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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Chris Sampson’s journal round-up for 5th 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.

Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration. The European Journal of Health Economics [PubMed] Published 29th October 2018

Health care is increasingly personalised. This creates the need to evaluate interventions for smaller and smaller subgroups as patient heterogeneity is taken into account. And this usually means we lack the statistical power to have confidence in our findings. The purpose of this paper is to consider the usefulness of a tool that hasn’t previously been employed in economic evaluation – the subpopulation treatment effect pattern plot (STEPP). STEPP works by assessing the interaction between treatments and covariates in different subgroups, which can then be presented graphically. Imagine your X-axis with the values defining the subgroups and your Y-axis showing the treatment outcome. This information can then be used to determine which subgroups exhibit positive outcomes.

This study uses data from a trial-based economic evaluation in heart failure, where patients’ 18-month all-cause mortality risk was estimated at baseline before allocation to one of three treatment strategies. For the STEPP procedure, the authors use baseline risk to define subgroups and adopt net monetary benefit at the patient level as the outcome. The study makes two comparisons (between three alternative strategies) and therefore presents two STEPP figures. The STEPP figures are used to identify subgroups, which the authors apply in a stratified cost-effectiveness analysis, estimating net benefit in each defined risk subgroup.

Interpretation of the STEPPs is a bit loose, with no hard decision rules. The authors suggest that one of the STEPPs shows no clear relationship between net benefit and baseline risk in terms of the cost-effectiveness of the intervention (care as usual vs basic support). The other STEPP shows that, on average, people with baseline risk below 0.16 have a positive net benefit from the intervention (intensive support vs basic support), while those with higher risk do not. The authors evaluate this stratification strategy against an alternative stratification strategy (based on the patient’s New York Heart Association class) and find that the STEPP-based approach is expected to be more cost-effective. So the key message seems to be that STEPP can be used as a basis for defining subgroups as cost-effectively as possible.

I’m unsure about the extent to which this is a method that deserves to have its own name, insofar as it is used in this study. I’ve seen plenty of studies present a graph with net benefit on the Y-axis and some patient characteristic on the X-axis. But my main concern is about defining subgroups on the basis of net monetary benefit rather than some patient characteristic. Is it OK to deny treatment to subgroup A because treatment costs are higher than in subgroup B, even if treatment is cost-effective for the entire population of A+B? Maybe, but I think that creates more challenges than stratification on the basis of treatment outcome.

Using post-market utilisation analysis to support medicines pricing policy: an Australian case study of aflibercept and ranibizumab use. Applied Health Economics and Health Policy [PubMed] Published 25th October 2018

The use of ranibizumab and aflibercept has been a hot topic in the UK, where NHS providers feel that they’ve been bureaucratically strong-armed into using an incredibly expensive drug to treat certain eye conditions when a cheaper and just-as-effective alternative is available. Seeing how other countries have managed prices in this context could, therefore, be valuable to the NHS and other health services internationally. This study uses data from Australia, where decisions about subsidising medicines are informed by research into how drugs are used after they come to market. Both ranibizumab (in 2007) and aflibercept (in 2012) were supported for the treatment of age-related macular degeneration. These decisions were based on clinical trials and modelling studies, which also showed that the benefit of ~6 aflibercept prescriptions equated to the benefit of ~12 ranibizumab prescriptions, justifying a higher price-per-injection for aflibercept.

In the UK and US, aflibercept attracts a higher price. The authors assume that this is because of the aforementioned trial data relating to the number of doses. However, in Australia, the same price is paid for aflibercept and ranibizumab. This is because a post-market analysis showed that, in practice, ranibizumab and aflibercept had a similar dose frequency. The purpose of this study is to see whether this is because different groups of patients are being prescribed the two drugs. If they are, then we might anticipate heterogenous treatment outcomes and thus a justification for differential pricing. Data were drawn from an administrative claims database for 208,000 Australian veterans for 2007-2017. The monthly number of aflibercept and ranibizumab prescriptions was estimated for each person, showing that total prescriptions increased steadily over the period, with aflibercept taking around half the market within a year of its approval. Ranibizumab initiators were slightly older in the post-aflibercept era but, aside from that, there were no real differences identified. When it comes to the prescription of ranibizumab or aflibercept, gender, being in residential care, remoteness of location, and co-morbidities don’t seem to be important. Dispensing rates were similar, at around 3 prescriptions during the first 90 days and around 9 prescriptions during the following 12 months.

The findings seem to support Australia’s decision to treat ranibizumab and aflibercept as substitutes at the same price. More generally, they support the idea that post-market utilisation assessments can (and perhaps should) be used as part of the health technology assessment and reimbursement process.

Do political factors influence public health expenditures? Evidence pre- and post-great recession. The European Journal of Health Economics [PubMed] Published 24th October 2018

There is mixed evidence about the importance of partisanship in public spending, and very little relating specifically to health care. I’d be worried if political factors didn’t influence public spending on health, given that that’s a definitively political issue. How the situation might be different before and after a recession is an interesting question.

The authors combined OECD data for 34 countries from 1970-2016 with the Database of Political Institutions. This allowed for the creation of variables relating to the ideology of the government and the proximity of elections. Stationary panel data models were identified as the most appropriate method for analysis of these data. A variety of political factors were included in the models, for which the authors present marginal effects. The more left-wing a government, the higher is public spending on health care, but this is only statistically significant in the period before the crisis of 2007. Before the crisis, coalition governments tended to spend more, while governments with more years in office tended to spend less. These effects also seem to disappear after 2007. Throughout the whole period, governing parties with a stronger majority tended to spend less on health care. Several of the non-political factors included in the models show the results that we would expect. GDP per capita is positively associated with health care expenditures, for example. The findings relating to the importance of political factors appear to be robust to the inclusion of other (non-political) variables and there are similar findings when the authors look at public health expenditure as a percentage of total health expenditure. In contradiction with some previous studies, proximity to elections does not appear to be important.

The most interesting finding here is that the effect of partisanship seems to have mostly disappeared – or, at least, reduced – since the crisis of 2007. Why did left-wing parties and right-wing parties converge? The authors suggest that it’s because adverse economic circumstances restrict the extent to which governments can make decisions on the basis of ideology. Though I dare say readers of this blog could come up with plenty of other (perhaps non-economic) explanations.

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Simon McNamara’s journal round-up for 1st October 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 review of NICE appraisals of pharmaceuticals 2000-2016 found variation in establishing comparative clinical effectiveness. Journal of Clinical Epidemiology [PubMed] Published 17th September 2018

The first paper in this week’s round-up is on the topic on single arm studies; specifically, the way in which the comparative effectiveness of medicines granted a marketing authorisation on the basis of single arm studies have been evaluated in NICE appraisals. If you are interested in comparative effectiveness, single arm studies are difficult to deal with. If you don’t have a control arm to refer to, how do you know what the impact of the intervention is? If you don’t know how effective the intervention is, how can you say whether it is cost-effective?

In this paper, the authors conduct a review into the way this problem has been dealt with during NICE appraisals. They do this by searching through the 489 NICE technology appraisals conducted between 2010 and 2016. The search identified 22 relevant appraisals (4% of the total). The most commonly used way of estimating comparative effectiveness (19 of 22 appraisals) was simulation of a control arm using external data – be that from observational study or a randomised trial. Of these,14 of the appraisals featured naïve comparison across studies, with no attempt made to adjust for potential differences between population groups. The three appraisals that didn’t use external data were reliant upon the use of expert opinion, or the assumption that non-responders in the intervention single-arm study could be used as a proxy for those who would receive the comparator intervention.

Interestingly, the authors find little difference between the proportion of medicines reliant on non-RCT data being approved by NICE (83%), compared to those with RCT data (86%), however; the likelihood of receiving an “optimised” (aka subgroup) approval was substantially higher for medicines with solely non-RCT data (41% vs 19%). These findings demonstrate that NICE do accept models based on single-arm studies – even if more than 75% of the comparative effectiveness estimates these models were based on were reliant upon naïve indirect comparisons, or other less robust methods.

The paper concludes by noting that single-arm studies are becoming more common (50% of the appraisals identified were conducted in 2015-2016), and suggesting that HTA and regulatory bodies should work together, to develop guidance on how to evaluate comparative effectiveness based on single-arm studies.

I thought this paper was great, and it made me reflect on a couple of things. Firstly, the fact that NICE completed such a high volume of appraisals (489) between 2010 and 2016 is extremely impressive – well done NICE. Secondly, should the EMA, or EUnetHTA, play a larger role in providing estimates of comparative effectiveness for single arm studies? Whilst different countries may reasonably make different value judgements about different health outcomes, comparative effectiveness is – at least in theory – a matter of fact, rather than values, so can’t we assess it centrally?

A QALY loss is a QALY loss is a QALY loss: a note on independence of loss aversion from health states. The European Journal of Health Economics [PubMed] Published 18th September 2018

If I told you that you would receive £10 in return for doing some work for me, and then I only paid you £5, how annoyed would you be? What about if I told you I would give you £10 but then gave you £15? How delighted would you be? If you are economically rational then these two impacts (annoyance vs being delighted) should be symmetrical; but, if you are a human, your annoyance in the first scenario would likely outweigh the delight you would experience in the second. This is the basic idea behind Kahneman and Tversky’s seminal work on “loss aversion” – we dislike changes we perceive as losses more than we like equivalent changes we perceive as gains. The second paper in this week’s roundup explores loss aversion in the context of health. Application of loss aversion in health is a really interesting idea, because it calls into question the idea that people value all QALYs equally – perhaps QALYs perceived as losses are valued more highly than QALYs perceived as gains.

In the introduction of this paper, the authors note that existing evidence suggests loss aversion is present for duration of life, and for quality of life, but note that nobody has explored whether loss aversion remains constant if the two elements change together – simply put, when it comes to loss aversion is “a QALY loss a QALY loss a QALY loss”? The authors test this idea via a choice experiment fielded in a sample of 111 Dutch students. In this experiment, the loss aversion of each participant was independently elicited for four EQ-5D-5L health states – ranging from perfect health down to a health state utility value of 0.46.

As you might have guessed from the title of the paper, the authors found that, at the aggregate level, loss aversion was not significantly different between the four health states – albeit with some variation at the individual level. For each health state, perceived losses were weighted around two times as highly as perceived gains.

I enjoyed this paper, and it prompted me to think about the consequences of loss-aversion for health economics more generally. Do health related decision makers treat the outcomes associated with a new technology as a reference-point, and so feel loss aversion when considering not funding it? From a normative perspective, should we accept asymmetry in the valuation of health? Is this simply a behavioural quirk that we should over-ride in our analyses, or should we be conforming to it and granting differential weight to outcomes depending upon whether the recipient perceives it as a gain or a loss?

Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare. The European Journal of Health Economics [PubMed] Published 18th September 2018

The final paper in this week’s roundup is on “Advanced Therapy Medicinal Products” (ATMPs). According to the European Union Regulation 1394/2007, an ATMP is a medicine which is either (1) a gene therapy, (2) a somatic-cell therapy, (3) a tissue-engineered therapy, or (4) a combination of these approaches. I don’t pretend to understand the nuances of how these medicines work, but in simple terms ATMPs aim to replace, or regenerate, human cells, tissues and organs in order to treat ill health. Whilst ATMPs are thought to have great potential in improving health and providing long-term survival gains, they present a number of challenges for Health Technology Assessment (HTA) bodies.

This paper details a meeting of a panel of experts from the UK, Germany, France and Sweden, who were tasked with identifying and discussing these challenges. The experts identified three key challenges; (1) uncertainty of long-term benefit, and subsequently cost-effectiveness, (2) discount rates, and (3) capturing the broader “value” of these therapies – including the incremental value associated with potentially curative therapies. These three challenges stem from the fact that at the point of HTA, ATMPs are likely to have immature data and the uncertain prospect of long-term benefits. The experts suggest a range of solutions to these problems, including the use of outcomes-based reimbursement schemes, initiating a multi-disciplinary forum to consider different approaches to discounting, and further research into elements of “value” not captured by current HTA processes.

Whilst there is undoubtedly merit to some of these suggestions, I couldn’t help but feel a bit uneasy about this paper due to its funder – an ATMP manufacturer. Would the authors have written this paper if they hadn’t been paid to by a company with a vested interest in changing HTA systems to suit their agenda? Whilst I don’t doubt the paper was written independently of the company, and don’t mean to cast aspersions on the authors, this does make me question how industry shapes the areas of discourse in our field – even if it doesn’t shape the specific details of that discourse.

Many of the problems raised in this paper are not unique to ATMPs, they apply equally to all interventions with the uncertain prospect of potential cure or long-term benefit (e.g. for therapies for the treatment of early stage cancer, public health interventions or immunotherapies). Science aside, funder aside, what makes ATMPs any different to these prior interventions?

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