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

Paying for kidneys? A randomized survey and choice experiment. American Economic Review [RePEc] Published August 2019

This paper starts with a quote from Alvin Roth about ‘repugnant transactions’, of which markets for organs provide a prime example. This idea of ‘repugnant transactions’ has been hijacked by some pop economists to represent the stupid opinions of non-economists. If you ask me, markets for organs aren’t repugnant, they just seem like a very bad idea in terms of both efficiency and equity. But it doesn’t matter what I think; it matters what the people of the United States think.

The authors of this study conducted an online survey with a representative sample of 2,666 Americans. Each respondent was randomised to evaluate one of eight systems compared with the current system. The eight systems differed with respect to i) cash or non-cash compensation of ii) different sizes ($30,000 or $100,000), iii) paid by either a public agency or the organ recipient. Participants made five binary choices that differed according to the gain – in transplants generated – associated with the new system. Half of the participants were also asked to express moral judgements.

Both the system features (e.g. who pays) and the outcomes of the new system influenced people’s choices. Broadly speaking, the results suggest that people aren’t opposed to donors being paid, but are opposed to patients paying. (Remember, we’re talking about the US here!). Around 21% of respondents opposed payment no matter what, 46% were in favour no matter what, and 18% were sensitive to the gain in the number of transplants. A 10% point increase in transplants resulted in a 2.6% point increase in support. Unsurprisingly, individuals’ moral judgements were predictive of the attitudes they expressed, particularly with respect to fairness. The authors describe their results as exhibiting ‘strong polarisation’, which is surely inevitable for questions that involve moral judgement.

Being in AER, this is a long meandering paper with extensive analyses and thoroughly reported results. There’s lots of information and findings that I can’t share here. It’s a valuable study with plenty of food for thought, but I can’t help but think that it is, methodologically, a bit weak. If we want to understand the different views in society, surely some Q methodology would be more useful than a basic online survey. And if we want to elicit stated preferences, surely a discrete choice experiment with a well-thought-out efficient design would give us more meaningful results.

Estimating local need for mental healthcare to inform fair resource allocation in the NHS in England: cross-sectional analysis of national administrative data linked at person level. The British Journal of Psychiatry [PubMed] Published 8th August 2019

The need to fairly (and efficiently) allocate NHS resources across the country played an important part in the birth of health economics in the UK, and resulted in resource allocation formulas. Since 1996 there has been a separate formula for mental health services, which is periodically updated. This study describes the work undertaken for the latest update.

The model is based on predicting service use and total mental health care costs observed in 2015 from predictors in the years 2013-2014, to inform allocations in 2019-2024. Various individual-level data sources available to the NHS were used for 43.7 million people registered with a GP practice and over the age of 20. The cost per patient who used mental health services ranged from £94 to over one million, averaging around £2,000. The predictor variables included individual indicators such as age, sex, ethnicity, physical diagnoses, and household type (e.g. number of adults and kids). The model also used variables observed at the local or GP practice level, such as the proportion of people receiving out-of-work benefits and the distance from the mental health trust. All of this got plugged into a good old OLS regression. From individual-level predictions, the researchers created aggregated indices of need for each clinical commission group (CCG).

A lot went into the model, which explained 99% of the variation in costs between CCGs. A key way in which this model differs from previous versions is that it relies on individual-level indicators rather than those observed at the level of GP practice or CCG. There was a lot of variation in the CCG need indices, ranging from 0.65 for Surrey Heath to 1.62 for Southwark, where 1.00 is the average. You’ll need to check the online appendices for your own CCG’s level of need (Lewisham: 1.52). As one might expect, the researchers observed a strong correlation between a CCG’s need index and the CCG’s area’s level of deprivation. Compared with previous models, this new model indicates a greater allocation of resources to more deprived and older populations.

Measuring, valuing and including forgone childhood education and leisure time costs in economic evaluation: methods, challenges and the way forward. Social Science & Medicine [PubMed] Published 7th August 2019

I’m a ‘societal perspective’ sceptic, not because I don’t care about non-health outcomes (though I do care less) but because I think it’s impossible to capture everything that is of value to society, and that capturing just a few things will introduce a lot of bias and noise. I would also deny that time has any intrinsic value. But I do think we need to do a better job of evaluating interventions for children. So I expected this paper to provide me with a good mix of satisfaction and exasperation.

Health care often involves a loss of leisure or work time, which can constitute an opportunity cost and is regularly included in economic evaluations – usually proxied by wages – for adults. The authors outline the rationale for considering ‘time-related’ opportunity costs in economic evaluations and describe the nature of lost time for children. For adults, the distinction is generally between paid or unpaid work and leisure time. Arguably, this distinction is not applicable to children. Two literature reviews are described. One looked at economic evaluations in the context of children’s health, to see how researchers have valued lost time. The other sought to identify ideas about the value of lost time for children from a broader literature.

The authors do a nice job of outlining how difficult it is to capture non-health-related costs and outcomes in the context of childhood. There is a handful of economic evaluations that have tried to measure and value children’s foregone time. The valuations generally focussed on the costs of childcare rather than the costs to the child, though one looked at the rate of return to education. There wasn’t a lot to go off in the non-health literature, which mostly relates to adults. From what there is, the recommendation is to capture absence from formal education and foregone leisure time. Of course, consideration needs to be given to the importance of lost time and thus the value of capturing it in research. We also need to think about the risk of double counting. When it comes to measurement, we can probably use similar methods as we would for adults, such as diaries. But we need very different approaches to valuation. On this, the authors found very little in the way of good examples to follow. More research needed.

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Chris Sampson’s journal round-up for 18th February 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.

An educational review about using cost data for the purpose of cost-effectiveness analysis. PharmacoEconomics [PubMed] Published 12th February 2019

Costing can seem like a cinderella method in the health economist’s toolkit. If you’re working on an economic evaluation, estimating resource use and costs can be tedious. That is perhaps why costing methodology has been relatively neglected in the literature compared to health state valuation (for example). This paper tries to redress the balance slightly by providing an overview of the main issues in costing, explaining why they’re important, so that we can do a better job. The issues are more complex than many assume.

Supported by a formidable reference list (n=120), the authors tackle 9 issues relating to costing: i) costs vs resource use; ii) trial-based vs model-based evaluations; iii) costing perspectives; iv) data sources; v) statistical methods; vi) baseline adjustments; vii) missing data; viii) uncertainty; and ix) discounting, inflation, and currency. It’s a big paper with a lot to say, so it isn’t easily summarised. Its role is as a reference point for us to turn to when we need it. There’s a stack of papers and other resources cited in here that I wasn’t aware of. The paper itself doesn’t get technical, leaving that to the papers cited therein. But the authors provide a good discussion of the questions that ought to be addressed by somebody designing a study, relating to data collection and analysis.

The paper closes with some recommendations. The main one is that people conducting cost-effectiveness analysis should think harder about why they’re making particular methodological choices. The point is also made that new developments could change the way we collect and analyse cost data. For example, the growing use of observational data demands that greater consideration be given to unobserved confounding. Costing methods are important and interesting!

A flexible open-source decision model for value assessment of biologic treatment for rheumatoid arthritis. PharmacoEconomics [PubMed] Published 9th February 2019

Wherever feasible, decision models should be published open-source, so that they can be reviewed, reused, recycled, or, perhaps, rejected. But open-source models are still a rare sight. Here, we have one for rheumatoid arthritis. But the paper isn’t really about the model. After all, the model and supporting documentation are already available online. Rather, the paper describes the reasoning behind publishing a model open-source, and the process for doing so in this case.

This is the first model released as part of the Open Source Value Project, which tries to convince decision-makers that cost-effectiveness models are worth paying attention to. That is, it’s aimed at the US market, where models are largely ignored. The authors argue that models need to be flexible to be valuable into the future and that, to achieve this, four steps should be followed in the development: 1) release the initial model, 2) invite feedback, 3) convene an expert panel to determine actions in light of the feedback, and 4) revise the model. Then, repeat as necessary. Alongside this, people with the requisite technical skills (i.e. knowing how to use R, C++, and GitHub) can proffer changes to the model whenever they like. This paper was written after step 3 had been completed, and the authors report receiving 159 comments on their model.

The model itself (which you can have a play with here) is an individual patient simulation, which is set-up to evaluate a variety of treatment scenarios. It estimates costs and (mapped) QALYs and can be used to conduct cost-effectiveness analysis or multi-criteria decision analysis. The model was designed to be able to run 32 different model structures based on different assumptions about treatment pathways and outcomes, meaning that the authors could evaluate structural uncertainties (which is a rare feat). A variety of approaches were used to validate the model.

The authors identify several challenges that they experienced in the process, including difficulties in communication between stakeholders and the large amount of time needed to develop, test, and describe a model of this sophistication. I would imagine that, compared with most decision models, the amount of work underlying this paper is staggering. Whether or not that work is worthwhile depends on whether researchers and policymakers make us of the model. The authors have made it as easy as possible for stakeholders to engage with and build on their work, so they should be hopeful that it will bear fruit.

EQ-5D-Y-5L: developing a revised EQ-5D-Y with increased response categories. Quality of Life Research [PubMed] Published 9th February 2019

The EQ-5D-Y has been a slow burner. It’s been around 10 years since it first came on the scene, but we’ve been without a value set and – with the introduction of the EQ-5D-5L – the questionnaire has lost some comparability with its adult equivalent. But the EQ-5D-Y has almost caught-up, and this study describes part of how that’s been achieved.

The reason to develop a 5L version for the EQ-5D-Y is the same as for the adult version – to reduce ceiling effects and improve sensitivity. A selection of possible descriptors was identified through a review of the literature. Focus groups were conducted with children between 8 and 15 years of age in Germany, Spain, Sweden, and the UK in order to identify labels that can be understood by young people. Specifically, the researchers wanted to know the words used by children and adolescents to describe the quantity or intensity of health problems. Participants ranked the labels according to severity and specified which labels they didn’t like. Transcripts were analysed using thematic content analysis. Next, individual interviews were conducted with 255 participants across the four countries, which involved sorting and response scaling tasks. Younger children used a smiley scale. At this stage, both 4L and 5L versions were being considered. In a second phase of the research, cognitive interviews were used to test for comprehensibility and feasibility.

A 5-level version was preferred by most, and 5L labels were identified in each language. The English version used terms like ‘a little bit’, ‘a lot’, and ‘really’. There’s plenty more research to be done on the EQ-5D-Y-5L, including psychometric testing, but I’d expect it to be coming to studies near you very soon. One of the key takeaways from this study, and something that I’ve been seeing more in research in recent years, is that kids are smart. The authors make this point clear, particulary with respect to the response scaling tasks that were conducted with children as young as 8. Decision-making criteria and frameworks that relate to children should be based on children’s preferences and ideas.

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James Lomas’s journal round-up for 21st May 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.

Decision making for healthcare resource allocation: joint v. separate decisions on interacting interventions. Medical Decision Making [PubMed] Published 23rd April 2018

While it may be uncontroversial that including all of the relevant comparators in an economic evaluation is crucial, a careful examination of this statement raises some interesting questions. Which comparators are relevant? For those that are relevant, how crucial is it that they are not excluded? The answer to the first of these questions may seem obvious, that all feasible mutually exclusive interventions should be compared, but this is in fact deceptive. Dakin and Gray highlight inconsistency between guidelines as to what constitutes interventions that are ‘mutually exclusive’ and so try to re-frame the distinction according to whether interventions are ‘incompatible’ – when it is physically impossible to implement both interventions simultaneously – and, if not, whether interventions are ‘interacting’ – where the costs and effects of the simultaneous implementation of A and B do not equal the sum of these parts. What I really like about this paper is that it has a very pragmatic focus. Inspired by policy arrangements, for example single technology appraisals, and the difficulty in capturing all interactions, Dakin and Gray provide a reader-friendly flow diagram to illustrate cases where excluding interacting interventions from a joint evaluation is likely to have a big impact, and furthermore propose a sequencing approach that avoids the major problems in evaluating separately what should be considered jointly. Essentially when we have interacting interventions at different points of the disease pathway, evaluating separately may not be problematic if we start at the end of the pathway and move backwards, similar to the method of backward induction used in sequence problems in game theory. There are additional related questions that I’d like to see these authors turn to next, such as how to include interaction effects between interventions and, in particular, how to evaluate system-wide policies that may interact with a very large number of interventions. This paper makes a great contribution to answering all of these questions by establishing a framework that clearly distinguishes concepts that had previously been subject to muddied thinking.

When cost-effective interventions are unaffordable: integrating cost-effectiveness and budget impact in priority setting for global health programs. PLoS Medicine [PubMed] Published 2nd October 2017

In my opinion, there are many things that health economists shouldn’t try to include when they conduct cost-effectiveness analysis. Affordability is not one of these. This paper is great, because Bilinski et al shine a light on the worldwide phenomenon of interventions being found to be ‘cost-effective’ but not affordable. A particular quote – that it would be financially impossible to implement all interventions that are found to be ‘very cost-effective’ in many low- and middle-income countries – is quite shocking. Bilinski et al compare and contrast cost-effectiveness analysis and budget impact analysis, and argue that there are four key reasons why something could be ‘cost-effective’ but not affordable: 1) judging cost-effectiveness with reference to an inappropriate cost-effectiveness ‘threshold’, 2) adoption of a societal perspective that includes costs not falling upon the payer’s budget, 3) failing to make explicit consideration of the distribution of costs over time and 4) the use of an inappropriate discount rate that may not accurately reflect the borrowing and investment opportunities facing the payer. They then argue that, because of this, cost-effectiveness analysis should be presented along with budget impact analysis so that the decision-maker can base a decision on both analyses. I don’t disagree with this as a pragmatic interim solution, but – by highlighting these four reasons for divergence of results with such important economic consequences – I think that there will be further reaching implications of this paper. To my mind, Bilinski et al essentially serves as a call to arms for researchers to try to come up with frameworks and estimates so that the conduct of cost-effectiveness analysis can be improved in order that paradoxical results are no longer produced, decisions are more usefully informed by cost-effectiveness analysis, and the opportunity costs of large budget impacts are properly evaluated – especially in the context of low- and middle-income countries where the foregone health from poor decisions can be so significant.

Patient cost-sharing, socioeconomic status, and children’s health care utilization. Journal of Health Economics [PubMed] Published 16th April 2018

This paper evaluates a policy using a combination of regression discontinuity design and difference-in-difference methods. Not only does it do that, but it tackles an important policy question using a detailed population-wide dataset (a set of linked datasets, more accurately). As if that weren’t enough, one of the policy reforms was actually implemented as a result of a vote where two politicians ‘accidentally pressed the wrong button’, reducing concerns that the policy may have in some way not been exogenous. Needless to say I found the method employed in this paper to be a pretty convincing identification strategy. The policy question at hand is about whether demand for GP visits for children in the Swedish county of Scania (Skåne) is affected by cost-sharing. Cost-sharing for GP visits has occurred for different age groups over different periods of time, providing the basis for regression discontinuities around the age threshold and treated and control groups over time. Nilsson and Paul find results suggesting that when health care is free of charge doctor visits by children increase by 5-10%. In this context, doctor visits happened subject to telephone triage by a nurse and so in this sense it can be argued that all of these visits would be ‘needed’. Further, Nilsson and Paul find that the sensitivity to price is concentrated in low-income households, and is greater among sickly children. The authors contextualise their results very well and, in addition to that context, I can’t deny that it also particularly resonated with me to read this approaching the 70th birthday of the NHS – a system where cost-sharing has never been implemented for GP visits by children. This paper is clearly also highly relevant to that debate that has surfaced again and again in the UK.

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