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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Meeting round-up: Society for Medical Decision Making 17th Biennial European Conference

The Society for Medical Decision Making (SMDM) held their 17th European Conference between 10th and 12th June at the Stadsgehoorzaal in Leiden, the Netherlands. The meeting was chaired by Anne Stiggelbout and Ewout Steyerberg who, along with Uwe Siebert, welcomed us (early) on Monday morning. Some delegates arrived on Sunday for short courses on a range of topics, from modelling in R and causal inference to the psychology of decision making.

Although based in the US, SMDM holds biennial meetings in Europe which are generally attended by delegates from around the world. Around 300 delegates were in attendance at this meeting, travelling from Toronto to Tehran.

The meeting was ‘Patients Included’ and we were introduced to around 10 patients and caregivers on the first morning. They confidently asked questions and gave comments after the presentations and the plenary, sharing their real-world experience to provide context to findings.

There were five ‘oral abstract’ sessions each comprising six presentations in 15 minute slots (10 minutes long with 5 minutes for audience questions). The sessions covered empirical research relating to physician and patient decision-making, and quantitative valuation and evaluation. Popular applied areas were prostate cancer, breast cancer and precision medicine.

Running in parallel to the oral presentations, workshops were dealing with methodological issues relating to health economics, shared decision-making and psychology.

Four poster sessions, conveniently surrounding the refreshment table, attracted delegates in the morning, breaks and lunch. SMDM provides some of the best poster sessions: posters are always of high quality which means poster sessions are always well attended.

One of the highlights of the meeting was the plenary presentation by Sir David Spiegelhalter who spoke about the challenges of communicating benefits and harms (often probabilities) impartially. Sir David gave examples from the UK’s national breast screening programme to show how presenting information can change people’s interpretation of risk. He also drew on examples of ‘nudges’ which may involve providing information in a persuasive rather than informing way in order to manipulate behaviour. Sir David gave us examples of materials which had been redesigned to improve both patients’ and clinicians’ understanding of the information of benefits and harms. The session concluded with a short video about how Ugandan primary school children have reading comic strips to help interpret information and find facts about the benefits and harms of healthcare interventions.

The European SMDM meeting was thoroughly enjoyable and very interesting. The standard of oral and poster presentations was very high, and the environment was very friendly and conducive to networking.

The next North American meeting is in Montreal (October 2018) and the next European meeting will be in 2020 (location to be confirmed).

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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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