Chris Sampson’s journal round-up for 17th December 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.

Health related quality of life aspects not captured by EQ-5D-5L: results from an international survey of patients. Health Policy Published 14th December 2018

Generic preference-based measures, such as the EQ-5D, cannot capture all aspects of health-related quality of life. They’re not meant to. Rather, their purpose is to capture just enough information to be able to adequately distinguish between health states with respect to the domains deemed normatively relavent to decisionmakers. The stated aim of this paper is to determine whether people with a variety of chronic conditions believe that their experiences can be adequately represented by the EQ-5D-5L.

The authors conducted an online survey, identifying participants through 320 patient associations across 47 countries. Participants were asked to complete the EQ-5D-5L and then asked if any aspects of their illness, which had a “big impact” on their health, were not captured by the EQ-5D-5L. 1,031 people started the survey and 767 completed it. More than half were from the UK. 51% of respondents said that there was some aspect of health not captured by the EQ-5D-5L. Of them, 19% mentioned fatigue, 12% mentioned medication side effects, 9.5% mentioned co-morbid conditions, and then a bunch of others in smaller proportions.

It’s nice to know what people think, but I have a few concerns about the usefulness of this study. One of the main problems is that it doesn’t seem safe to assume that respondents interpret “big impact” as meaning “an impact that is independently important in determining your overall level of quality of life”. So, even if we accept that people judging something to be important makes it important (which I’m not sure it does), then we still can’t be sure whether what they are identifying is within the scope of what we’re trying to measure. For starters, I can see no justification for including a ‘medication side effects’ domain. There’s also some concern about selection and attrition. I’d guess that people with more complicated or less common health concerns would be more likely to start and finish a survey about more complicated or less common health concerns.

The main thing I took from this study is that half of respondents with chronic diseases thought that the EQ-5D-5L captured every single aspect of health that had a “big impact”, and that there wasn’t broad support for any other specific dimension.

Reducing drug wastage in pharmaceuticals dosed by weight or body surface areas by optimising vial sizes. Applied Health Economics and Health Policy [PubMed] Published 5th December 2018

It’s common for pharmaceuticals to be wasted. Not just those out-of-date painkillers you threw in the bin, but also the expensive stuff being used in hospitals. One of the main reasons that waste occurs is that vials are made to specific sizes and, often, dosage varies from patient to patient – according to weight, for example – and doesn’t match the vial size. Suppose that vials are available as 50mg and 80mg and that an individual requires a 60mg dose. One way to address this might be to allow for vial sharing, whereby the leftovers are given to the next patient. But that isn’t always possible. So, we might like to consider what the best combination of available vial sizes should be, given the characteristics of the population.

In this paper, the authors set out the problem mathematically. Essentially, the optimisation problem is to minimise cost across the population subject to the vial sizes. An example is presented for two drugs (pembrolizumab and cabazitaxel), simulating patients based on samples drawn from the Health Survey for England. Simplifications are applied to the examples, such as setting a constraint of 8 vials per patient and assuming that prices are linear (i.e. fixed per milligram).

Pembrolizumab is currently available in 50mg and 100mg vials, and the authors estimate current wastage to be 13.2%. The simulations show that switching the 50mg to a 70mg would cut wastage to 8.6%. Cabazitaxel is available in 60mg vials, resulting in 19.4% wastage. Introducing a 12.5mg vial would cut wastage by around two thirds. An important general finding, which should be self-evident, is that vial sizes should not be divisible by each other, as this limits the number of possible combinations.

Depending on when vial sizes are determined (e.g. pre- or post-authorisation), pharmaceutical companies might use it to increase profit margins, or health systems might use it to save costs. Regardless, wastage isn’t useful. Evidence-based manufacture is an example of one of those best ideas; the sort that is simple and seems obvious once it’s spelt out. It’s a rare opportunity to benefit patients, health care providers, and manufacturers, with no significant burden on policymakers.

Death or debt? National estimates of financial toxicity in persons with newly-diagnosed cancer. The American Journal of Medicine [PubMed] Published October 2018

If you’re British, what’s the scariest thing about an ‘Americanised’ (/Americanized) health care system? Expensive inhalers? A shortened life expectancy? My guess is that the prospect of having to add financial ruin to terminal illness looms pretty large. You should make sure your fear is evidence-based. Here’s a paper to shake in the face of anyone who doesn’t support universal health care.

The authors use data from the Health and Retirement Study from 1998-2014, which includes people over 50 years of age and includes new (self-reported) diagnoses of cancer. This was the basis for inclusion in the study, with over 9.5 million new diagnoses of cancer. Up to two years pre-diagnosis was taken as a baseline. The data set also includes information on participants’ assets and debts, allowing the authors to use change in net worth as the primary outcome. Generalised linear models were used to assess various indicators of financial toxicity, including change or incurrence of consumer debt, mortgage debt, and home equity debt at two- and four-year follow-up. In addition to cancer diagnosis, various chronic comorbidities and socio-demographic variables were included in the models.

Shockingly, after two years following diagnosis, 42.4% of people had depleted their entire life’s assets. Average net worth had dropped $92,000. After four years, 38.2% were still insolvent. Women, older people, people who weren’t White, people with Medicaid, and those with worsening cancer status were among those more likely to have completely depleted their assets within two years. Having private insurance and being married had protective effects, as we might expect. There were some interesting findings associated with the 2008 financial crisis, which also seemed to be protective. And a protective effect associated with psychiatric comorbidity deserves more thought.

It’s difficult to explain away any (let alone all) of the magnitude of these findings. The analysis seems robust. But, given all other evidence available about out-of-pocket costs for cancer patients in the US, it should be shocking but not unexpected. The authors describe financial toxicity as ‘unintended’. There’s nothing unintended about this. Policymakers in the US keep deciding that they’d prefer to destroy the lives of sick people than allow for the spreading of that financial risk.

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Rita Faria’s journal round-up for 10th December 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.

Calculating the expected value of sample information using efficient nested Monte Carlo: a tutorial. Value in Health [PubMed] Published 17th July 2018

The expected value of sample information (EVSI) represents the added benefit from collecting new information on specific parameters in future studies. It can be compared to the cost of conducting these future studies to calculate the expected net benefit of sampling. The objective is to help inform which study design is best, given the information it can gather and its costs. The theory and methods to calculate EVSI have been around for some time, but we rarely see it in applied economic evaluations.

In this paper, Anna Heath and Gianluca Baio present a tutorial about how to implement a method they had previously published on, which is more computationally efficient than the standard nested Monte Carlo simulations.

The authors start by explaining the method in theory, then illustrate it with a simple worked example. I’ll admit that I got a bit lost with the theory, but I found that the example made it much clearer. They demonstrate the method’s performance using a previously published cost-effectiveness model. Additionally, they have very helpfully published a suite of functions to apply this method in practice.

I really enjoyed reading this paper, as it takes the reader step-by-step through the method. However, I wasn’t sure about when this method is applicable, given that the authors note that it requires a large number of probabilistic simulations to perform well, and it is only appropriate when EVPPI is high. The issue is, how large is large and how high is high? Hopefully, these and other practical questions are on the list for this brilliant research team.

As an applied researcher, I find tutorial papers such as this one incredibly useful to learn new methods and help implement them in practice. Thanks to work such as this one and others, we’re getting close to making value of information analysis a standard element of cost-effectiveness studies.

Future costs in cost-effectiveness analyses: past, present, future. PharmacoEconomics [PubMed] Published 26th November 2018

Linda de Vries, Pieter van Baal and Werner Brouwer help illuminate the debate on future costs with this fascinating paper. Future costs are the costs of resources used by patients during the years of life added by the technology under evaluation. Future costs can be distinguished between related or unrelated, depending on whether the resources are used for the target disease. They can also be distinguished between medical or non-medical, depending on whether the costs fall on the healthcare budget.

The authors very skilfully summarise the theoretical literature on the inclusion of future costs. They conclude that future related and unrelated medical costs should be included and present compelling arguments to do so.

They also discuss empirical research, such as studies that estimate future unrelated costs. The references are a useful starting point for other researchers. For example, I noted that there is a tool to include future unrelated medical costs in the Netherlands and some studies on their estimation in the UK (see, for example, here).

There is a thought-provoking section on ethical concerns. If unrelated costs are included, technologies that increase the life expectancy of people who need a lot of resources will look less cost-effective. The authors suggest that these issues should not be concealed in the analysis, but instead dealt with in the decision-making process.

This is an enjoyable paper that provides an overview of the literature on future costs. I highly recommend it to get up to speed with the arguments and the practical implications. There is clearly a case for including future costs, and the question now is whether the cost-effectiveness practice follows suit.

Cost-utility analysis using EQ-5D-5L data: does how the utilities are derived matter? Value in Health Published 4th July 2018

We’ve recently become spoilt for choice when it comes to the EQ-5D. To obtain utility values, just in the UK, there are a few options: the 3L tariff, the 5L tariff, and crosswalk tariffs by Ben van Hout and colleagues and Mónica Hernandez and colleagues [PDF]. Which one to choose? And does it make any difference?

Fan Yang and colleagues have done a good job in getting us closer to the answer. They estimated utilities obtained from EQ-5D-5L data using the 5L value set and crosswalk tariffs to EQ-5D-3L and tested the values in cost-effectiveness models of hemodialysis compared to peritoneal dialysis.

Reassuringly, hemodialysis had always greater utilities than peritoneal dialysis. However, the magnitude of the difference varied with the approach. Therefore, using either EQ-5D-5L or the crosswalk tariff to EQ-5D-3L can influence the cost-effectiveness results. These results are in line with earlier work by Mónica Hernandez and colleagues, who compared the EQ-5D-3L with the EQ-5D-5L.

The message is clear in that both the type of EQ-5D questionnaire and the EQ-5D tariff makes a difference to the cost-effectiveness results. This can have huge policy implications as decisions by HTA agencies, such as NICE, depend on these results.

Which EQ-5D-5L to use in a new primary research study remains an open question. In the meantime, NICE recommends the use of the EQ-5D-3L or, if EQ-5D-5L was collected, Ben van Hout and colleagues’ mapping function to the EQ-5D-3L. Hopefully, a definite answer won’t be long in coming.

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Chris Sampson’s journal round-up for 19th November 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.

Valuation of health states considered to be worse than death—an analysis of composite time trade-off data from 5 EQ-5D-5L valuation studies. Value in Health Published 12th November 2018

I have a problem with the idea of health states being ‘worse than dead’, and I’ve banged on about it on this blog. Happily, this new article provides an opportunity for me to continue my campaign. Health state valuation methods estimate how much a person prefers being in a more healthy state. Positive values are easy to understand; 1.0 is twice as good as 0.5. But how about the negative values? Is -1.0 twice as bad as -0.5? How much worse than being dead is that? The purpose of this study is to evaluate whether or not negative EQ-5D-5L values meaningfully discriminate between different health states.

The study uses data from EQ-5D-5L valuation studies conducted in Singapore, the Netherlands, China, Thailand, and Canada. Altogether, more than 5000 people provided valuations of 10 states each. As a simple measure of severity, the authors summed the number of steps from full health in all domains, giving a value from 0 (11111) to 20 (55555). We’d expect this measure of severity of states to correlate strongly with the mean utility values derived from the composite time trade-off (TTO) exercise.

Taking Singapore as an example, the mean of positive values (states better than dead) decreased from 0.89 to 0.21 with increasing severity, which is reassuring. The mean of negative values, on the other hand, ranged from -0.98 to -0.89. Negative values were clustered between -0.5 and -1.0. Results were similar across the other countries. In all except Thailand, observed negative values were indistinguishable from random noise. There was no decreasing trend in mean utility values as severity increased for states worse than dead. A linear mixed model with participant-specific intercepts and an ANOVA model confirmed the findings.

What this means is that we can’t say much about states worse than dead except that they are worse than dead. How much worse doesn’t relate to severity, which is worrying if we’re using these values in trade-offs against states better than dead. Mostly, the authors frame this lack of discriminative ability as a practical problem, rather than anything more fundamental. The discussion section provides some interesting speculation, but my favourite part of the paper is an analogy, which I’ll be quoting in future: “it might be worse to be lost at sea in deep waters than in a pond, but not in any way that truly matters”. Dead is dead is dead.

Determining value in health technology assessment: stay the course or tack away? PharmacoEconomics [PubMed] Published 9th November 2018

The cost-per-QALY approach to value in health care is no stranger to assault. The majority of criticisms are ill-founded special pleading, but, sometimes, reasonable tweaks and alternatives have been proposed. The aim of this paper was to bring together a supergroup of health economists to review and discuss these reasonable alternatives. Specifically, the questions they sought to address were: i) what should health technology assessment achieve, and ii) what should be the approach to value-based pricing?

The paper provides an unstructured overview of a selection of possible adjustments or alternatives to the cost-per-QALY method. We’re very briefly introduced to QALY weighting, efficiency frontiers, and multi-criteria decision analysis. The authors don’t tell us why we ought (or ought not) to adopt these alternatives. I was hoping that the paper would provide tentative answers to the normative questions posed, but it doesn’t do that. It doesn’t even outline the thought processes required to answer them.

The purpose of this paper seems to be to argue that alternative approaches aren’t sufficiently developed to replace the cost-per-QALY approach. But it’s hardly a strong defence. I’m a big fan of the cost-per-QALY as a necessary (if not sufficient) part of decision making in health care, and I agree with the authors that the alternatives are lacking in support. But the lack of conviction in this paper scares me. It’s tempting to make a comparison between the EU and the QALY.

How can we evaluate the cost-effectiveness of health system strengthening? A typology and illustrations. Social Science & Medicine [PubMed] Published 3rd November 2018

Health care is more than the sum of its parts. This is particularly evident in low- and middle-income countries that might lack strong health systems and which therefore can’t benefit from a new intervention in the way a strong system could. Thus, there is value in health system strengthening. But, as the authors of this paper point out, this value can be difficult to identify. The purpose of this study is to provide new methods to model the impact of health system strengthening in order to support investment decisions in this context.

The authors introduce standard cost-effectiveness analysis and economies of scope as relevant pieces of the puzzle. In essence, this paper is trying to marry the two. An intervention is more likely to be cost-effective if it helps to provide economies of scope, either by making use of an underused platform or providing a new platform that would improve the cost-effectiveness of other interventions. The authors provide a typology with three types of health system strengthening: i) investing in platform efficiency, ii) investing in platform capacity, and iii) investing in new platforms. Examples are provided for each. Simple mathematical approaches to evaluating these are described, using scaling factors and disaggregated cost and outcome constraints. Numerical demonstrations show how these approaches can reveal differences in cost-effectiveness that arise through changes in technical efficiency or the opportunity cost linked to health system strengthening.

This paper is written with international development investment decisions in mind, and in particular the challenge of investments that can mostly be characterised as health system strengthening. But it’s easy to see how many – perhaps all – health services are interdependent. If anything, the broader impact of new interventions on health systems should be considered as standard. The methods described in this paper provide a useful framework to tackle these issues, with food for thought for anybody engaged in cost-effectiveness analysis.

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