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 17th September 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.

Does competition from private surgical centres improve public hospitals’ performance? Evidence from the English National Health Service. Journal of Public Economics Published 11th September 2018

This study looks at proper (supply-side) privatisation in the NHS. The subject is the government-backed introduction of Independent Sector Treatment Centres (ISTCs), which, in the name of profit, provide routine elective surgical procedures to NHS patients. ISTCs were directed to areas with high waiting times and began rolling out from 2003.

The authors take pre-surgery length of stay as a proxy for efficiency and hypothesise that the entry of ISTCs would improve efficiency in nearby NHS hospitals. They also hypothesise that the ISTCs would cream-skim healthier patients, leaving NHS hospitals to foot the bill for a more challenging casemix. Difference-in-difference regressions are used to test these hypotheses, the treatment group being those NHS hospitals close to ISTCs and the control being those not likely to be affected. The authors use patient-level Hospital Episode Statistics from 2002-2008 for elective hip and knee replacements.

The key difficulty here is that the trend in length of stay changed dramatically at the time ISTCs began to be introduced, regardless of whether a hospital was affected by their introduction. This is because there was a whole suite of policy and structural changes being implemented around this period, many targeting hospital efficiency. So we’re looking at comparing new trends, not comparing changes in existing levels or trends.

The authors’ hypotheses prove right. Pre-surgery length of stay fell in exposed hospitals by around 16%. The ISTCs engaged in risk selection, meaning that NHS hospitals were left with sicker patients. What’s more, the savings for NHS hospitals (from shorter pre-surgery length of stay) were more than undermined by an increase in post-surgery length of stay, which may have been due to the change in casemix.

I’m not sure how useful difference-in-difference is in this case. We don’t know what the trend would have been without the intervention because the pre-intervention trend provides no clues about it and, while the outcome is shown to be unrelated to selection into the intervention, we don’t know whether selection into the ISTC intervention was correlated with exposure to other policy changes. The authors do their best to quell these concerns about parallel trends and correlated policy shocks, and the results appear robust.

Broadly speaking, the study satisfies my prior view of for-profit providers as leeches on the NHS. Still, I’m left a bit unsure of the findings. The problem is, I don’t see the causal mechanism. Hospitals had the financial incentive to be efficient and achieve a budget surplus without competition from ISTCs. It’s hard (for me, at least) to see how reduced length of stay has anything to do with competition unless hospitals used it as a basis for getting more patients through the door, which, given that ISTCs were introduced in areas with high waiting times, the hospitals could have done anyway.

While the paper describes a smart and thorough analysis, the findings don’t tell us whether ISTCs are good or bad. Both the length of stay effect and the casemix effect are ambiguous with respect to patient outcomes. If only we had some PROMs to work with…

One method, many methodological choices: a structured review of discrete-choice experiments for health state valuation. PharmacoEconomics [PubMed] Published 8th September 2018

Discrete choice experiments (DCEs) are in vogue when it comes to health state valuation. But there is disagreement about how they should be conducted. Studies can differ in terms of the design of the choice task, the design of the experiment, and the analysis methods. The purpose of this study is to review what has been going on; how have studies differed and what could that mean for our use of the value sets that are estimated?

A search of PubMed for valuation studies using DCEs – including generic and condition-specific measures – turned up 1132 citations, of which 63 were ultimately included in the review. Data were extracted and quality assessed.

The ways in which the studies differed, and the ways in which they were similar, hint at what’s needed from future research. The majority of recent studies were conducted online. This could be problematic if we think self-selecting online panels aren’t representative. Most studies used five or six attributes to describe options and many included duration as an attribute. The methodological tweaks necessary to anchor at 0=dead were a key source of variation. Those using duration varied in terms of the number of levels presented and the range of duration (from 2 months to 50 years). Other studies adopted alternative strategies. In DCE design, there is a necessary trade-off between statistical efficiency and the difficulty of the task for respondents. A variety of methods have been employed to try and ease this difficulty, but there remains a lack of consensus on the best approach. An agreed criterion for this trade-off could facilitate consistency. Some of the consistency that does appear in the literature is due to conformity with EuroQol’s EQ-VT protocol.

Unfortunately, for casual users of DCE valuations, all of this means that we can’t just assume that a DCE is a DCE is a DCE. Understanding the methodological choices involved is important in the application of resultant value sets.

Trusting the results of model-based economic analyses: is there a pragmatic validation solution? PharmacoEconomics [PubMed] Published 6th September 2018

Decision models are almost never validated. This means that – save for a superficial assessment of their outputs – they are taken at good faith. That should be a worry. This article builds on the experience of the authors to outline why validation doesn’t take place and to try to identify solutions. This experience includes a pilot study in France, NICE Evidence Review Groups, and the perspective of a consulting company modeller.

There are a variety of reasons why validation is not conducted, but resource constraints are a big part of it. Neither HTA agencies, nor modellers themselves, have the time to conduct validation and verification exercises. The core of the authors’ proposed solution is to end the routine development of bespoke models. Models – or, at least, parts of models – need to be taken off the shelf. Thus, open source or otherwise transparent modelling standards are a prerequisite for this. The key idea is to create ‘standard’ or ‘reference’ models, which can be extensively validated and tweaked. The most radical aspect of this proposal is that they should be ‘freely available’.

But rather than offering a path to open source modelling, the authors offer recommendations for how we should conduct ourselves until open source modelling is realised. These include the adoption of a modular and incremental approach to modelling, combined with more transparent reporting. I agree; we need a shift in mindset. Yet, the barriers to open source models are – I believe – the same barriers that would prevent these recommendations from being realised. Modellers don’t have the time or the inclination to provide full and transparent reporting. There is no incentive for modellers to do so. The intellectual property value of models means that public release of incremental developments is not seen as a sensible thing to do. Thus, the authors’ recommendations appear to me to be dependent on open source modelling, rather than an interim solution while we wait for it. Nevertheless, this is the kind of innovative thinking that we need.

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Chris Sampson’s journal round-up for 27th August 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.

Ethically acceptable compensation for living donations of organs, tissues, and cells: an unexploited potential? Applied Health Economics and Health Policy [PubMed] Published 25th August 2018

Around the world, there are shortages of organs for transplantation. In economics, the debate around the need to increase organ donation can be frustratingly ignorant of ethical and distributional concerns. So it’s refreshing to see this article attempting to square concerns about efficiency and equity. The authors do so by using a ‘spheres of justice’ framework. This is the idea that different social goods should be distributed according to different principles. So, while we might be happy for brocolli and iPhones to be distributed on the basis of free exchange, we might want health to be distributed on the basis of need. The argument can be extended to state that – for a just situation to prevail – certain exchanges between these spheres of justice (e.g. health for iPhones) should never take place. This idea might explain why – as the authors demonstrate with a review of European countries – policy tends not to allow monetary compensation for organ donation.

The paper cleverly sets out to taxonomise monetary and non-monetary reimbursement and compensation with reference to individuals’ incentives and the spheres of justice principles. From this, the authors reach two key conclusions. Firstly, that (monetary) reimbursement of donors’ expenses (e.g. travel costs or lost earnings) is ethically sound as this does not constitute an incentive to donate but rather removes existing disincentives. Secondly, that non-monetary compensation could be deemed ethical.

Three possible forms of non-monetary compensation are discussed: i) prioritisation, ii) free access, and iii) non-health care-related benefits. The first could involve being given priority for receiving organs, or it could extend to the jumping of other health care waiting lists. I think this is more problematic than the authors let on because it asserts that health care should – at least in part – be distributed according to desert rather than need. The second option – free access – could mean access to health care that people would otherwise have to pay for. The third option could involve access to other social goods such as education or housing.

This is an interesting article and an enjoyable read, but I don’t think it provides a complete solution. Maybe I’m just too much of a Marxist, but I think that this – as all other proposals – fails to distribute from each according to ability. That is, we’d still expect non-monetary compensation to incentivise poorer (and on average less healthy) people to donate organs, thus exacerbating health inequality. This is because i) poorer people are more likely to need the non-monetary benefits and ii) we live in a capitalist society in which there is almost nothing that money can’t by and which is strictly non-monetary. Show me a proposal that increases donation rates from those who can most afford to donate them (i.e. the rich and healthy).

Selecting bolt-on dimensions for the EQ-5D: examining their contribution to health-related quality of life. Value in Health Published 18th August 2018

Measures such as the EQ-5D are used to describe health-related quality of life as completely and generically as possible. But there is a trade-off between completeness and the length of the questionnaire. Necessarily, there are parts of the evaluative space that measures will not capture because they are a simplification. If the bit they’re missing is important to your patient group, that’s a problem. You might fancy a bolt-on. But how do we decide which areas of the evaluative space should be more completely included in the measure? Which bolt-ons should be used? This paper seeks to provide means of answering these questions.

The article builds on an earlier piece of work that was included in an earlier journal round-up. In the previous paper, the authors used factor analysis to identify candidate bolt-ons. The goal of this paper is to outline an approach for specifying which of these candidates ought to be used. Using data from the Multi-Instrument Comparison study, the authors fit linear regressions to see how well 37 candidate bolt-on items explain differences in health-related quality of life. The 37 items correspond to six different domains: energy/vitality, satisfaction, relationships, hearing, vision, and speech. In a second test, the authors explored whether the bolt-on candidates could explain differences in health-related quality of life associated with six chronic conditions. Health-related quality of life is defined according to a visual analogue scale, which notably does not correspond to that used in the EQ-5D but rather uses a broader measure of physical, mental, and social health.

The results suggest that items related to energy/vitality, relationships, and satisfaction explained a significant part of health-related quality of life on top of the existing EQ-5D dimensions. The implication is that these could be good candidates for bolt-ons. The analysis of the different conditions was less clear.

For me, there’s a fundamental problem with this study. It moves the goals posts. Bolt-ons are about improving the extent to which a measure can more accurately represent the evaluative space that it is designed to characterise. In this study, the authors use a broader definition of health-related quality of life that – as far as I can tell – the EQ-5D is not designed to capture. We’re not dealing with bolt-ons, we’re dealing with extensions to facilitate expansions to the evaluative space. Nevertheless, the method could prove useful if combined with a more thorough consideration of the evaluative space.

Sources of health financing and health outcomes: a panel data analysis. Health Economics [PubMed] [RePEc] Published 15th August 2018

There is a growing body of research looking at the impact that health (care) spending has on health outcomes. Usually, these studies don’t explicitly look at who is doing the spending. In this study, the author distinguishes between public and private spending and attempts to identify which type of spending (if either) results in greater health improvements.

The author uses data from the World Bank’s World Development Indicators for 1995-2014. Life expectancy at birth is adopted as the primary health outcome and the key expenditure variables are health expenditure as a share of GDP and private health expenditure as a share of total health expenditure. Controlling for a variety of other variables, including some determinants of health such as income and access to an improved water source, a triple difference analysis is described. The triple difference estimator corresponds to the difference in health outcomes arising from i) differences in the private expenditure level, given ii) differences in total expenditure, over iii) time.

The key finding from the study is that, on average, private expenditure is more effective in increasing life expectancy at birth than public expenditure. The author also looks at government effectiveness, which proves crucial. The finding in favour of private expenditure entirely disappears when only countries with effective government are considered. There is some evidence that public expenditure is more effective in these countries, and this is something that future research should investigate further. For countries with ineffective governments, the implication is that policy should be directed towards increasing overall health care expenditure by increasing private expenditure.

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