertainty – an index generated based upon the articles published in 650 UK newspapers – to the daily suicide rates in England and Wales between 2001 and 2015. The authors find evidence of an increase in suicide rates on the days on which the EPU index was higher, and also of a lagged effect on the day after a spike in the index. Over the course of a year, this effect means a one standard deviation increase in the EPU is expected to lead to 11 additional deaths in that year. In comparison to the number of deaths per year from cardiovascular disease, and cancer, this effect is relatively modest, but is nevertheless concerning given the nature of the way in which these people are dying.

I am not going to pretend I enjoyed reading this paper. Technically it is good, and it is an interesting paper, but the topic was just a bit too dark and too relevant to our current situation. Whilst reading I couldn’t help but wonder whether I am going to be reading a similar paper linking Brexit uncertainty to suicide at some point in the future. Fingers crossed this isn’t the case.

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Chris Sampson’s journal round-up for 4th June 2018

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.

A qualitative investigation of the health economic impacts of bariatric surgery for obesity and implications for improved practice in health economics. Health Economics [PubMed] Published 1st June 2018

Few would question the ‘economic’ nature of the challenge of obesity. Bariatric surgery is widely recommended for severe cases but, in many countries, the supply is not sufficient to satisfy the demand. In this context, this study explores the value of qualitative research in informing economic evaluation. The authors assert that previous economic evaluations have adopted a relatively narrow focus and thus might underestimate the expected value of bariatric surgery. But rather than going and finding data on what they think might be additional dimensions of value, the authors ask patients. Emotional capital, ‘societal’ (i.e. non-health) impacts, and externalities are identified as theories for the types of value that might be derived from bariatric surgery. These theories were used to guide the development of questions and prompts that were used in a series of 10 semi-structured focus groups. Thematic analysis identified the importance of emotional costs and benefits as part of the ‘socioemotional personal journey’ associated with bariatric surgery. Out-of-pocket costs were also identified as being important, with self-funding being a challenge for some respondents. The information seems useful in a variety of ways. It helps us understand the value of bariatric surgery and how individuals make decisions in this context. This information could be used to determine the structure of economic evaluations or the data that are collected and used. The authors suggest that an EQ-5D bolt-on should be developed for ’emotional capital’ but, given that this ‘theory’ was predefined by the authors and does not arise from the qualitative research as being an important dimension of value alongside the existing EQ-5D dimensions, that’s a stretch.

Developing accessible, pictorial versions of health-related quality-of-life instruments suitable for economic evaluation: a report of preliminary studies conducted in Canada and the United Kingdom. PharmacoEconomics – Open [PubMed] Published 25th May 2018

I’ve been telling people about this study for ages (apologies, authors, if that isn’t something you wanted to read!). In my experience, the need for more (cognitively / communicatively) accessible outcome measures is widely recognised by health researchers working in contexts where this is relevant, such as stroke. If people can’t read or understand the text-based descriptors that make up (for example) the EQ-5D, then we need some alternative format. You could develop an entirely new measure. Or, as the work described in this paper set out to do, you could modify existing measures. There are three descriptive systems described in this study: i) a pictorial EQ-5D-3L by the Canadian team, ii) a pictorial EQ-5D-3L by the UK team, and iii) a pictorial EQ-5D-5L by the UK team. Each uses images to represent the different levels of the different dimensions. For example, the mobility dimension might show somebody walking around unaided, walking with aids, or in bed. I’m not going to try and describe what they all look like, so I’ll just encourage you to take a look at the Supplementary Material (click here to download it). All are described as ‘pilot’ instruments and shouldn’t be picked up and used at this stage. Different approaches were used in the development of the measures, and there are differences between the measures in terms of the images selected and the ways in which they’re presented. But each process referred to conventions in aphasia research, used input from clinicians, and consulted people with aphasia and/or their carers. The authors set out several remaining questions and avenues for future research. The most interesting possibility to most readers will be the notion that we could have a ‘generic’ pictorial format for the EQ-5D, which isn’t aphasia-specific. This will require continued development of the pictorial descriptive systems, and ultimately their validation.

QALYs in 2018—advantages and concerns. JAMA [PubMed] Published 24th May 2018

It’s difficult not to feel sorry for the authors of this article – and indeed all US-based purveyors of economic evaluation in health care. With respect to social judgments about the value of health technologies, the US’s proverbial head remains well and truly buried in the sand. This article serves as a primer and an enticement for the use of QALYs. The ‘concerns’ cited relate almost exclusively to decision rules applied to QALYs, rather than the underlying principles of QALYs, presumably because the authors didn’t feel they could ignore the points made by QALY opponents (even if those arguments are vacuous). What it boils down to is this: trade-offs are necessary, and QALYs can be used to promote value in those trade-offs, so unless you offer some meaningful alternative then QALYs are here to stay. Thankfully, the Institute for Clinical and Economic Review (ICER) has recently added some clout to the undeniable good sense of QALYs, so the future is looking a little brighter. Suck it up, America!

The impact of hospital costing methods on cost-effectiveness analysis: a case study. PharmacoEconomics [PubMed] Published 22nd May 2018

Plugging different cost estimates into your cost-effectiveness model could alter the headline results of your evaluation. That might seems obvious, but there are a variety of ways in which the selection of unit costs might be somewhat arbitrary or taken for granted. This study considers three alternative sources of information for hospital-based unit costs for hip fractures in England: (a) spell-level tariffs, (b) finished consultant episode (FCE) reference costs, and (c) spell-level reference costs. Source (b) provides, in theory, a more granular version of (a), describing individual episodes within a person’s hospital stay. Reference costs are estimated on the basis of hospital activity, while tariffs are prices estimated on the basis of historic reference costs. The authors use a previously reported cohort state transition model to evaluate different models of care for hip fracture and explore how the use of the different cost figures affects their results. FCE-level reference costs produced the highest total first-year hospital care costs (£14,440), and spell-level tariffs the lowest (£10,749). The more FCEs within a spell, the greater the discrepancy. This difference in costs affected ICERs, such that the net-benefit-optimising decision would change. The study makes an important point – that selection of unit costs matters. But it isn’t clear why the difference exists. It could just be due to a lack of precision in reference costs in this context (rather than a lack of accuracy, per se), or it could be that reference costs misestimate the true cost of care across the board. Without clear guidance on how to select the most appropriate source of unit costs, these different costing methodologies represent another source of uncertainty in modelling, which analysts should consider and explore.

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