Chris Sampson’s journal round-up for 29th 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.

“Naming and framing”: The impact of labeling on health state values for multiple sclerosis. Medical Decision Making [PubMedPublished 21st May 2017

Tell someone that the health state that they’re valuing is actually related to cancer, and they’ll give you a different value than if you hadn’t mentioned cancer. A lower value, probably. There’s a growing amount of evidence that ‘labelling’ health state descriptions with the name of a particular disease can influence the resulting values. Generally, the evidence is that mentioning the disease will lower values, though that’s probably because researchers have been selecting diseases that they think will show this. (Has anyone tried it for hayfever?) The jury is out on whether labelling is a good thing or a bad thing, so in the meantime, we need evidence for particular diseases to help us understand what’s going on. This study looks at MS. Two UK-representative samples (n = 1576; n = 1641) completed an online TTO valuation task for states defined using the condition-specific preference-based MSIS-8D. Participants were first asked to complete the MSIS-8D to provide their own health state, and then to rank three MSIS-8D states and also complete a practice TTO task. For the preference elicitation proper, individuals were presented with a set of 5 MSIS-8D health states. One group were asked to imagine that they had MS and were provided with some information and a link to the NHS Choices website. The authors’ first analysis tests for a difference due to labelling. Their second analysis creates two alternative tariffs for the MSIS-8D based on the two surveys. People in the label group reported lower health state values on average. The size of this labelling-related decrement was greater for less severe health states. The creation of the tariffs seemed to show that labelling does not have a consistent impact across dimensions. This means that, in practice, the two tariffs could favour different types of interventions, depending on for which dimensions benefits might be observed. The tariff derived from the label group demonstrated slightly poorer predictive performance. This study tells us that label-or-not is a decision that will influence the relative cost-effectiveness of interventions for MS. But we still need a sound basis for making that choice.

Nudges in a post-truth world. Journal of Medical Ethics [PubMed] Published 19th May 2017

Not everyone likes the idea of nudges. They can be used to get people to behave in ways that are ‘better’… but who decides what is better? Truth, surely, we can all agree, is better. There are strong forces against the truth, whether they be our own cognitive biases, the mainstream media (FAKE NEWS!!!), or Nutella trying to tell us they offer a healthy breakfast option thanks to all that calcium. In this essay, the author outlines a special kind of nudge, which he refers to as a ‘nudge to reason’. The paper starts with a summary of the evidence regarding the failure of people to change their minds in response to evidence, and the backfire effect, whereby false beliefs become even more entrenched in light of conflicting evidence. Memory failures, and the ease with which people can handle the information, are identified as key reasons for perverse responses to evidence. The author then goes on to look at the evidence in relation to the conditions in which people do respond to evidence. In particular, where people get their evidence matters (we still trust academics, right?). The persuasiveness of evidence can be influenced by the way it is delivered. So why not nudge towards the truth? The author focuses on a key objection to nudges; that they do not protect freedom in a substantive sense because they bypass people’s capacities for deliberation. Nudges take advantage of non-rational features of human nature and fail to treat people as autonomous agents deserving of respect. One of the reasons I’ve never much like nudges is that they could promote ignorance and reinforce biases. Nudges to reason, on the other hand, influence behaviour indirectly via beliefs: changing behaviour by changing minds by improving responses to genuine evidence. The author argues that nudges to reason do not bypass the deliberative capacities of agents at all, but rather appeal to them, and are thus permissible. They operate by appealing to mechanisms that are partially constitutive of rationality and this is itself part of what defines our substantive freedom. We could also extend this to argue that we have a moral responsibility to frame arguments in a way that is truth-conducive, in order to show respect to individuals. I think health economists are in a great position to contribute to these debates. Our subfield exists principally because of uncertainty and asymmetry of information in health care. We’ve been studying these things for years. I’m convinced by the author’s arguments about the permissibility of nudges to reason. But they’d probably make for flaccid public policy. Nudges to reason would surely be dominated by nudges to ignorance. Either people need coercing towards the truth or those nudges to ignorance need to be shut down.

How should hospital reimbursement be refined to support concentration of complex care services? Health Economics [PubMed] Published 19th May 2017

Treating rare and complex conditions in specialist centres may be good for patients. We might expect these patients to be especially expensive to treat compared with people treated in general hospitals. Therefore, unless reimbursement mechanisms are able to account for this, specialist hospitals will be financially disadvantaged and concentration might not be sustainable. Healthcare Resource Groups (HRGs) – the basis for current payments – only work if variation in cost is not related to any differences in the types of patients treated at particular hospitals. This study looks at hospitals that might be at risk of financial disadvantage due to differences in casemix complexity. Individual-level Hospital Episode Statistics for 2013-14 were matched to hospital-level Reference Costs and a set of indicators for the use of specialist services were applied. The data included 12.4 million patients of whom 766,204 received complex care. The authors construct a random effects model estimating the cost difference associated with complex care, by modelling the impact of a set of complex care markers on individual-level cost estimates. The Gini coefficient is estimated to look at the concentration of complex care across hospitals. Most of the complex care markers were associated with significantly higher costs. 26 of 69 types of complex care were associated with costs more than 10% higher. What’s more, complex care was concentrated among relatively few hospitals with a mean Gini coefficient of 0.88. Two possible approaches to fixing the payment system are considered: i) recalculation of the HRG price to include a top-up or ii) a more complex refinement of the allocation of patients to different HRGs. The second option becomes less attractive as more HRGs are subject to this refinement as we could end up with just one hospital reporting all of the activity for a particular HRG. Based on the expected impact of these differences – in view of the size of the cost difference and the extent of distribution across different HRGs and hospitals – the authors are able to make recommendations about which HRGs might require refinement. The study also hints at an interesting challenge. Some of the complex care services were associated with lower costs where care was concentrated in very few centres, suggesting that concentration could give rise to cost savings. This could imply that some HRGs may need refining downwards with complexity, which feels a bit counterintuitive. My only criticism of the paper? The references include at least 3 web pages that are no longer there. Please use WebCite, people!

Credits

A taxonomy of behavioural interventions

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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.

DOI: 10.6084/m9.figshare.1232109

The economics of the ‘nudge’: Why the UK government’s new public health policy won’t work

The UK Government recently announced its plans for “Public Health England”, with ‘nudging’ high on the agenda. What the government considers a ‘nudge’ is unclear, though it seems to mean giving people the opportunity to improve their own health. Here’s why I think this is a flawed policy.

As a recent BMJ article pointed out, there appear to be many more ‘nudges’ in the opposite direction. As long as private companies are allowed to push their delicious fatty foods and cigarettes with all the marketing that they desire, it seems that any government ‘nudge’ will be overshadowed. In the UK, the total value of tobacco sales alone was £11.3 billion, while the government suggests a figure of just £4 billion to be ring-fenced for the new public health service. True, Public Health England will be able to spend almost all of this money on ‘nudging’, but with Diageo (the world’s biggest spirit maker) alone pulling in revenue of £7.1 billion and profits of £1.6 billion in 2010/11, it’s hard to see how a measly £4 billion could counteract the industries’ marketing ‘nudges’.

This doesn’t even take in to account the utility individuals gain from scoffing that 3rd bag of Walkers, or guzzling that penultimate pint of cheap lager at last orders. Nor does it take in to account the fact that you really can’t be bothered using that new-cycle-path-shaped ‘nudge’ to get to work tomorrow, or the fact that you’ll have to sacrifice an hour of your valuable leisure time to take your kid to that new playground. Nor does it take in to account the fact that you don’t even have a bike, and you’re too drunk to even notice the ‘nudge’ on that second bottle that says “the average British drinker drinks one glass of wine a night”.

To be fair, I know very little about behavioural economics, and I have not read “Nudge” (though I will). However, my understanding of compensating and equivalent variations tells me that people are going to need some hard-cash-value incentives or disincentives to have them change their behaviour; either that or regulatory restrictions.

So, where does the ‘nudge’ stand in relation to health economics? Is it something that we should be harnessing? Will anybody be trying to evaluate the QALY loss or gain from particular ‘nudges’? Surely we should, otherwise we might be getting ‘nudged’ into accepting a policy that currently has almost no empirical support.