How do you solve a problem like obesity?


Making headlines this morning (Thursday 20th November) has been the report by McKinsey Global Institute, an offshoot of the management consultancy McKinsey, on the global economic impact of obesity. This report estimates that $2.0 trillion is spent annually worldwide as a result of obesity, which it compares to the global burden of smoking and armed conflict; the quoted figure is comprised of various elements such as productivity losses and spending to mitigate obesity. Certainly, the magnitude of the burden is in part due to the fact that obesity is generally a developed nation problem, and these nations typically spend many orders of magnitude more on healthcare than their developing nation counterparts. The claim then that obesity represents a problem as serious as armed conflict and violence may therefore end up being somewhat spurious if global issues were measured on a scale other than total financial expenditure. Nonetheless, the report acknowledges such issues, and provides a comprehensive summary of obesity related statistics to demonstrate them.

One of the main aims of the report is to identify interventions that may be used to tackle obesity in order to reduce expenditure resulting from obesity. To credit the McKinsey report, it recognises the complex nature of obesity and reproduces the above figure, asking if it is possible to tackle obesity given its complex aetiology. The report even provides some evidence that various social and cultural factors are at play. However, the authors write that while the background may be complex, the proximal causes are well known, and that interventions that target these proximal causes are both more feasible and simpler to implement and ought to be the ones they consider. This expression of a certain public health ideology, I would argue, is an issue with many discussions about population and global health issues.

This is the notion that public health and healthcare should be focussed on targeting individuals and modifying their behaviour, through such things as technological innovation, divorced from social, economic, or political contexts. For example, the McKinsey report suggests calorie labelling, advertising restrictions, and public health campaigns. However, if we want to tackle health issues such as obesity at the aggregate level then we should probably consider asking aggregate level questions, such as why markets are producing inefficient outcomes in terms of the health of the labour force, and why there is an oversupply of calories in some countries and an undersupply elsewhere. Policies that result from such analyses are likely to be more complex but are also more likely to be efficacious.

Historically, public health progress has been the result of a convergence of a wide range of social, economic, and political projects. Countries have adopted various strategies, historically, to reduce mortality including: better income distribution; improved diet; public health; medicine; changes in household education – however, none of these policies have been universally successful on its own and real progress requires integration of various social, medical, political, and economic strategies (Brin, 2005The Lancet—University of Oslo Commission on Global Governance for Health, 2014). The interventions in the report seem to me to be somewhat limp in the face of what they call a problem with a ‘global burden’.


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Is there any use in publishing surgeons’ death rates?


Today sees the publication of surgeons’ death rates on the MyNHS website (see Guardian and BBC stories). The website presents full lists of surgeons by specialty alongside either blue circles with a large ‘OK’ inside, grey circles with question marks, or green circles with ticks, to reflect, respectively, whether the surgeons’ risk adjusted mortality (or other significant morbidity) falls within expected limits or is a negative or positive outlier. The important question here is whether these measures actually reflect surgeon quality.

This issue returns to the perennial question of measuring healthcare quality. In terms of surgeon quality, we should consider that a high quality surgeon is one which makes fewer errors, and as such causes fewer preventable adverse events. Deaths, or other adverse health outcomes, that would have occurred regardless of the responsible consultant cannot be attributed to variations in surgeon quality. Therefore, the question we should ask is whether risk-adjusted mortality is a good proxy for preventable mortality. Girling et al (2012) ask exactly this question in relation to case-mix adjusted hospital mortality and preventable mortality and conclude, ‘If 6% of hospital deaths are preventable (as suggested by the literature), the predictive value of the SMR can be no greater than 9%. This value could rise to 30%, if 15% of deaths are preventable.’ A similar argument applies to individual physicians.

It is also important to ask what the consequences of publishing such data would be on patient and surgeon behaviour. In the latter case, surgeons may become more risk averse, avoiding cases in which there is a greater chance of non-preventable mortality since these cases would reflect badly against them. Indeed, speaking on this morning’s (Wednesday 19th November) Today programme, Ian Martin, from the Federation of Surgical Speciality Associations, suggested that there was anecdotal evidence indicating that this was the case. This is certainly not in the interests of the patient population. The publication of these data may also alter the way in which patients and surgeons are matched to one another, since patients will likely decide not to visit a surgeon with a high risk adjusted mortality rate. Yet, this altering of a specific surgeon’s case-mix resulting from patient choice, will mean that previous adjusted mortality rates will have poor predictive value for future adjusted mortality rates, and even less predictive value for preventable mortality.

These figures are published in the name of patient choice. Yet they may actually contain little useful information to support such a choice.


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Is payment by diagnosis for dementia a good strategy?

There is a considerable furore surrounding the new proposal to pay GPs £55 for each dementia diagnosis. The Patients Association called it “a step too far” that would mean a “bounty on the head” of some patients (link), while the Daily Mail quoted a GP as describing the programme as ‘an intellectual and ethical travesty.’ Vitriol aside, there are clearly some issues with incentivising clinicians on the basis of making diagnoses.

Payment by diagnosis could be compared to other schemes, such as the Pay for Performance (P4P) scheme, which Sutton et al (2012) demonstrated had a mortality reducing effect in hospitals in England. However, P4P created incentives by paying doctors on the basis of specific process variables, such as prescribing aspirin at discharge for patients with acute myocardial infarction. These incentives act by altering the opportunity cost of time. For clinicians qua clinicians they may prioritise their time differently in order to increase their revenue from medical practice so that they are more likely to engage in clinical tasks with higher earnings potential. For clinicians qua individuals they may allocate more time to labour, substituting from leisure or work at home, at the benefit of patients. The P4P interventions operate at a specific part of the healthcare causal chain, at the level of process or specific interventions, which may then generate an increase in detection rates or a reduction in adverse events, all leading to improved patient outcomes. Incentivising physicians by diagnosis, however, operates at a different part of the healthcare process. Certainly, the payment for diagnosis may ensure GPs spend more time diagnosing or working with potential dementia patients, in order to boost dementia detection rates; however, equally, a diagnosis per se does not require much time to make and doctors may be incentivised to make incorrect diagnoses. Furthermore, in distorting the opportunity costs of physician time, GPs will allocate more time to identifying dementia patients at the potential risk of neglecting other patients.

Dementia is a concern for an ageing population. Only around 50% of dementia cases are thought to have been diagnosed. The global burden of dementia and Alzheimer’s disease was estimated to be $422 billion in 2009, of which $124 billion was unpaid care (Wimo et al, 2010). One strategy for reducing the burden of dementia is earlier detection – before the development of frank dementia most patients have a period of cognitive decline and suffer from what is termed mild cognitive impairment (MCI) (Petersen et al, 1999). While the deterioration of cognitive function is inexorable in dementia patients, it may possibly be slowed with appropriate therapy, which would then potentially delay or prevent a patient requiring highly costly care for late stage dementia (Gestios et al, 2010, 2012, Petersen et al, 2005, Teixera et al, 2012). There would also be considerable benefit to people with MCI and their families where the devastating impact of dementia can be reduced. Whether or not an incentive for dementia diagnoses would lead to earlier detection remains to be seen. Nonetheless, it would seem that incentivising testing for MCI in order to improve early detection, would be a more appropriate strategy. Indeed, this is the aim with type 2 diabetes where the potential benefits of a screening programme have been discussed widely (Gillies et al, 2008, Kahn et al, 2010, Schaufler and Wolff, 2010, among many examples). Simply paying doctors every time they diagnose a case of diabetes would, at face value, be less effective, particularly since earlier cases may be harder to detect – the harder to detect cases would require more time on the part of the clinician, the marginal benefit of which may be smaller than the marginal cost to the clinician. Incentivising for conducting tests arguably does not discriminate on the same basis.

While this may be a step in the right direction to improve dementia detection rates, there may have been a more effective method of incentivising GPs than payment by diagnosis.

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Posted by on October 22, 2014 in Supply of Health Services


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Do economists care about patients?

I stand accused. Not of a particularly heinous crime, but of something that has given me pause for thought recently. During a discussion about a piece of work involving patient outcomes, I was accused of ‘thinking like an economist’. Had this come from an economist, it would have been meant in a complementary sense – ‘now you’re thinking like an economist!’ Alas, it was not an economist but a clinician and medical researcher who said it and as such was meant more along the lines of ‘Unfortunately, you’re thinking like an economist’. In particular, the comment was meant to suggest that I was missing the importance of the patient, that I was focussed solely on the numbers, that I knew the price of everything and the value of nothing. Perhaps I am exaggerating the meaning but the sentiment still stands. I felt that this was unfair, both to me in that particular circumstance, but to economists and economics in general. However, I do see why people may come to this understanding of economics; economic discourse to the untrained ear is abstract and separate from the real world, economics is often very mathematical.

Typically, most economic fields do not work as closely alongside natural science as does health economics. Medical sciences and health economics often overlap greatly in their interests; many health economists publish in both medical and economics journals. However, their approach differs somewhat and the difference between them highlights an important difference between the natural and social sciences. Indeed, medical statistics and health econometrics are often considered different subjects, despite the similarities in the tools they use to approach the problems they face. Perhaps, then, it is important to analyse the distinction between the fields to find out the implications of ‘thinking like an economist’.

Economics is a social science. I am not going to provide a treatise on the philosophy of social science but will provide some thoughts on the distinction between the social sciences and the natural sciences. Arguably, the goal of science, social or natural, is to provide and validate statements which are epistemically objective. That is, we may say it produces falsifiable statements and facts. The difference between the natural and social sciences is that the former studies objects which are ontologically objective whereas the latter studies objects that are ontologically subjective. Or, to phrase it in another way, the objects of study of the natural sciences are observer independent and would exist and function whether or not there were humans or science; the social sciences studies observer dependent objects. The economy is a system of social relations in which economists participate and about which economists already have preconceived notions and have already made value judgements.

An education in the natural sciences involves teaching a student to ‘think scientifically’. To formulate and test falsifiable hypotheses about observer independent objects. An education in the social sciences involves teaching a student to re-observe the world in which they live, and to provide them with the tools to simplify it. This way we can think clearly about processes of causality in the social world. In economics, this often involves the use of mathematics and, importantly, a simplified vocabulary. This ‘econspeak’ does not identify new, economic objects – it is not more fundamental or philosophical than everyday language, it serves to simplify the social world. And, since economic words describe a social reality and since the social world is itself entirely linguistic, the meaning of economic words can always be restated in terms of everyday language and have the same meaning. For example, elasticity can be restated as ‘how much one variable changes in response to a change in another variable’, which can then be understood in terms of everyday social life – ‘how many more oranges would I buy if the price of oranges went up by 10p?’ But, in the natural sciences, the subject specific words cannot be restated in everyday language since they are not part of our world. The name of a specific protein cannot be restated in language any more simply, I could describe its function or its structure, but these explanations just skirt around the object, trying to identify it exactly. Our alternative ways of stating elasticity mean exactly the same thing.

Health economics is a social science. Medical science is perhaps more tricky to define. It has elements of both natural and social science since it studies how proteins and cells and ontologically objective objects function but also how behaviour affects health. Medicine is the application of science to improve health and is a social activity. But, many medical scientists come from a natural scientific background rather than a social scientific background. Thus, unless otherwise trained, economic representations of the social world will seem alien and abstract.

I certainly do not deny that economists lack the social interaction with many patients that health service staff have and that this may create a distance between the analyst and the people they study. This may even lead to a lack of understanding on the part of the economist about the nature of things and the state of the world that they examine. I would certainly advocate enhanced dialogue between practitioners of a variety of disciplines – this can only enhance the work being conducted – indeed, this goes both ways. Economists and clinicians alike may better learn about their subject matter. Social science has a lot to contribute to the understanding of medicine and its practice. Greater dialogue may mean greater understanding and fewer accusations such as this.


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Nurses on strike

Monday saw the first strike by health service staff in England and Wales for 32 years. This dispute surrounds the refusal of the government to implement a 1% pay rise recommended by the NHS pay review body. The reason for not awarding the pay increase given by the Secretary of State for Health, Jeremy Hunt, was that it is “unaffordable”.

There are a number of intersecting interests involved in any industrial action such as this where various stakeholders have a number of positions to consider. For example, the Secretary of State for Health must balance his mandate to protect public health with political considerations such as re-election and positioning within his party. The reasons for rejecting the pay increase, however, are typically given an economic flavour; in particular, Jeremy Hunt warned that an increase in pay this year may lead to the laying off of a large number of nurses next year, leading to a reduction in the quality of care. But, an examination of some of the economic issues surrounding the rejection of pay increases in the healthcare sector may suggest that the driving forces are more likely to be of a political nature.

In England and Wales, the wage paid to nurses is regulated by the state, and is homogeneous across all areas regardless of the local wage rate. Propper and van Reenen (2010) showed that in areas where the regulated nursing wage is lower than the ‘outside’ market wage there are reductions in the quality of nursing staff and hence healthcare quality, which they measured using hospital mortality rates for acute myocardial infarction. Moreover, they found that ‘the effect is “convex” in that the negative effect of regulation on hospital quality is much stronger in the high-cost areas (where regulated wages are much lower than the outside wage) than the positive effect in the low-cost areas (where regulated wages are higher than the outside wage).’ While these findings may be used to argue against a nationally regulated pay structure for health service staff, they certainly suggest that suppressing the nursing wage is likely to have deleterious consequences to patient health outcomes.

Much of the reasoning behind reducing pay is to do with constraining expenditure in the healthcare sector which, across most developed countries, is rising as a proportion of GDP. Nonetheless, there are sound arguments as to why we might expect healthcare to take up an increasing proportion of national expenditure, and furthermore, why this is not a worry. In particular, the Cost Disease argument (which has been previous discussed here and here), suggests that healthcare will take up a bigger and bigger proportion of the GDP pie, but that this pie will grow at least as quick. This is, in part, due to the low marginal rate of substitution between capital and labour and less than average rate of productivity growth in the healthcare sector. If these arguments hold, then governments may be unnecessarily reducing real terms health expenditure. Indeed, in many cases the government targets for NHS spending are wholly unrealistic (Appleby, 2012).

There have certainly been changes to the composition of the labour force in the healthcare sector. The density of nurses has declined from 12.21 per 1,000 people in 1997 to 8.93 per 1,000 people in 2013 while the density of physicians has increased from 2.3 to 2.79 per 1,000 over the same period (World Health Organisation – data here). This may perhaps reflect a replacement of some nursing tasks with capital or the evolving nature of medical care. However, in many areas, recommended nurse to patient ratios are not met; for example, in neonatal care, one recent survey of neonatal units found that 54% of observed shifts were understaffed with respect to recommended nurse to patient ratios (Pillay, 2012). However, given the relative lack of evidence on the cost-effectiveness of nurse to patient ratios, it cannot be said that the reduction in total nursing labour is the result of calculated cost-effectiveness decisions.

Taken together, it would seem that suppressing the nursing wage rate, or reducing the number of nurses, would have negative consequences on patient outcomes. There may certainly be an argument that the losses in quality are worth the costs saved, whether you agree with it or not, but no evidence has been presented to support this point. At a macroeconomic level, the austerity plan presented by many Western governments, the UK’s included, is rejected by a large proportion of economists.* As many economists and commentators have suggested the austerity programme is likely to be used to satisfy political ends rather than economic ones.** The reduction (in real terms) of the nursing wage may support political gains at the expense of healthcare quality and worse patient outcomes.

*For a discussion of these issues and numerous links, see the blogs of Paul Krugman, Simon Wren-Lewis, Martin Wolf, Jonathan Portes, and Chris Dillow among many others.

**Again, this wide ranging discussion is captured by many commentators, see, for example, here and here, from the above mentioned blogs, and this article.


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#HEJC for 24/10/2014

The next #HEJC discussion will take place Friday 24th October, at 1pm London time on Twitter. To see what this means for your time zone visit or join the Facebook event. For more information about the Health Economics Journal Club and how to take part, click here.

The paper for discussion is a working paper published by Glasgow Caledonian University’s Yunus Centre. The authors are Neil McHugh and colleagues. The title of the paper is:

Extending life for people with a terminal illness: a moral right or an expensive death? Exploring societal perspectives

Following the meeting, a transcript of the Twitter discussion can be downloaded here.

Links to the article



Summary of the paper

A lot of research effort has been spent on whether health economists’ most ingrained normative assumption should hold; is a QALY of equal value regardless of to whom it accrues. In the UK, the National Institute for Health and Care Excellence has given weighting to ‘special cases'; namely, life-extending drugs for patients near the end of their life (mainly for cancer). However, existing empirical research about whether societal values support such a weighting has given conflicting results.

McHugh et al, in their new working paper, present the first major mixed methods study of societal perspectives for QALY-weighting. The authors use Q methodology – which involves the ranking of opinion statements according to agreement – to elicit societal perspectives on the relative value of life extension for people with terminal illness. Opinion statements were collected from 4 sources:

  • newspaper articles
  • a NICE public consultation
  • 16 interviews with key informants
  • 3 focus groups with the general public

The Q sort was conducted with people from academia, the pharmaceutical industry, charities, patient groups, religious groups, clinicians, people with experience of terminal illness and a sample of the general public. The authors’ final sample included 61 Q sorts and factor analysis identified 3 distinguishable perspectives, which can be summarised as:

  1. A population perspective (value for money, no special cases)
  2. An individual perspective (value of life, not cost)
  3. A mixed perspective

Factor 1 individuals are unlikely to support any QALY-weighting, maintaining a utilitarian-type health-maximising perspective. Factor 2 respondents reject the denial of life extending treatments and assert that patients and their families should decided whether or not they wish to receive the treatment; regardless of cost. This group appear to disagree with cost-effectiveness analysis altogether. Factor 3 represents a more nuanced view, asserting that value is broader than health gain alone. However, factor 3 was associated with a focus on quality of life, and so support for expensive life-extending treatment would depend on this. It is unclear whether QALY-weighting would adequately achieve this.

Discussion points

  • Is the question of QALY-weighting a normative one or a positive one?
  • Are the three factors likely to be robust across ethical dilemmas other than terminal illness?
  • To what extent are the opinions associated with the 3 factors likely to be robust to further deliberation?
  • Are factor 2 respondents simply wrong?
  • Should QALY-weighting be based on democratic processes?
  • Is it of concern that current policy appears to reflect the views of health economists better than other groups?
  • Where do you stand?

Can’t join in with the Twitter discussion? Add your thoughts on the paper in the comments below.

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Posted by on October 15, 2014 in #HEJC, Health and its Value


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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.


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.

doi: 10.6084/m9.figshare.1152711

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


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