David Mott’s journal round-up for 16th September 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.

Opening the ‘black box’: an overview of methods to investigate the decision‑making process in choice‑based surveys. The Patient [PubMed] Published 5th September 2019

Choice-based surveys using methods such as discrete choice experiments (DCEs) and best-worst scaling (BWS) exercises are increasingly being used in health to understand people’s preferences. A lot of time and energy is spent on analysing the data that come out from these surveys but increasingly there is an interest in better understanding respondents’ decision-making processes. Whilst many will be aware of ‘think aloud’ interviews (often used for piloting), other methods may be less familiar as they’re not applied frequently in health. That’s where this fascinating paper by Dan Rigby and colleagues comes in. It provides an overview of five different methods of what they call ‘pre-choice process analysis’ of decision-making, describing the application, state of knowledge, and future research opportunities.

Eye-tracking has been used in health recently. It’s intuitive and provides an insight into where the participants’ focus is (or isn’t). The authors explained that one of the ways it has been used is to explore attribute non-attendance (ANA), which essentially occurs when people are ignoring attributes either because they’re irrelevant to them, or simply because it makes the task easier. However, surprisingly, it has been suggested that ‘visual ANA’ (not looking at the attribute) doesn’t always align with ‘stated ANA’ (participants stating that they ignored the attribute) – which raises some interesting questions!

However, the real highlight for me was the overview of the use of brain imaging techniques to explore choices being made in DCEs. One study highlighted by the authors – which was a DCE about eggs and is now at least #2 on my list of the bizarre preference study topics after this oddly specific one on Iberian ham – predicted choices from an initial ‘passive viewing’ using functional magnetic resonance imaging (fMRI). They found that incorporating changes in blood flow (prompted by changes in attribute levels during ‘passive viewing’) into a random utility model accounted for a lot of the variation in willingness to pay for eggs – pretty amazing stuff.

Whilst I’ve highlighted the more unusual methods here, after reading this overview I have to admit that I’m an even bigger advocate for the ‘think aloud’ technique now. Although it may have some limitations, the amount of insight offered combined with its practicality is hard to beat. Though maybe I’m biased because I know that I won’t get my hands on any eye-tracking or brain imaging devices any time soon. In any case, I highly recommend that any researchers conducting preference studies give this paper a read as it’s really well written and will surely be of interest.

Disentangling public preferences for health gains at end-of-life: further evidence of no support of an end-of-life premium. Social Science & Medicine [PubMed] Published 21st June 2019

The end of life (EOL) policy introduced by NICE in 2009 [PDF] has proven controversial. The policy allows treatments that are not cost-effective within the usual range to be considered for approval, provided that certain criteria are met. Specifically, that the treatment targets patients with a short life expectancy (≤24 months), offers a life extension (of ≥3 months) and is for a ‘small patient population’. One of the biggest issues with this policy is that it is unclear whether the general population actually supports the idea of valuing health gains (specifically life extension) at EOL more than other health gains.

Numerous academic studies, usually involving some form of stated preference exercise, have been conducted to test whether the public might support this EOL premium. A recent review by Koonal Shah and colleagues summarised the existing published studies (up to October 2017), highlighting that evidence is extremely mixed. This recently published Danish study, by Lise Desireé Hansen and Trine Kjær, adds to this literature. The authors conducted an incredibly thorough stated preference exercise to test whether quality of life (QOL) gains and life extension (LE) at EOL are valued differently from other similarly sized health gains. Not only that, but the study also explored the effect of perspective on results (social vs individual), the effect of age (18-35 vs. 65+), and impact of initial severity (25% vs. 40% initial QOL) on results.

Overall, they did not find evidence of support for an EOL premium for QOL gains or for LEs (regardless of perspective) but their results do suggest that QOL gains are preferred over LE. In some scenarios, there was slightly more support for EOL in the social perspective variant, relative to the individual perspective – which seems quite intuitive. Both age and initial severity had an impact on results, with respondents preferring to treat the young and those with worse QOL at baseline. One of the most interesting results for me was within their subgroup analyses, which suggested that women and those with a relation to a terminally ill patient had a significantly positive preference for EOL – but only in the social perspective scenarios.

This is a really well-designed study, which covers a lot of different concepts. This probably doesn’t end the debate on NICE’s use of the EOL criteria – not least because the study wasn’t conducted in England and Wales – but it contributes a lot. I’d consider it a must-read for anyone interested in this area.

How should we capture health state utility in dementia? Comparisons of DEMQOL-Proxy-U and of self- and proxy-completed EQ-5D-5L. Value in Health Published 26th August 2019

Capturing quality of life (QOL) in dementia and obtaining health state utilities is incredibly challenging; which is something that I’ve started to really appreciate recently upon getting involved in a EuroQol-funded ‘bolt-ons’ project. The EQ-5D is not always able to detect meaningful changes in cognitive function and condition-specific preference-based measures (PBMs), such as the DEMQOL, may be preferred as a result. However, this isn’t the only challenge because in many cases patients are not in a position to complete the surveys themselves. This means that proxy-reporting is often required, which could be done by either a professional (formal) carer, or a friend or family member (informal carer). Researchers that want to use a PBM in this population therefore have a lot to consider.

This paper compares the performance of the EQ-5D-5L and the DEMQOL-Proxy when completed by care home residents (EQ-5D-5L only), formal carers and informal carers. The impressive dataset that the authors use contains 1,004 care home residents, across up to three waves, and includes a battery of different cognitive and QOL measures. The overall objective was to compare the performance of the EQ-5D-5L and DEMQOL-Proxy, across the three respondent groups, based on 1) construct validity, 2) criterion validity, and 3) responsiveness.

The authors found that self-reported EQ-5D-5L scores were larger and less responsive to changes in the cognitive measures, but better at capturing residents’ self-reported QOL (based on a non-PBM) relative to proxy-reported scores. It is unclear whether this is a case of adaptation as seen in many other patient groups, or if the residents’ cognitive impairments prevent them from reliably assessing their current status. The proxy-reported EQ-5D-5L scores were generally more responsive to changes in the cognitive measures relative to the DEMQOL-Proxy (irrespective of which type of proxy), which the authors note is probably due to the fact that the DEMQOL-Proxy focuses more on the emotional impact of dementia rather than functional impairment.

Overall, this is a really interesting paper, which highlights the challenges well and illustrates that there is value in collecting these data from both patients and proxies. In terms of the PBM comparison, whilst the authors do not explicitly state it, it does seem that the EQ-5D-5L may have a slight upper hand due to its responsiveness, as well as for pragmatic reasons (the DEMQOL-Proxy has >30 questions). Perhaps a cognition ‘bolt-on’ to the EQ-5D-5L might help to improve the situation in future?

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Journal Club Briefing: Dolan and Kahneman (2008)

Today’s Journal Club Briefing comes from the Academic Unit of Health Economics at the University of Leeds. At their journal club on 2nd August 2017, they discussed Dolan and Kahneman’s 2008 article from The Economic Journal: ‘Interpretations of utility and their implications for the valuation of health‘. If you’ve discussed an article at a recent journal club meeting at your own institution and would like to write a briefing for the blog, get in touch.

Why this paper?

Dolan and Kahneman (2008) is a paper which was published nearly ten years ago, was written several years before that, and was not published in a health-related journal. It’s hence, at first sight, a slightly curious choice for a health economics journal club. However, it raises issues which are at the heart of health economics practice. The questions raised by this article have not as yet been answered, and don’t look likely to be answered anytime soon.

Summary

Experienced vs. decision utility

The article’s point of departure is the distinction between experienced utility and decision utility, often a source of fruitful research in behavioural economics. Experienced utility is utility in the Benthamite sense, meaning the hedonic experience in the current moment: the pleasure and/or pain felt by a person at any given point in time. Decision utility is utility as taught in undergraduate economics textbooks: an objective function which the individual dispassionately acts to maximise. In the neoclassical framework of said undergraduate textbooks, this is a distinction without a difference. The individual correctly forecasts the expected flow of experienced utility given the available information and her actions, forms a decision utility function from it and acts to maximise it.

However, Thaler and Sunstein wouldn’t have sold as many books if things were so simple. Many systematic and significant instances of divergences between experienced and decision utility have been well documented, and several people (including one of the authors of this paper) have won Nobel prizes for it. The one which this article focuses on is adaptation.

Adaptation

The authors summarise a large body of evidence that shows that individuals suffer a large loss of utility after a traumatic event (e.g. the loss of a limb or loss of function), but that for many conditions they will adapt to their new situation and recover much of their utility loss. After as little as a year, their valuation of their health is very similar to that of the general population. Furthermore, the authors precis various studies which show that individuals routinely underestimate drastically the amount of adaptation that would occur should such a traumatic event befall them.

This improvement over time in the health-related utility experienced by people with many conditions is partly due to hedonic adaptation – the internal scale of pleasure/pain re-calibrates to their new situation – and partly due to behavioural change, such as finding new pastimes to replace those ruled out by their condition. While the causes of adaptation are fascinating, the focus here is not on the mechanisms behind it, but rather on the consequences for measuring utility and the implications for resource allocation.

Health valuation and adaptation

The methods health economists use to evaluate the utility of being in a given health state, such as time trade-off, standard gamble or discrete choice experiments, will tend to elicit decision utility. They are based on choices between hypothetical states and so will not capture the changes in experienced utility due to adaptation. Thus valuations of health states from the general public will tend to be lower than the valuations from people actually living in the health state.

At first glance, the consequences for resource allocation may not appear to be particularly severe. It may lead to more resources being devoted to healthcare as a whole (at least for life-improving treatments – life-extending treatments are a different case), but the overall healthcare budget is in practice largely a political decision. However, it will not lead to distortions between treatments for alternative conditions.

Yet adaptation is not a universal phenomenon. There are conditions for which little or no adaptation is seen (for example unexplained pain), and when it occurs, it occurs at different speeds and to differing extents for different conditions. The authors show that valuations of conditions with a greater initial utility loss are lower than conditions with a lesser initial loss but a lower degree of adaptation, and thus will receive a greater level of resources, despite the sum of experienced utility being the same for both. The authors argue that this is unfair, and that health economists should update their practices to better capture experienced utility.

Public vs. patient preference

A common argument in favour of the status quo is that (in many countries at least) it is public resources which are being allocated, and thus it is public preferences which should be respected. It appears legitimate to allocate resources to assuage public fears of health states, even if those health states are worse in their imagination than in reality. The authors consider this argument and reply that, in this case, the instruments of health economists are still not fit for purpose. General measures of health states, such as EQ-5D, go out of their way to describe states in abstract terms and to separate them from causes, such as cancer, which may carry an emotional affect. It cannot be argued that public valuations are justified because resources should be allocated according to public fears if the measurement of valuation deliberately tries not to elicit those fears.

The argument that adaptation causes serious problems for valuing health and for allocation of health resources is a persuasive one. It is undoubtedly true that changes in utility over time, and other violations of the neoclassical economic paradigm such as reference dependence, do not presently receive sufficient attention in health economics and policy decisions in general.

Discussion

Which yardstick?

Despite the stimulating discussion and the overall brilliance of the paper, there are some elements which can be challenged. One of them is that throughout, the authors’ arguments and recommendations are made from the standpoint that the sum over time of the flow of experienced utility from a health state is to be used as the sole measure of value. This would consist in what one of the authors calls the day reconstruction method (DRM) which consists in rating a range of feelings including happiness, worry, and frustration.

Despite the acknowledgement of some philosophical difficulties, the sum of the flow of experienced utility is treated as if it is the only true yardstick with which to measure health, without a convincing justification and no discussion on the qualitative aspect of the measurement as opposed to a truly cardinal measure of health allowing ranking of individuals’ health states.

Public vs. private preferences revisited

The authors raise the question of whether current practice can be justified by a desire to soothe public fears, and dismiss it since the elicitation tools are not suitable. However, they do not address the question of whether allocating public resources according to the public’s (incorrect) fears of given diseases or health states could be a legitimate health policy aim. One could imagine, for example, a discrete choice experiment eliciting how much the general public dreads cancer over other diseases, and make an argument that the welfare of the public is improved by allocating resources based on these results. There are myriad problems with such an approach, of course, but there seem to be no fewer problems with alternative approaches.

Intertemporal welfare

Intertemporal welfare judgements are notoriously difficult once the exponential discounting framework is left. It seems just as legitimate to base valuations on the ex post judgement of individuals who have fully adjusted to a health state as on an integration of past feelings, most of which are now distant memories. Most people would agree that the time to value their experience of a marathon is after completing it, not during the twenty-fifth mile or at the start line.

Indeed, this appears to be the position tacitly taken elsewhere by Kahneman in his work on the peak-end rule. In Redelmeier et al. (2003), it was found that the retrospective rating of the pain of a colonoscopy was based almost exclusively on the peak intensity of pain and on the pain felt at the end. Thus procedures which were extended by an extra three minutes were remembered as less painful than standard procedures, even though the total pain experienced was greater. Furthermore, those who underwent the extended procedure were more likely to state they would undergo it again. It would seem strange, in this case, to judge them as worse off.

Schelling (1984) ends his superlative discussion of the problems of intertemporal decision making with the following thought experiment. Just as with valuing health, there are no easy answers.

[S]ome anesthetics block transmission of the nervous impulses that constitute pain; others have the characteristic that the patient responds to the pain as if feeling it fully but has utterly no recollection afterwards. One of these is sodium pentothal. In my imaginary experiment we wish to distinguish the effects of the drug from the effects of the unremembered pain, and we want a healthy control subject in parallel with some painful operations that will be performed with the help of this drug. For a handsome fee you will be knocked out for an hour or two, allowed to sleep it off, then tested before you go home. You do this regularly, and one afternoon you walk into the lab a little early and find the experimenters viewing some videotape. On the screen is an experimental subject writhing, and though the audio is turned down the shrieks are unmistakably those of a person in pain. When the pain stops the victim pleads, “Don’t ever do that again. Please.”

The person is you.

Do you care?

Do you walk into your booth, lie on the couch, and hold out your arm for today’s injection?

Should I let you?

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Chris Sampson’s journal round-up for 8th May 2017

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.

Verification of decision-analytic models for health economic evaluations: an overview. PharmacoEconomics [PubMed] Published 29th April 2017

Increasingly, it’s expected that model-based economic evaluations can be validated and shown to be fit-for-purpose. However, up to now, discussions have focussed on scientific questions about conceptualisation and external validity, rather than technical questions, such as whether the model is programmed correctly and behaves as expected. This paper looks at how things are done in the software industry with a view to creating guidance for health economists. Given that Microsoft Excel remains one of the most popular software packages for modelling, there is a discussion of spreadsheet errors. These might be errors in logic, simple copy-paste type mistakes and errors of omission. A variety of tactics is discussed. In particular, the authors describe unit testing, whereby individual parts of the code are demonstrated to be correct. Unit testing frameworks do not exist for application to spreadsheets, so the authors recommend the creation of a ‘Tests’ spreadsheet with tests for parameter assignments, functions, equations and exploratory items. Independent review by another modeller is also recommended. Six recommendations are given for taking model verification forward: i) the use of open source models, ii) standardisation in model storage and communication (anyone for a registry?), iii) style guides for script, iv) agency and journal mandates, v) training and vi) creation of an ISPOR/SMDM task force. This is a worthwhile read for any modeller, with some neat tactics that you can build into your workflow.

How robust are value judgments of health inequality aversion? Testing for framing and cognitive effects. Medical Decision Making [PubMed] Published 25th April 2017

Evidence shows that people are often extremely averse to health inequality. Sometimes these super-egalitarian responses imply such extreme preferences that monotonicity is violated. The starting point for this study is the idea that these findings are probably influenced by framing effects and cognitive biases, and that they may therefore not constitute a reliable basis for policy making. The authors investigate 4 hypotheses that might indicate the presence of bias: i) realistic small health inequality reductions vs larger one, ii) population- vs individual-level descriptions, iii) concrete vs abstract intervention scenarios and iv) online vs face-to-face administration. Two samples were recruited: one with a face-to-face discussion (n=52) and the other online (n=83). The questionnaire introduced respondents to health inequality in England before asking 4 questions in the form of a choice experiment, with 20 paired choices. Responses are grouped according to non-egalitarianism, prioritarianism and strict egalitarianism. The main research question is whether or not the alternative strategies resulted in fewer strict egalitarian responses. Not much of an effect was found with regard to large gains or to population-level descriptions. There was evidence that the abstract scenarios resulted in a greater proportion of people giving strong egalitarian responses. And the face-to-face sample did seem to exhibit some social desirability bias, with more egalitarian responses. But the main take-home message from this study for me is that it is not easy to explain-away people’s extreme aversion to health inequality, which is heartening. Yet, as with all choice experiments, we see that the mode of administration – and cognitive effects induced by the question – can be very important.

Adaptation to health states: sick yet better off? Health Economics [PubMed] Published 20th April 2017

Should patients or the public value health states for the purpose of resource allocation? It’s a question that’s cropped up plenty of times on this blog. One of the trickier challenges is understanding and dealing with adaptation. This paper has a pretty straightforward purpose – to look for signs of adaptation in a longitudinal dataset. The authors’ approach is to see whether there is a positive relationship between the length of time a person has an illness and the likelihood of them reporting better health. I did pretty much the same thing (for SF-6D and satisfaction with life) in my MSc dissertation, and found little evidence of adaptation, so I’m keen to see where this goes! The study uses 4 waves of data from the British Cohort Study, looking at self-assessed health (on a 4-point scale) and self-reported chronic illness and health shocks. Latent self-assessed health is modelled using a dynamic ordered probit model. In short, there is evidence of adaptation. People who have had a long-standing illness for a greater duration are more likely to report a higher level of self-assessed health. An additional 10 years of illness is associated with an 8 percentage point increase in the likelihood of reporting ‘excellent’ health. The study is opaque about sample sizes, but I’d guess that finding is based on not-that-many people. Further analyses are conducted to show that adaptation seems to become important only after a relatively long duration (~20 years) and that better health before diagnosis may not influence adaptation. The authors also look at specific conditions, finding that some (e.g. diabetes, anxiety, back problems) are associated with adaptation, while others (e.g. depression, cancer, Crohn’s disease) are not. I have a bit of a problem with this study though, in that it’s framed as being relevant to health care resource allocation and health technology assessment. But I don’t think it is. Self-assessed health in the ‘how healthy are you’ sense is very far removed from the process by which health state utilities are obtained using the EQ-5D. And they probably don’t reflect adaptation in the same way.

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