## Review: Happiness by Design (Paul Dolan)

Happiness by Design: Finding Pleasure and Purpose in Everyday Life

Hardcover, 256 pages, ISBN: 9780241003107, published 28 August 2014

Amazon / Google Books / Allen Lane

Many economists balk at the mention of happiness research. I consider myself a sceptic. But people care about attaining happiness, and governments care about measuring it. And why not? There are behaviours and outcomes that are difficult to explain without its careful consideration. Why might cancer patients report lower levels of life satisfaction when their disease is in remission? Understanding such quirks can help us to improve outcomes for patients and, as Paul Dolan argues in his new book, enhance our own lives on a day-to-day basis.

Paul Dolan’s career – as far as we’re concerned – has come in two waves. Until 2006 he was professor of health economics at the University of Sheffield, where his first wave came to an end. You’d be hard pressed to find a health economist unfamiliar with his work from this period, such as that on the EQ-5D. Since then, Dolan has embarked on a programme of work developing measures of happiness and using experiments to shed light on individual behaviour and ways of influencing it for good. Happiness by Design goes some way to summarise the second wave of Dolan’s career, and shares with the reader the potentially life-enhancing implications of the research.

Be happy

Dolan describes the route to happiness as analogous to a production function. Firstly, there are inputs from various stimuli, such as the TV, this blog post or your back pain. Secondly, the production process corresponds to the allocation of your attention to these stimuli. Finally, the output is your level of happiness. A key message of HbD is this: learn to allocate more attention to positive stimuli – and less to negative stimuli – and you will be happier.

In order to achieve this, Dolan proposes a nudge-like context-focused approach. We should design our surroundings such that our behaviour is automatically guided towards maximising our happiness. Clearly some cognitive effort is required to achieve this, but Dolan’s approach is designed to be minimal. Change your banking password to Sav£M0ney; stop taking your cigarettes to work; or put a recurring event in your calendar to Skype your best friend. All of these could improve your happiness by influencing your behaviour.

The arguments in HbD are compelling. Almost every claim is backed-up by research, with 30 pages of references for you to trawl through should you fancy it. At times this results in the book reading a little like a review of Dolan’s work to date, which might be alienating for lay readers but comfortably familiar for academics (or nerds more generally). I only have a rudimentary understanding of behavioural science, but I suspect I may have struggled with some concepts and terminology without it. Nevertheless, the book remains engaging throughout. We’re given examples from the author’s own life, where he or the people around him have (or haven’t) dealt with the challenges to happiness, making the ideas easier to grasp and the concepts more relatable.

When it comes to policy prescriptions, Dolan argues that we shouldn’t care so much about ratings of life satisfaction. These are too subject to biases, such as the current weather. Rather, we should estimate levels of happiness over time. Such data could be obtained using the day reconstruction method (DRM). Dolan hints at a QALY-esque, area-under-the-curve type quantification of happiness over time, without fully proposing such a thing. HbD also gives a very useful whistlestop tour of the cognitive biases that influence our behaviour and determine our happiness. As a health economist, with limited exposure to behavioural science, HbD gets you thinking about the implications of these for health.

I have never read a self-help book, but it seems to me that HbD serves well as one. Throughout, the book encourages interaction. There are thought experiments in which the reader can participate, and doing so will enhance the experience. As a consistently happy person, with a relatively sunny disposition, I found myself identifying with many of the traits that Dolan encourages us to adopt in the name of happiness. I listen to a lot of music, my phone does not receive Facebook notifications, and I prefer to spend money on experiences rather than products. But am I happy because I adopt these behaviours, or do I adopt these behaviours because I am happy? Unfortunately, HbD will do little to dispel your concerns about causality. Though the book is evidence-based throughout, few of the references convincingly demonstrate causal relationships between behaviour and happiness.

Like John Stuart Mill before him, Dolan advances a unidimensional approach. Happiness is all that matters. We may think that we wish to experience sentiments of achievement or authenticity, for example, but these are ‘mistaken desires’. However, unlike Mill, Dolan appears to allow one concession: purpose. On this, I am less convinced.

The PPP

Dolan proposes that individuals choose to behave in particular ways not only because of the pleasure associated with the choice, but also because of sentiments of ‘purpose’. The pleasure-purpose principle (PPP) states that people’s happiness is determined by sentiments of pleasure and purpose. If we only consider pleasure then we are missing something; though people may experience less pleasure at work, this may be counterbalanced by feelings of purpose.

Pleasure and purpose are subject to diminishing marginal returns and, so Dolan argues, many people would benefit from achieving a more balanced ratio. There is no evidence to support this, but it is nevertheless a compelling argument. We can all imagine behaving in ways that are pleasurable and ways that feel purposeful, and that there is often a trade-off between these. However, on closer inspection, I feel the PPP is of limited use. Take my writing of this review. I would probably consider this to be a purposeful activity. I am presently experiencing at best a modicum of pleasure; the opportunity cost being the pleasure I’d get from sitting and reading or listening to music. It feels somewhat purposeful, though, and that’s why I’m doing it. But it appears to me that we can dissect this sentiment of purpose and rationalise it to feelings of pleasure. For example:

• I gained pleasure from reading the book; a condition of which was me writing this review
• I expect to gain pleasure in the near future as I see this page receive hits and my ego is massaged
• I expect to gain pleasure in the more distant future as my better understanding of this book, and my exposure as a writer, improves my employability and the quality of my future writing.

Each of these sources of pleasure lends to increases in my happiness. So the question is, what is left for purpose? Purposeful health behaviours, such as going for a run, share similar implications for pleasure. It seems clear to me that sentiments of purpose are at least partly explained by anticipation of future pleasure, making the two ‘P’s difficult to separate. Even if sentiments of purpose are distinguishable from sentiments of pleasure, I do not see how we could avoid double-counting in policy or evaluative applications. I remain open to being convinced by the PPP, but unfortunately HbD falls short in this regard. A real proof-of-principle would lie in a behaviour which provides some sentiment of purpose but absolutely no pleasure. I cannot imagine such an activity existing, let alone anybody choosing to do it. Of course, there are situations where people may choose to behave in purposeful ways that ultimately do not provide pleasure, but this is surely more likely due to biases in people’s expectations. Another explanation could lie in the concern for equity and for the well-being of others which, as Dolan points out in HbD, affects our own happiness. Part of the reason I experience purposefulness in my work in health economics is that I expect it to be of some (small) benefit to others at some point in the future, and this thought gives me pleasure when I complete a piece of work. Dolan provides a table that ranks various professions by the percentage of people agreeing that they are happy. Interestingly, the jobs that one might consider most ‘purposeful’ in this respect – e.g. nurses and teachers – occupy the middle ground.

It seems more likely to me that the PPP may actually be a reflection of our desire to smooth the pleasure we experience over time. Behaviours that maximise our pleasure in the present – skiving off work to watch TV and eat doughnuts, say – may have negative implications for future pleasure. As such, we invest some of our time in activities with the purpose of increasing future pleasure.

Last word

HbD provides many useful tools for improving your happiness; even for someone already satisfied with their life. I intend to use some in my day-to-day life. I believe this to have been Dolan’s primary purpose in writing the book, in which case – as far as this reader is concerned – HbD is a great success.

Posted by on August 28, 2014 in Health and its Value, Reviews

## Bean counting and the NHS

I was recently questioned about the future of the NHS, during a live debate on the BBC Radio 4 programme Moral Maze, on 9 July 2014. One of the panelists took me to task for being a “bean counter”. I got side-tracked by this somewhat less-than-flattering characterisation of my professional role as a health economist, and so only managed to get across three of the six points I had planned to make. For what they are worth, this blog sets out all six points. And, as an added bonus, it then concludes by explaining why I am proud to be a ‘bean counter’.

This blog sets out personal ethical views on a number of controversial matters of social value judgement. That is what the BBC programme makers asked me to do, and I hope my professional colleagues will not ‘tut tut’ too loudly when they see me doing it. Professional economists are supposed to help decision makers and stakeholders think through the implications of a range of alternative value judgements, rather than to impose their own particular personal or professional value judgements. However, this blog post merely voices my own value judgements – it does not impose them on anyone.

Point number one is that the NHS performs rather well compared with other health systems across the world. It is relatively cheap, relatively good, and very fair. The UK currently spends about 9% of national income on health care, just under the OECD average, compared with 18% in the US. People in the UK are on average healthier than those in the US – even rich people with access to the best available health care in the US. And the UK regularly comes top of Commonwealth Fund surveys of fairness in high income health systems. The UK NHS is widely regarded as the fairest health system in the world, with the possible exception of Cuba.

Point number two is that financial strain on the NHS will get worse in decades to come – potentially much worse. This is due to a fundamental clash between health economics and tax politics. The tax politics is obvious. Voters do not like high taxes, so there is a limit to how far taxes can be raised, even to pay for something as popular as health care. The health economics is less obvious, but surprisingly simple when you think about it. As countries get richer, they spend a higher percentage of national income on health care. There is a simple reason for this. As we get richer, which is more valuable – a third car, yet more electronic gadgets, or an extra year of life? (I am here paraphrasing Hall and Jones, who predicted that health spending in the US will rise to 30% of national income by 2050). In the technical economic jargon, health care is a ‘superior’ or ‘luxury’ good. Do not be misled by this jargon – it does not mean that health care is an unimportant frippery. Quite the opposite. Effective health care that extends life and improves quality of life is much more important than fripperies. That is why rich people want to spend such a large share of their incomes on it.

Point three is that my own preferred solution to this problem – and here you will notice that personal ethical opinions are coming thick and fast – is gradually to ration NHS care more explicitly and extensively, within whatever budget the electorate are willing to vote for. That would enable the preservation of a tax-funded national health service that continues to provide a fairly comprehensive package of cost-effective health services to all citizens, that is nearly free at the point of delivery. (The NHS has never been 100% comprehensive or 100% free at the point of delivery). The rationing should be done through a transparent deliberative process, and based on a range of ethical principles, including cost-effectiveness, need, and compassion. Chief among these principles, however, should be cost-effectiveness – the principle that scarce NHS resources should be used to do as much good as possible in terms of extending people’s lives and improving their quality of life.

Point four is that more extensive rationing is a better and fairer solution to the problem of preserving the NHS than more extensive user charges. User charges should not be imposed on cost-effective forms of health care, such as GP visits. Charges for GP visits deter people – especially poorer people – from seeking preventive and diagnostic care. Without effective prevention and diagnosis, health problems progress to become more harmful to the patient and more costly to the NHS. If health care is cost-effective it should be provided free on the NHS; and otherwise not. People can then pay for non-cost-effective care themselves, either out of pocket or via ‘top up’ private health insurance. The slogan “all necessary care should be free” should be re-interpreted as the slogan “all cost-effective care should be free”.

Point five is that fervent ideological debates about ‘competition’ and ‘choice’ and ‘markets’ and ‘privatisation’ are largely red herrings. What matters is that the NHS provides a fairly comprehensive range of cost-effective care to all citizens, so that everyone receives the care they need at a cost they can afford. Who owns or manages health care provider organisations does not matter directly in and of itself. Ownership and management may matter indirectly, of course – but only insofar as they impact upon the cost, quality and social distribution of health care. The direction and size of such impacts in different contexts is a factual matter, to be settled in the court of evidence and experience, rather than a matter for fervent ideological debate.

Point six is that a more extensively rationed NHS can still preserve the founding principles of the NHS. On the delivery side, it can preserve the principle of ‘equality of access’ to all necessary health care – where ‘necessary’ means ‘cost-effective’. And on the financing side, continued tax funding continues to preserves the principle of ‘solidarity’, that the strong should help the weak – the rich should help the poor, the young should help the old, and the healthy should help the sick. Finally, the NHS also preserves the benefit of financial risk protection. As was stated in the public information leaflet sent to all UK citizens at the founding of the NHS in 1948, one of the main benefits of the NHS is that “it will spare your family from money worries in time of ill health”.

In conclusion, the best way to preserve the NHS is to engage in more explicit and extensive rationing. This in turn will require more of what my Moral Maze inquisitor called “bean counting”. More evidence will be needed to inform a suitably transparent and deliberative rationing process. In particular, more evidence will be needed about the impacts of different NHS services on cost, length and quality of life, patient experience, need, compassion and dignity, and other ethically important outcomes and processes. This form of ‘bean counting’ is not an ignoble exercise. The ‘beans’ in question here are people’s lives. People’s lives matter, and if seeking to improve the length and quality of people’s lives makes me a “bean counter” then I am proud to be one.

## Bayesian evidence synthesis and bootstrapping for trial-based economic evaluations: comfortable bed fellows?

By Mohsen Sadatsafavi and Stirling Bryan

In economic evaluation of health technologies, evidence synthesis is typically about quantification of the evidence in terms of parameters. Bootstrapping is a non-parametric inferential method in trial-based economic evaluations. On the surface the two paradigms seem incompatible. In a recent paper, we show that a simple and intuitive modification of the bootstrap can indeed accommodate parametric evidence synthesis.

When the recruitment phase of a pragmatic randomized controlled trial (RCT) is over, two groups of investigators will become busy. The clinical evaluation team is interested in inference about the population value of the primary outcome, typically a measure of relative effect (e.g. relative risk [RR] of the clinical outcome of interest) between the treatment groups. The economic evaluation team is in charge of inference chiefly on the population value of the incremental cost effectiveness ratio (ICER).

A widely used method of characterizing uncertainty around the ICER in RCT-based cost-effectiveness analyses is the bootstrap. For a typical two-arm RCT, the investigator obtains a bootstrap sample of the data to calculate the difference in costs and difference in effectiveness between the two treatments. Repeating this step many times provides a sample from the joint distribution of the difference in costs and effectiveness that can be used to calculate the ICER and to represent uncertainty around its value (such as to calculate credible intervals, to draw the cost-effectiveness plane and acceptability curve). As an example, the table below gives results from repeated bootstrap samples of a hypothetical two-arm RCT:

 Bootstrap # Difference in costs ($) Difference in effectiveness (QALYs) 1$1,670.1 0.0130 2 $1592.9 0.0143 … 10,000$1,091.0 0.0133 Average $1,450.2 0.0151 ICER 1,450.2/0.0151=96,039.7 In deriving the costs and effectiveness values within each bootstrap loop, many steps might be involved, such as imputation of missing values and adjusting for covariates. This is what makes the bootstrap method so powerful, as all such steps are enveloped within the bootstrap, allowing for the uncertainty in all inferential steps to be accounted for. The dilemma of external evidence Imagine at the time of such analyses, another ‘external’ trial is published which reports results for the same interventions and treatment protocol, in the same population, with the same clinical outcome measure. Also imagine the external RCT reports the maximum-likelihood and 95% confidence interval of the RR of treatment, which we find to be more favorable for the new treatment versus the standard treatment than the RR in the current RCT. Of course, this carries some information about the effect of the treatment at the population level. But how can this be incorporated in the inference? The task in front of the clinical evaluation team is rather straightforward: the RR from the two RCTs can be combined using meta-analytic techniques to provide an estimate for the population RR. But what about the economic evaluation team? We can speculate that, given the observed treatment effect in the external RCT, the population value of the ICER could be more favorable for the new treatment than what the current RCT suggests. But is there any way to make the above-mentioned subjective line of reasoning into a formal and objective form of inference? This is what we have addressed in our recent paper. Before we explain our solution, we note that there are already at least two ways of performing this task: (a) to desist statistical inference and use decision-analytic modeling (which can use the pooled RR as an input parameter), and (b) to resort to parametric Bayesian inference. The former is not really a solution as long as the desire for statistical inference for cost-effectiveness is concerned, and the latter is a complete paradigm shift which also imposes a myriad of parametric assumptions (think of the regression equations, error terms, and link functions required to connect cost and effectiveness outcomes to the clinical variable, and the clinical variable to external evidence). Can evidence synthesis be carried out using the bootstrap? Yes! And our proposed solution is rather intuitive: the investigator first parameterises the external evidence using appropriate probability distributions (e.g. a log-normal distribution for RR constructed from the reported point estimate and interval bounds). For each bootstrap sample, the investigator calculates, in addition to cost and effectiveness outcomes, the parameters for which external evidence is available, and uses the constructed probability distribution to weight the bootstrap sample according to its degree of plausibility against external evidence. The ICER is the weighted-average of difference in costs over the weighted-average of difference in effectiveness:  Bootstrap # Difference in costs ($) Difference in effectiveness (QALYs) Treatment effect (RR) Weight according to  external evidence 1 $1,670.1 0.0130 0.521 0.058 2$1592.9 0.0143 0.650 0.068 … 10,000 $1,091.0 0.0151 0.452 0.025 Weighted Average$1,034.2 0.0161 ICER 1,034.2/0.0161=64,236.0

A more practical method of assigning weights to bootstraps, instead of using the weights directly, is to ‘accept’ each bootstrap with a probability that is proportional to its weight. Rejected bootstraps are removed from the analysis. This gives the investigator an idea about the ‘effective’ number of bootstraps, and makes the subsequent calculations independent of the weights.

Why does it work?

The theory is provided in the paper, but in a nutshell, a Bayesian interpretation of the bootstrap allows one to see the bootstrap estimate of the difference in costs and difference in effectiveness as their posterior distribution conditional on the current RCT. It can be shown that the weights transform this to the posterior distribution conditional on the current AND external RCT.

An appealing feature of the method is the minimal parametric assumptions. Unlike the parametric Bayesian methods, the investigator need not make any assumption on the distribution of costs and effectiveness outcomes and how the clinical outcome affects the cost and effectiveness values. The effect is channeled directly through the experience of patients in the course of the trial, represented through the correlation structure between clinical outcomes, costs, and effectiveness variables at the individual level.

Further developments

There are indeed many gaps to be filled. The method only focuses on parallel-arm RCTs and leaves the problem open for other designs. In addition, rejection sampling can be wasteful, and if there are several parameters, then the method becomes quite unwieldy. An interesting potential solution is to create auto-correlated Markov Chain bootstraps that tend to concentrate on the high probability areas of the posterior distribution. In general, this sampling paradigm is quite flexible and can be used to incorporate external evidence in other contexts such as model-based evaluations or evaluations based on observational data.

## A taxonomy of behavioural interventions

Back in March I made a note to myself to write a paper – or, more likely, a blog post – presenting a taxonomy of behavioural interventions. I had gotten tired of everything being called a ‘nudge’ and with debates about whether nudges are ethical. I even bought a copy of Nudge so that I could use it to populate the taxonomy with examples.

Thankfully, someone else was already working on this and has beaten me to it – producing almost exactly what I had in mind. Mira Fischer from the University of Cologne and Sebastian Lotz from Stanford have written a working paper titled ‘Is soft paternalism ethically legitimate? – the relevance of psychological processes for the assessment of nudge-based policies‘. They differentiate between 4 types of behavioural intervention – or ‘nudge’ – and discuss the ethical implications associated with each by considering the psychological processes at play. It’s far better than any blog post I could have written, and I recommend reading it.

Fischer and Lotz’s taxonomy

Consider a utility-maximising individual with 2 choices (A or B), each with 2 possible outcomes (1 and 2), such that the utility associated with choice A would be $U_{A} = \pi_{A1}(u_{A1M}+u_{A1N})+\pi_{A2}(u_{A2M}+u_{A2N})$, where ‘π’ is the probability and ‘u’ the utility of the outcome and the ‘M’ and ‘N’ refers to monetary and non-monetary utility. Based on this, the authors then discuss the ways in which various types of nudge might influence the individual’s choice. The table below is not from the paper and is my interpretation of the taxonomy.

 Type Name Point of influence Means of impact on expected utility of choice Examples 1 ‘discomfort nudge’ choice evaluation non-monetary utility default settings on electronic devices; communication of social norms 2 ‘probability nudge’ choice evaluation subjective probability of realisation informational campaigns 3 ‘indifference nudge’ preference formation monetary or non-monetary utility positioning of healthy/unhealthy products 4 ‘automatism nudges’ ? ? changes in road markings

Is the taxonomy complete and well-defined?

In my opinion, it is not.

I do not believe that Type 4 nudges exist in the way described. The authors use the example of changing road markings to make drivers think they are travelling faster than they actually are and thus reduce their speed. It seems clear to me that this is an example of Type 2; the driver has been made to believe that the probability of them crashing at their current speed is greater than they would otherwise have believed. The idea that there is an ethical difference between nudges to our ‘automatic’ behaviour and nudges to our considered behaviour – given that so much of our behaviour is automatic – I believe is unfounded.

When I was considering writing my own taxonomy of behavioural interventions, I was approaching it from a decision analysis perspective. Simply imagining the structure of an individual’s decision process and considering the different points at which an individual could be influenced. Based on the Thaler/Sunstein definition, a nudge can affect any part of a person’s decision process.

Based on this I believe there are 3 points of influence: i) before an individual’s preferences are defined ii) after the definition of preferences but before the observation of the choice set and iii) once the choice set has been recognised. Once preferences are defined and the choice set has been recognised there are 2 means of influencing choice; utility or probability.

As such, I think the taxonomy should look like this:

 Type Point of influence Means of impact on expected utility of choice Examples A preference formation values/priorities education; positioning of food B choice set observation choice set expansion/compression positioning of food; introduction of cycle lanes C choice evaluation subjective probability informational campaigns D choice evaluation utility defaults; communication of social norms

As the authors outline in their paper, particular nudges will cross type boundaries. I have included the ‘positioning of food’ nudge under 2 types to highlight this. If positioning causes an individual to choose a healthy item – where they otherwise would have chosen a less healthy one – this could either be because they saw the healthy item first or because they simply didn’t see and fully consider the unhealthy option. In the former case Type A is at work, while in the latter case Type B is at work. I believe that educational interventions could fall into any of the above types because they can improve an individual’s ability to satisfy their own preferences. Type D could, of course, include a tax or a subsidy.

Furthermore, the ethical implications may be different depending on whether the impact on types B, C or D is positive or negative, and also whether the impact on utility is monetary or non-monetary, which would increase the total number of types to 9.

I don’t know whether I, the authors or both of us are right, but there’s one thing we can agree on. One nudge isn’t necessarily as ethical as the next, so we need better ways of defining behavioural interventions.

Posted by on May 30, 2014 in Public Health

## Recommended reading for Steven Levitt

Steven Levitt and his trusty Freakonomics sidekick were invited by David Cameron to advise on health policy, as they recount in their latest book. Apparently the PM walked out on them. Levitt has kindly given us some more details on his proposal for British healthcare. It consisted of the following:

• Every British resident is given £1000 per year
• People must pay out of pocket up to £2000
• For annual expenditure between £2000 and £8000, the co-payment is 50%
• The government pays for everything in excess of £8000 in a year

Various people have already commented, so go to them for some insights. Here I just want to help Steve by offering him some suggestions for weekend reading. He makes a number of claims in his blog post that make him look like a novice; most notably in comparing ill health to a broken TV. Hopefully, by doing the reading that I suggest here, he can avoid having smart people walk out on him in future.

“…it doesn’t take a whole lot of smarts or a whole lot of blind faith in markets to recognize that when you don’t charge people for things (including health care), they will consume too much of it”

So Steve’s proposal is framed as a solution to moral hazard. If you think this is a problem for the NHS, you’ll have to show me the evidence. We already have mechanisms in place to deal with it – some people pay for prescriptions, doctors don’t receive fee-for-service – but Steve’s proposal wouldn’t prevent it anyway. Bizarrely he appears to have forgotten about private insurance, under which moral hazard could very much exist. Furthermore, under his system, insurers would only have to pay up to a maximum of £5000. Even if individuals did consume as much healthcare as humanly possible their premiums would not need to go up. There would be no adverse selection. Moral hazard could persist and the government would shoulder the cost.

“Consumers will cut out the low-value healthcare services they are currently using only because the services come for free”

They won’t. Co-payments stop people using care that they actually need because we aren’t able to distinguish between low value and high value healthcare.

“If it turns out that consumers are sensitive to prices (i.e., that the most basic principle of economics holds, and demand curves slope downwards), total spending on health care will decrease”

How did prices come into this!? Steve’s tone suggests he hasn’t considered the possibility that this ‘most basic principle’ doesn’t hold, but never mind. I assume Steve knows about the RAND health insurance experiment and its famous -0.2 figure for the price elasticity of demand. Anyway, his claim simply doesn’t follow. Capping expenditures in this way can cause poor adherence resulting in greater healthcare costs through hospitalisation.

“Competition will likely lead to increased efficiency”

This claim, which it appears is built into his team’s simulations of the likely outcomes of the proposal, is a biggy. Based on the evidence we certainly cannot say it is ‘likely’. It’s clear that Steve hasn’t fully considered the evidence in regard to the NHS, which is far from being clear-cut. Steve is vague, but I assume here that he means competition in the provision of healthcare, and that the NHS would cease supplying care. Or maybe he just didn’t realise how the NHS works. Given his previous comment, it’s clear he means competition on price. Even the most pro-competition academic health economists in the UK don’t support price competition because the evidence suggests it negatively affects quality.

“The majority of Brits will be better off in the scenario I laid out”

Here Steve appears to completely miss the purpose of the NHS. It’s hard to imagine the majority of Brits being ‘better off’ following the dismantling of the NHS – not least because we think it’s great. However, given what we know, Steve’s system will necessarily result in an increase in health inequality because the poorer are sicker. This increase in health inequality would result in welfare losses because the (British) public really value equality in health, even more than doctors.

What Steve doesn’t seem to realise – or, probably, he does – is that his system is akin to just making the tax system less progressive and at the same time rationing less expensive healthcare based on ability to pay. It isn’t clear to me how any good can come from this. So here’s some weekend reading for Steve, which might help him refine his ‘model’. I’ve limited the list to just 10 items relating to the points above (to give Steve a chance), so feel free to suggest any others in the comments below.

1. Adams, AS et al (2001) The case for a Medicare drug coverage benefit: a critical review of the empirical evidence. Annual Review of Public Health 22: 49-61
2. Aron-Dine, A et al (2013) The RAND Health Insurance Experiment, three decades later. Journal of Economic Perspectives 27(1):197-222
3. Arrow, K (1963) Uncertainty and the welfare economics of medical care. American Economic Review 53(5): 941-973
4. Culyer, AJ & Wagstaff, A (1993) Equity and equality in health and health care. Journal of Health Economics 12(4): 431-457
5. Dolan, P et al (2005) QALY maximisation and people’s preferences: a methodological review of the literature. Health Economics 14(2): 197-208
6. Lohr, KN et al (1986) Use of medical care in the Rand Health Insurance Experiment. Diagnosis- and service-specific analyses in a randomized controlled trial. Medical Care 24(9): S1-87
7. Nyman, J (1999). The economics of moral hazard revisited. Journal of Health Economics 18(6): 811-824
8. Propper, C et al (2008) Competition and quality: evidence from the NHS internal market 1991-9. The Economic Journal 119(525): 138-170
9. van Doorslaer, E et al (1997) Income-related inequalities in health: some international comparisons. Journal of Health Economics 16(1): 93-112
10. Zweifel, P & Manning, WG (2000) Moral hazard and consumer incentives in health care. Handbook of health economics 1: 409-459

## Were QALYs invented in 1956?

On May 2 this year, the topic chosen by Tim Harford in his ‘Undercover economist‘ column in the Financial Times was ‘Healthcare: the final reckoning‘. Tim is always interesting, but I don’t find him sound on health care economics and I disagreed with much of what he said in his column. Others, including Jeff Round of UCL and Mark Sculpher from University of York have challenged his comments, and I’ll simply endorse their statements on substantive issues. But I was intrigued by the background that Tim used to introduce a description and critique of QALYs. Apparently “The Qaly was dreamt up in 1956 by two health economists, Christopher Cundell and Carlos McCartney.” Now, if that is true, then I do apologise to those two economists, or to their friends and families if they are no longer with us, for what I am about to say. But as someone who knows a bit about health economics and especially about QALYs, I found it mysterious that I had never heard of these inventors or of their act of inventing. Apart from which, my understanding has been that QALYs were not invented in a single act by one or more named individuals. Instead, several different sources, in different intellectual disciplines and traditions, have contributed to their development.

I shared my query about this on twitter, after which James Raftery from Southampton University and I both investigated where this invention claim came from. Using some variations in wording, it is to be found in many places on the web. But it was obvious that the original source for all of them was a Wikipedia article entitled ‘Quality-adjusted life year’. That article even had a citation for this claim, a review paper in a Serbian medical journal. It seemed to me to be unlikely that such a paper would actually have uncovered a fact about QALYs unknown to anyone else, and indeed the paper has no reference for its claim, just offering it as fact.

Adding to the mystery, the Wikipedia article actually claimed that three health economists had invented QALYs, including someone called “Toni Morgan”. Digging a little further, I found that the third inventor had been added relatively recently. Apologies to Toni if he or she is real and did help to invent QALYs, but I couldn’t help noticing that this name is an anagram of “Giant Moron” and may therefore be made up. The Serbian paper citation had also been added relatively recently, and that paper was published long after the original invention claim was made on Wikipedia. My conclusion is that the claim was originally made in the Wikipedia article; that was the unreferenced source for the same claim in the Serbian paper; and the Serbian paper was then added as a citation supporting the Wikipedia claim. This problem of circularity in the generation of fake facts is well-known, and Tim Harford, in acknowledging his error, pointed to this comic from xkcd:

It’s a lesson in how referencing and citation can go wrong. More digging shows that someone in the University of Ulster posted the original invented invention claim in December 2010, and the same or perhaps another wag at the same institution added the third name in February 2014. I guess they did it for the lulz. I am not too dismayed by the fact that no-one who knows anything about QALYs picked this spoof up for over three years, since such people should not really be using Wikipedia as a source. However, as Chris Sampson has suggested elsewhere on this blog, health economists should be paying more attention to what is published about their discipline on Wikipedia. Mis- or dis-information should be corrected or challenged.

I’m pleased to say that Don Husereau from the Institute of Health Economics in Canada has now fixed the Wikipedia entry and given a much more accurate and sensible history. Let’s hope that this is the last we hear of Christopher, Carlos and Toni. Unless of course they are real, in which case I hope their pioneering work gets the recognition that it deserves.

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Posted by on May 9, 2014 in News

## The ‘value’ in value based pricing

The use of labour market outcomes in the Value Based Pricing scheme is inconsistent with the concept of value

This year, the Department of Health in the UK will begin using a new system of ‘value based pricing’ (VBP) to set prices for medicines and other health technologies. Decisions regarding the adoption of new medical technologies relies, in a large part, upon formal assessments of cost-effectiveness; these assessments are most often carried out by the National Institute of Health and Care Excellence (NICE). The aim of the new VBP system is to better capture the benefits of a certain treatment, particularly benefits accruing both directly to other non-treated individuals such as carers and indirectly to society as a whole. In the latter case, these indirect benefits are referred to as wider societal benefits (WSB), and are to be measured in terms of market based activity—specifically, the difference between productivity and consumption. However, I believe that the proposed methodology is inconsistent with the concept of ‘value’.

The concept of value is hard to specify, but whenever we talk of something being ‘good’, ‘better’, or ‘best’, or conversely ‘bad’, ‘worse’, or ‘worst’, then we are talking in terms of value. The health technology assessments (HTAs) conducted in the UK, generally define that what is best is the state of affairs with the greatest amount of goodness, and hence value, overall, subject to a budget constraint. But how do we measure value in these HTAs? The standard measure used in HTAs currently, is the quality adjusted life year (QALY); a medicine that leads to the largest number of QALYs overall within our budget constraint, i.e. a cost-effective medicine, is good. And, in this sense we can say one treatment is better than another in terms of its cost-effectiveness. At this point it becomes important to think about different types of value.

An important contrast is made between intrinsic value and instrumental value. Something with intrinsic value is good in and of itself whereas things of instrumental value are good because they causally lead to intrinsically good things. Consider money, it is good only because it leads to things that are themselves good, such as good housing or an HDTV, which themselves may be good because of what they lead to, such as a safe and clean environment and relaxing weekends watching sport, for example. As a third category, there is also constitutive value; while instrumental values causally lead to intrinsic values, constitutive value constitutes intrinsic value without causing it. For example, giving you money may lead to your pleasure, and this pleasure constitutes your happiness without necessarily causing it. In these distinctions, QALYs arguably have constitutive value in that they constitute well-being and longevity.

One further distinction is the difference between value monism and pluralism. A monist believes that there is only one kind of value to which all other values are reducible. Economists arguably fall into this camp since they often use utility as the encompassing super-value. This position has some attractive features, such as being able to explain rational choice through, for example, diminishing marginal value. The opposing school of thought is value pluralism that posits that different kinds of value (e.g. happiness and liberty) are distinct and hence incommensurable. Thus, the QALY may be constitutive of the singular super-value, which we can refer to as utility without loss of generality, or be a measure of just one kind of value, such as quality of life.

In a monist perspective, we could consider the aim of VBP to estimate the effect of healthcare expenditure for each specific technology on overall utility. The new VBP system aims to capture not only the utility accruing directly to the recipients of a medical technology (which QALYs are constitutive of) but also the utility generated by the increased level of resources in the economy caused by their increase in productivity (i.e. the instrumental value of productivity). In this sense, the VBP system aims to estimate a multiplier effect of healthcare expenditure for each technology. But, the VBP methodology would appear inconsistent with this position. Firstly, the WSBs of a treatment are determined by productivity minus consumption, but consumption generates utility. All rational decisions regarding consumption boil down to utility, in a monist sense. Secondly, the changes to societal welfare caused by increased productivity are estimated by calculating the effect of changes in individual QALYs on productivity. There is no reason to suspect the effect of productivity on QALYs is at all similar.

We could adopt a pluralist position in which QALYs constitute only one kind of value and productivity is instrumental for another kind of value. But if these types of value are distinct then they are incommensurable and cannot be combined. Furthermore, linking productivity to other types of value, such as liberty or happiness is certainly fraught with difficulty and not discussed as such in the VBP literature. We could argue that just because two things are incommensurable does not mean they are incomparable—to take a particularly contrived example, we may prefer to reimburse a medicine that treats a disease that afflicts only charity workers rather than a sales person specific disease out of a particular notion of value (I have no qualms to bear against people who work in sales). In this way we could create an ordinal scale, but this would preclude the calculation of thresholds and cost-effectiveness ratios, the very existence of which HTA relies upon.

I believe VBP to be a good idea in order to more accurately capture the effects of healthcare expenditure but the V in VBP is particularly nebulous. At the very least, however, VBP is a step in the right direction and will lead to wider discussions about the often under-considered normative side of economics.