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.

Credits

Chris Sampson’s journal round-up for 2nd July 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.

Choice in the presence of experts: the role of general practitioners in patients’ hospital choice. Journal of Health Economics [PubMed] [RePEc] Published 26th June 2018

In the UK, patients are in principle free to choose which hospital they use for elective procedures. However, as these choices operate through a GP referral, the extent to which the choice is ‘free’ is limited. The choice set is provided by the GP and thus there are two decision-makers. It’s a classic example of the principal-agent relationship. What’s best for the patient and what’s best for the local health care budget might not align. The focus of this study is on the applied importance of this dynamic and the idea that econometric studies that ignore it – by looking only at patient decision-making or only at GP decision-making – may give bias estimates. The author outlines a two-stage model for the choice process that takes place. Hospital characteristics can affect choices in three ways: i) by only influencing the choice set that the GP presents to the patient, e.g. hospital quality, ii) by only influencing the patient’s choice from the set, e.g. hospital amenities, and iii) by influencing both, e.g. waiting times. The study uses Hospital Episode Statistics for 30,000 hip replacements that took place in 2011/12, referred by 4,721 GPs to 168 hospitals, to examine revealed preferences. The choice set for each patient is not observed, so a key assumption is that all hospitals to which a GP made referrals in the period are included in the choice set presented to patients. The main findings are that both GPs and patients are influenced primarily by distance. GPs are influenced by hospital quality and the budget impact of referrals, while distance and waiting times explain patient choices. For patients, parking spaces seem to be more important than mortality ratios. The results support the notion that patients defer to GPs in assessing quality. In places, it’s difficult to follow what the author did and why they did it. But in essence, the author is looking for (and in most cases finding) reasons not to ignore GPs’ preselection of choice sets when conducting econometric analyses involving patient choice. Econometricians should take note. And policymakers should be asking whether freedom of choice is sensible when patients prioritise parking and when variable GP incentives could give rise to heterogeneous standards of care.

Using evidence from randomised controlled trials in economic models: what information is relevant and is there a minimum amount of sample data required to make decisions? PharmacoEconomics [PubMed] Published 20th June 2018

You’re probably aware of the classic ‘irrelevance of inference’ argument. Statistical significance is irrelevant in deciding whether or not to fund a health technology, because we ought to do whatever we expect to be best on average. This new paper argues the case for irrelevance in other domains, namely multiplicity (e.g. multiple testing) and sample size. With a primer on hypothesis testing, the author sets out the regulatory perspective. Multiplicity inflates the chance of a type I error, so regulators worry about it. That’s why triallists often obsess over primary outcomes (and avoiding multiplicity). But when we build decision models, we rely on all sorts of outcomes from all sorts of studies, and QALYs are never the primary outcome. So what does this mean for reimbursement decision-making? Reimbursement is based on expected net benefit as derived using decision models, which are Bayesian by definition. Within a Bayesian framework of probabilistic sensitivity analysis, data for relevant parameters should never be disregarded on the basis of the status of their collection in a trial, and it is up to the analyst to properly specify a model that properly accounts for the effects of multiplicity and other sources of uncertainty. The author outlines how this operates in three settings: i) estimating treatment effects for rare events, ii) the number of trials available for a meta-analysis, and iii) the estimation of population mean overall survival. It isn’t so much that multiplicity and sample size are irrelevant, as they could inform the analysis, but rather that no data is too weak for a Bayesian analyst.

Life satisfaction, QALYs, and the monetary value of health. Social Science & Medicine [PubMed] Published 18th June 2018

One of this blog’s first ever posts was on the subject of ‘the well-being valuation approach‘ but, to date, I don’t think we’ve ever covered a study in the round-up that uses this method. In essence, the method is about estimating trade-offs between (for example) income and some measure of subjective well-being, or some health condition, in order to estimate the income equivalence for that state. This study attempts to estimate the (Australian) dollar value of QALYs, as measured using the SF-6D. Thus, the study is a rival cousin to the Claxton-esque opportunity cost approach, and a rival sibling to stated preference ‘social value of a QALY’ approaches. The authors are trying to identify a threshold value on the basis of revealed preferences. The analysis is conducted using 14 waves of the Australian HILDA panel, with more than 200,000 person-year responses. A regression model estimates the impact on life satisfaction of income, SF-6D index scores, and the presence of long-term conditions. The authors adopt an instrumental variable approach to try and address the endogeneity of life satisfaction and income, using an indicator of ‘financial worsening’ to approximate an income shock. The estimated value of a QALY is found to be around A$42,000 (~£23,500) over a 2-year period. Over the long-term, it’s higher, at around A$67,000 (~£37,500), because individuals are found to discount money differently to health. The results also demonstrate that individuals are willing to pay around A$2,000 to avoid a long-term condition on top of the value of a QALY. The authors apply their approach to a few examples from the literature to demonstrate the implications of using well-being valuation in the economic evaluation of health care. As with all uses of experienced utility in the health domain, adaptation is a big concern. But a key advantage is that this approach can be easily applied to large sets of survey data, giving powerful results. However, I haven’t quite got my head around how meaningful the results are. SF-6D index values – as used in this study – are generated on the basis of stated preferences. So to what extent are we measuring revealed preferences? And if it’s some combination of stated and revealed preference, how should we interpret willingness to pay values?

Credits

 

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.

Credits