Simon McNamara’s journal round-up for 6th 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.

Euthanasia, religiosity and the valuation of health states: results from an Irish EQ5D5L valuation study and their implications for anchor values. Health and Quality of Life Outcomes [PubMed] Published 31st July 2018

Do you support euthanasia? Do you think there are health states worse than death? Are you religious? Don’t worry – I am not commandeering this week’s AHE journal round-up just to bombard you with a series of difficult questions. These three questions form the foundation of the first article selected for this week’s round-up.

The paper is based upon the hypothesis that your religiosity (“adherence to religious beliefs”) is likely to impact your support for euthanasia and, subsequently, the likelihood of you valuing severe health states as worse than death. This seems like a logical hypothesis. Religions tend to be anti-euthanasia, and so it appears likely that religious people will have lower levels of support for euthanasia than non-religious people. Equally, if you don’t support the principle of euthanasia, it stands to reason that you are likely to be less willing to choose immediate death over living in a severe health state – something you would need to do for a health state to be considered as being worse than death in a time trade-off (TTO) study.

The authors test this hypothesis using a sub-sample of data (n=160) collected as part of the Irish EQ-5D-5L TTO valuation study. Perhaps unsurprisingly, the authors find evidence in support of the above hypotheses. Those that attend a religious service weekly were more likely to oppose euthanasia than those who attend a few times a year or less, and those who oppose euthanasia were less likely to give “worse than death” responses in the TTO than those that support it.

I found this paper really interesting, as it raises a number of challenging questions. If a society is made up of people with heterogeneous beliefs regarding religion, how should we balance these in the valuation of health? If a society is primarily non-religious is it fair to apply this valuation tariff to the lives of the religious, and vice versa? These certainly aren’t easy questions to answer, but may be worth reflecting on.

E-learning and health inequality aversion: A questionnaire experiment. Health Economics [PubMed] [RePEc] Published 22nd July 2018

Moving on from the cheery topic of euthanasia, what do you think about socioeconomic inequalities in health? In my home country, England, if you are from the poorest quintile of society, you can expect to experience 62 years in full health in your lifetime, whilst if you are from the richest quintile, you can expect to experience 74 years – a gap of 12 years.

In the second paper to be featured in this round-up, Cookson et al. explore the public’s willingness to sacrifice incremental population health gains in order to reduce these inequalities in health – their level of “health inequality aversion”. This is a potentially important area of research, as the vast majority of economic evaluation in health is distributionally-naïve and effectively assumes that members of the public aren’t at all concerned with inequalities in health.

The paper builds on prior work conducted by the authors in this area, in which they noted a high proportion of respondents in health inequality aversion elicitation studies appear to be so averse to inequalities that they violate monotonicity – they choose scenarios that reduce inequalities in health even if these scenarios reduce the health of the rich at no gain to the poor, or they reduce the health of the poor, or they may reduce the health of both groups. The authors hypothesise that these monotonicity violations may be due to incomplete thinking from participants, and suggest that the quality of their thinking could be improved by two e-learning educational interventions. The primary aim of the paper is to test the impact of these interventions in a sample of the UK public (n=60).

The first e-learning intervention was an animated video that described a range of potential positions that a respondent could take (e.g. health maximisation, or maximising the health of the worst off). The second was an interactive spreadsheet-based questionnaire that presented the consequences of the participant’s choices, prior to them confirming their selection. Both interventions are available online.

The authors found that the interactive tool significantly reduced the amount of extreme egalitarian (monotonicity-violating) responses, compared to a non-interactive, paper-based version of the study. Similarly, when the video was watched before completing the paper-based exercise, the number of extreme egalitarian responses reduced. However, when the video was watched before the interactive tool there was no further decrease in extreme egalitarianism. Despite this reduction in extreme egalitarianism, the median levels of inequality aversion remained high, with implied weights of 2.6 and 7.0 for QALY gains granted to someone from the poorest fifth of society, compared to the richest fifth of society for the interactive questionnaire and video groups respectively.

This is an interesting study that provides further evidence of inequality aversion, and raises further concern about the practical dominance of distributionally-naïve approaches to economic evaluation. The public does seem to care about distribution. Furthermore, the paper demonstrates that participant responses to inequality aversion exercises are shaped by the information given to them, and the way that information is presented. I look forward to seeing more studies like this in the future.

A new method for valuing health: directly eliciting personal utility functions. The European Journal of Health Economics [PubMed] [RePEc] Published 20th July 2018

Last, but not least, for this round-up, is a paper by Devlin et al. on a new method for valuing health.

The relative valuation of health states is a pretty important topic for health economists. If we are to quantify the effectiveness, and subsequently cost-effectiveness, of an intervention, we need to understand which health states are better than others, and how much better they are. Traditionally, this is done by asking members of the public to choose between different health profiles featuring differing levels of fulfilment of a range of domains of health, in order to ‘uncover’ the relative importance the respondent places on these domains, and levels. These can then be used in order to generate social tariffs that assign a utility value to a given health state for use in economic evaluation.

The authors point out that, in the modern day, valuation studies can be conducted rapidly, and at scale, online, but at the potential cost of deliberation from participants, and the resultant risk of heuristic dominated decision making. In response to this, the authors propose a new method – direct elicitation of personal utility functions, and pilot its use for the valuation of EQ-5D in a sample of the English public (n=76).

The proposed approach differs from traditional approaches in three key ways. Firstly, instead of simply attempting to infer the relative importance that participants place on differing domains based upon choices between health profiles, the respondents are asked directly about the relative importance they place on differing domains of health, prior to validating these with profile choices. Secondly, the authors place a heavy emphasis on deliberation, and the construction, rather than uncovering, of preferences during the elicitation exercises. Thirdly, a “personal utility function” for each individual is constructed (in effect a personal EQ-5D tariff), and these individual utility functions are subsequently aggregated into a social utility function.

In the pilot, the authors find that the method appears feasible for wider use, albeit with some teething troubles associated with the computer-based tool developed to implement it, and the skills of the interviewers.

This direct method raises an interesting question for health economics – should we be inferring preferences based upon choices that differ in terms of certain attributes, or should we just ask directly about the attributes? This is a tricky question. It is possible that the preferences elicited via these different approaches could result in different preferences – if they do, on what grounds should we choose one or other? This requires a normative judgment, and at present, it appears both are (potentially) as legitimate as each other.

Whilst the authors apply this direct method to the valuation of health, I don’t see why similar approaches couldn’t be applied to any multi-attribute choice experiment. Keep your eyes out for future uses of it in valuation, and perhaps beyond? It will be interesting to see how it develops.

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Chris Sampson’s journal round-up for 11th June 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.

End-of-life healthcare expenditure: testing economic explanations using a discrete choice experiment. Journal of Health Economics Published 7th June 2018

People incur a lot of health care costs at the end of life, despite the fact that – by definition – they aren’t going to get much value from it (so long as we’re using QALYs, anyway). In a 2007 paper, Gary Becker and colleagues put forward a theory for the high value of life and high expenditure on health care at the end of life. This article sets out to test a set of hypotheses derived from this theory, namely: i) higher willingness-to-pay (WTP) for health care with proximity to death, ii) higher WTP with greater chance of survival, iii) societal WTP exceeds individual WTP due to altruism, and iv) societal WTP may exceed individual WTP due to an aversion to restricting access to new end-of-life care. A further set of hypotheses relating to the ‘pain of risk-bearing’ is also tested. The authors conducted an online discrete choice experiment (DCE) with 1,529 Swiss residents, which asked respondents to suppose that they had terminal cancer and was designed to elicit WTP for a life-prolonging novel cancer drug. Attributes in the DCE included survival, quality of life, and ‘hope’ (chance of being cured). Individual WTP – using out-of-pocket costs – and societal WTP – based on social health insurance – were both estimated. The overall finding is that the hypotheses are on the whole true, at least in part. But the fact is that different people have different preferences – the authors note that “preferences with regard to end-of-life treatment are very heterogeneous”. The findings provide evidence to explain the prevailing high level of expenditure in end of life (cancer) care. But the questions remain of what we can or should do about it, if anything.

Valuation of preference-based measures: can existing preference data be used to generate better estimates? Health and Quality of Life Outcomes [PubMed] Published 5th June 2018

The EuroQol website lists EQ-5D-3L valuation studies for 27 countries. As the EQ-5D-5L comes into use, we’re going to see a lot of new valuation studies in the pipeline. But what if we could use data from one country’s valuation to inform another’s? The idea is that a valuation study in one country may be able to ‘borrow strength’ from another country’s valuation data. The author of this article has developed a Bayesian non-parametric model to achieve this and has previously applied it to UK and US EQ-5D valuations. But what about situations in which few data are available in the country of interest, and where the country’s cultural characteristics are substantially different. This study reports on an analysis to generate an SF-6D value set for Hong Kong, firstly using the Hong Kong values only, and secondly using the UK value set as a prior. As expected, the model which uses the UK data provided better predictions. And some of the differences in the valuation of health states are quite substantial (i.e. more than 0.1). Clearly, this could be a useful methodology, especially for small countries. But more research is needed into the implications of adopting the approach more widely.

Can a smoking ban save your heart? Health Economics [PubMed] Published 4th June 2018

Here we have another Swiss study, relating to the country’s public-place smoking bans. Exposure to tobacco smoke can have an acute and rapid impact on health to the extent that we would expect an immediate reduction in the risk of acute myocardial infarction (AMI) if a smoking ban reduces the number of people exposed. Studies have already looked at this effect, and found it to be large, but mostly with simple pre-/post- designs that don’t consider important confounding factors or prevailing trends. This study tests the hypothesis in a quasi-experimental setting, taking advantage of the fact that the 26 Swiss cantons implemented smoking bans at different times between 2007 and 2010. The authors analyse individual-level data from Swiss hospitals, estimating the impact of the smoking ban on AMI incidence, with area and time fixed effects, area-specific time trends, and unemployment. The findings show a large and robust effect of the smoking ban(s) for men, with a reduction in AMI incidence of about 11%. For women, the effect is weaker, with an average reduction of around 2%. The evidence also shows that men in low-education regions experienced the greatest benefit. What makes this an especially nice paper is that the authors bring in other data sources to help explain their findings. Panel survey data are used to demonstrate that non-smokers are likely to be the group benefitting most from smoking bans and that people working in public places and people with less education are most exposed to environmental tobacco smoke. These findings might not be generalisable to other settings. Other countries implemented more gradual policy changes and Switzerland had a particularly high baseline smoking rate. But the findings suggest that smoking bans are associated with population health benefits (and the associated cost savings) and could also help tackle health inequalities.

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Chris Sampson’s journal round-up for 14th 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.

A practical guide to conducting a systematic review and meta-analysis of health state utility values. PharmacoEconomics [PubMed] Published 10th May 2018

I love articles that outline the practical application of a particular method to solve a particular problem, especially when the article shares analysis code that can be copied and adapted. This paper does just that for the case of synthesising health state utility values. Decision modellers use utility values as parameters. Most of the time these are drawn from a single source which almost certainly introduces some kind of bias to the resulting cost-effectiveness estimates. So it’s better to combine all of the relevant available information. But that’s easier said than done, as numerous researchers (myself included) have discovered. This paper outlines the various approaches and some of the merits and limitations of each. There are some standard stages, for which advice is provided, relating to the identification, selection, and extraction of data. Those are by no means simple tasks, but the really tricky bit comes when you try and pool the utility values that you’ve found. The authors outline three strategies: i) fixed effect meta-analysis, ii) random effects meta-analysis, and iii) mixed effects meta-regression. Each is illustrated with a hypothetical example, with Stata and R commands provided. Broadly speaking, the authors favour mixed effects meta-regression because of its ability to identify the extent of similarity between sources and to help explain heterogeneity. The authors insist that comparability between sources is a precondition for pooling. But the thing about health state utility values is that they are – almost by definition – never comparable. Different population? Not comparable. Different treatment pathway? No chance. Different utility measure? Ha! They may or may not appear to be similar statistically, but that’s totally irrelevant. What matters is whether the decision-maker ‘believes’ the values. If they believe them then they should be included and pooled. If decision-makers have reason to believe one source more or less than another then this should be accounted for in the weighting. If they don’t believe them at all then they should be excluded. Comparability is framed as a statistical question, when in reality it is a conceptual one. For now, researchers will have to tackle that themselves. This paper doesn’t solve all of the problems around meta-analysis of health state utility values, but it does a good job of outlining methodological developments to date and provides recommendations in accordance with them.

Unemployment, unemployment duration, and health: selection or causation? The European Journal of Health Economics [PubMed] Published 3rd May 2018

One of the major socioeconomic correlates of poor health is unemployment. It appears not to be very good for you. But there’s an obvious challenge here – does unemployment cause ill-health, or are unhealthy people just more likely to be unemployed? Both, probably, but that answer doesn’t make for clear policy solutions. This paper – following a large body of literature – attempts to explain what’s going on. Its novelty comes in the way the author considers timing and distinguishes between mental and physical health. The basis for the analysis is that selection into unemployment by the unhealthy ought to imply time-constant effects of unemployment on health. On the other hand, the negative effect of unemployment on health ought to grow over time. Using seven waves of data from the German Socio-economic Panel, a sample of 17,000 people (chopped from 48,000) is analysed, of which around 3,000 experienced unemployment. The basis for measuring mental and physical health is summary scores from the SF-12. A fixed-effects model is constructed based on the dependence of health on the duration and timing of unemployment, rather than just the occurrence of unemployment per se. The author finds a cumulative effect of unemployment on physical ill-health over time, implying causation. This is particularly pronounced for people unemployed in later life, and there was essentially no impact on physical health for younger people. The longer people spent unemployed, the more their health deteriorated. This was accompanied by a strong long-term selection effect of less physically healthy people being more likely to become unemployed. In contrast, for mental health, the findings suggest a short-term selection effect of people who experience a decline in mental health being more likely to become unemployed. But then, following unemployment, mental health declines further, so the balance of selection and causation effects is less clear. In contrast to physical health, people’s mental health is more badly affected by unemployment at younger ages. By no means does this study prove the balance between selection and causality. It can’t account for people’s anticipation of unemployment or future ill-health. But it does provide inspiration for better-targeted policies to limit the impact of unemployment on health.

Different domains – different time preferences? Social Science & Medicine [PubMed] Published 30th April 2018

Economists are often criticised by non-economists. Usually, the criticisms are unfounded, but one of the ways in which I think some (micro)economists can have tunnel vision is in thinking that preferences elicited with respect to money exhibit the same characteristics as preferences about things other than money. My instinct tells me that – for most people – that isn’t true. This study looks at one of those characteristics of preferences – namely, time preferences. Unfortunately for me, it suggests that my instincts aren’t correct. The authors outline a quasi-hyperbolic discounting model, incorporating both short-term present bias and long-term impatience, to explain gym members’ time preferences in the health and monetary domains. A survey was conducted with members of a chain of fitness centres in Denmark, of which 1,687 responded. Half were allocated to money-related questions and half to health-related questions. Respondents were asked to match an amount of future gains with an amount of immediate gains to provide a point of indifference. Health problems were formulated as back pain, with an EQ-5D-3L level 2 for usual activities and a level 2 for pain or discomfort. The findings were that estimates for discount rates and present bias in the two domains are different, but not by very much. On average, discount rates are slightly higher in the health domain – a finding driven by female respondents and people with more education. Present bias is the same – on average – in each domain, though retired people are more present biased for health. The authors conclude by focussing on the similarity between health and monetary time preferences, suggesting that time preferences in the monetary domain can safely be applied in the health domain. But I’d still be wary of this. For starters, one would expect a group of gym members – who have all decided to join the gym – to be relatively homogenous in their time preferences. Findings are similar on average, and there are only small differences in subgroups, but when it comes to health care (even public health) we’re never dealing with average people. Targeted interventions are increasingly needed, which means that differential discount rates in the health domain – of the kind identified in this study – should be brought into focus.

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