Chris Sampson’s journal round-up for 24th April 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.

The association between socioeconomic status and adult fast-food consumption in the U.S. Economics & Human Biology Published 19th April 2017

It’s an old stereotype, that people of lower socioeconomic status eat a lot of fast food, and that this contributes to poorer nutritional intake and therefore poorer health. As somebody with a deep affection for Gregg’s pasties and Pot Noodles, I’ve never really bought into the idea. Mainly because a lot of fast food isn’t particularly cheap. And anyway, what about all those cheesy paninis that the middle classes are chowing down on in Starbuck’s? Plus, wouldn’t the more well-off folk have a higher opportunity cost of time that would make fast food more attractive? Happily for me, this paper provides some evidence to support these notions. The study uses 3 recent waves of data from the National Longitudinal Survey of Youth, with 8136 participants born between 1957 and 1964. The authors test for an income gradient in adult fast food consumption, as well as any relationship to wealth. I think that makes it extra interesting because wealth is likely to be more indicative of social class (which is probably what people really think about when it comes to the stereotype). The investigation of wealth also sets it apart from previous studies, which report mixed findings for the income gradient. The number of times people consumed fast food in the preceding 7 days is modelled as a function of price, time requirement, preferences and monetary resources (income and wealth). The models included estimators for these predictors and a number of health behaviour indicators and demographic variables. Logistic models distinguish fast food eaters and OLS and negative binomial models estimate how often fast food is eaten. 79% ate fast food at least once, and 23% were frequent fast food eaters. In short, there isn’t much variation by income and wealth. What there is suggests an inverted U-shape pattern, which is more pronounced when looking at income than wealth. The regression results show that there isn’t much of a relationship between wealth and the number of times a respondent ate fast food. Income is positively related to the number of fast food meals eaten. But other variables were far more important. Living in a central city and being employed were associated with greater fast food consumption, while a tendency to check ingredients was associated with a lower probability of eating fast food. The study has some important policy implications, particularly as our preconceptions may mean that interventions are targeting the wrong groups of people.

Views of the UK general public on important aspects of health not captured by EQ-5D. The Patient [PubMed] Published 13th April 2017

The notion that the EQ-5D might not reflect important aspects of health-related quality of life is a familiar one for those of us working on trial-based analyses. Some of the claims we hear might just be special pleading, but it’s hard to deny at least some truth. What really matters – if we’re trying to elicit societal values – is what the public thinks. This study tries to find out. Face-to-face interviews were conducted in which people completed time trade-off and discrete choice experiment tasks for EQ-5D-5L states. These were followed by a set of questions about the value of alternative upper anchors (e.g. ‘full health’, ‘11111’) and whether respondents believed that relevant health or quality of life domains were missing from the EQ-5D questionnaire. This paper focuses on the aspects of health that people identified as being missing, using a content analysis framework. There were 436 respondents, about half of whom reported being in a 11111 EQ-5D state. 41% of participants considered the EQ-5D questionnaire to be missing some important aspect of health. The authors identified 22 (!) different themes and attached people’s responses to these themes. Sensory deprivation and mental health were the two biggies, with many more responses than other themes. 50 people referred to vision, hearing or other sensory loss. 29 referred to mental health generally while 28 referred to specific mental health problems. This study constitutes a guide for future research and for the development of the EQ-5D and other classification systems. Obviously, the objective of the EQ-5D is not to reflect all domains. And it may be that the public’s suggestions – verbatim, at least – aren’t sensible. 10 people stated ‘cancer’, for example. But the importance of mental health and sensory deprivation in describing the evaluative space does warrant further investigation.

Re-thinking ‘The different perspectives that can be used when eliciting preferences in health’. Health Economics [PubMed] Published 21st March 2017

Pedantry is a virtue when it comes to valuing health states, which is why you’ll often find me banging on about the need for clarity. And why I like this paper. The authors look at a 2003 article by Dolan and co that outlined the different perspectives that health preference researchers ought to be using (though notably aren’t) when presenting elicitation questions to respondents. Dolan and co defined 6 perspectives along two dimensions: preferences (personal, social and socially-inclusive personal) and context (ex ante and ex post). This paper presents the argument that Dolan and co’s framework is incomplete. The authors throw new questions into the mix regarding who the user of treatment is, who the payer is and who is assessing the value, as well as introducing consideration of the timing of illness and the nature of risk. This gives rise to a total of 23 different perspectives along the dimensions of preferences (personal, social, socially-inclusive personal, non-use and proxy) and context (4 ex ante and 1 ex post). This new classification makes important distinctions between different perspectives, and health preference researchers really ought to heed its advice. However, I still think it’s limited. As I described in a recent blog post and discussed at a recent HESG meeting, I think the way we talk about ex ante and ex post in this context is very confused. In fact, this paper demonstrates the problem nicely. The authors first discuss the ex post context, the focus being on the value of ‘treatment’ (an event). Then the paper moves on to the ex ante context, and the discussion relates to ‘illness’ (a state). The problem is that health state valuation exercises aren’t (explicitly) about valuing treatments – or illnesses – but about valuing health states in relation to other health states. ‘Ex ante’ means making judgements about something before an event, and ‘ex post’ means to do so after it. But we’re trying to conduct health state valuation, not health event valuation. May the pedantry continue.

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Chris Sampson’s journal round-up for 3rd April 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.

Return on investment of public health interventions: a systematic review. Journal of Epidemiology & Community Health [PubMed] Published 29th March 2017

Cost-effectiveness analysis in the context of public health is tricky. Often the health benefits are small at the individual level and the returns to investment might be cross-sectoral. Lots of smart people believe that spending on public health is low in proportion to other health spending. Here we have a systematic review of studies reporting cost-benefit ratios (CBR) or return on investment (ROI) estimates for public health interventions. The stated aim of the paper is to demonstrate the false economy associated with cuts to public health spending. 52 titles were included from a search that identified 2957. The inclusion and exclusion criteria are not very clear, with some studies rejected on the basis of ‘poor generalisability to the UK’. There’s a bit too much subjectivity sneaking around in the methods for my liking.  Results for CBR and ROI estimates are presented according to local or national level and grouped by ‘specialism’. From all studies, the median CBR was 8.3 and the median ROI was 14.3. As we might have suspected, public health interventions are cost-saving in a big way. National health protection and legislative interventions offered the greatest return on investment. While there is wide variation in the results, all specialism groupings showed a positive return on average. I don’t doubt the truth of the study’s message – that cuts to public health spending are foolish. But the review doesn’t really demonstrate what the authors want it to demonstrate. We don’t know what (if any) disinvestment is taking place with respect to the interventions identified in the review. The results presented in the study represent a useful reference point for discussion and further analysis, but they aren’t a sufficient basis for supporting general increases in public health spending. That said, the study adds to an already resounding call and may help bring more attention to the issue.

Acceptable health and priority weighting: discussing a reference-level approach using sufficientarian reasoning. Social Science & Medicine Published 27th March 2017

In some ways, the moral principle of sufficiency is very attractive. It acknowledges a desire for redistribution from the haves to the have-nots and may also make for a more manageable goal than all-out maximisation. It may also be particularly useful in specific situations, such as evaluating health care for the elderly, for whom ‘full health’ is never achievable and not a meaningful reference point. This paper presents a discussion of the normative issues at play, drawing insights from the distributive justice literature. We’re reminded of the fair innings argument as a familiar sufficientarian flavoured allocation principle. The sufficientarian approach is outlined in contrast to egalitarianism and prioritarianism. Strict sufficientarian value weighting is not a good idea. If we suppose a socially ‘acceptable’ health state value of 0.7, such an approach would – for example – value an improvement from 0.69 to 0.71 for one person as infinitely more valuable than an improvement from 0.2 to 0.6 for the whole population. The authors go on to outline some more relaxed sufficiency weightings, whereby improvements below the threshold are attributed a value greater than 0 (though still less than those achieving sufficiency). The sufficientarian approach alone is (forgive me) an insufficient framework for the allocation of health care resources and cannot represent the kind of societal preferences that have been observed in the literature. Thus, hybrids are proposed. In particular, a sufficientarian-prioritarian weighting function is presented and the authors suggest that this may be a useful basis for priority setting. One can imagine a very weak form of the sufficientarian approach that corresponds to a prioritarian weighting function that is (perhaps) concave below the threshold and convex above it. Still, we have the major problem of identifying a level of acceptable health that is not arbitrary. The real question you need to ask yourself is this: do you really want health economists to start arguing about another threshold?

Emotions and scope effects in the monetary valuation of health. The European Journal of Health Economics [PubMed] Published 24th March 2017

It seems obvious that emotions could affect the value people attach to goods and services, but little research has been conducted with respect to willingness to pay for health services. This study considers the relationship between a person’s self-reported fear of being operated on and their willingness to pay for risk-reducing drug-eluting stents. A sample of 1479 people in Spain made a series of choices between bare-metal stents at no cost and drug-eluting stents with some out-of-pocket cost, alongside a set of sociodemographic questions and a fear of surgery Likert scale. Each respondent provided 8 responses with 4 different risk reductions and 2 different willingness to pay ‘bids’. The authors outline what they call a ‘cognitive-emotional random utility model’ including an ’emotional shift effect’. Four different models are presented to demonstrate the predictive value of the emotion levels interacting with the risk reduction levels. The sample was split roughly in half according to whether people reported high emotion (8, 9 or 10 on the fear Likert) or low emotion (<8). People who reported more fear of being operated on were willing to pay more for risk reductions, which is the obvious result. More interesting is that the high emotion group exhibited a lower sensitivity to scope – that is, there wasn’t much difference in their valuation of the alternative magnitudes of risk reduction. This constitutes a problem for willingness to pay estimates in this group as it may prevent the elicitation of meaningful values, and it is perhaps another reason why we usually go for collective approaches to health state valuation. The authors conclude that emotional response is a bias that needs to be corrected. I don’t buy this interpretation and would tend to the view that the bias that needs correcting here is that of the economist. Emotions may be a justifiable reflection of personality traits that ought to determine preferences, at least at the individual level. But I do agree with the authors that this is an interesting field for further research if only to understand possible sources of heterogeneity in health state valuation.

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The irrelevance of inference: (almost) 20 years on is it still irrelevant?

The Irrelevance of Inference was a seminal paper published by Karl Claxton in 1999. In it he outlines a stochastic decision making approach to the evaluation of health technologies. A key point that he makes is that we need only to examine the posterior mean incremental net benefit of one technology compared to another to make a decision. Other aspects of the distribution of incremental net benefits are irrelevant – hence the title.

I hated this idea. From a Bayesian perspective estimation and inference is a decision problem. Surely uncertainty matters! But, in the extra-welfarist framework that we generally conduct cost-effectiveness analysis in, it is irrefutable. To see why let’s consider a basic decision making framework.

There are three aspects to a decision problem. Firstly, there is a state of the world, \theta \in \Theta with density \pi(\theta). In this instance it is the net benefits in the population, but could be the state of the economy, or effectiveness of a medical intervention in other contexts, for example. Secondly, there is the possible actions denoted by a \in \mathcal{A}. There might be a discrete set of actions or a continuum of possibilities. Finally, there is the loss function L(a,\theta). The loss function describes the losses or costs associated with making decision a given that \theta is the state of nature. The action that should be taken is the one which minimises expected losses \rho(\theta,a)=E_\theta(L(a,\theta)). Minimising losses can be seen as analogous to maximising utility. We also observe data x=[x_1,...,x_N]' that provide information on the parameter \theta. Our state of knowledge regarding this parameter is then captured by the posterior distribution \pi(\theta|x). Our expected losses should be calculated with respect to this distribution.

Given the data and posterior distribution of incremental net benefits, we need to make a choice about a value (a Bayes estimator), that minimises expected losses. The opportunity loss from making the wrong decision is “the difference in net benefit between the best choice and the choice actually made.” So the decision falls down to deciding whether the incremental net benefits are positive or negative (and hence whether to invest), \mathcal{A}=[a^+,a^-]. The losses are linear if we make the wrong decision:

L(a^+,\theta) = 0 if \theta >0 and L(a^+,\theta) = \theta if \theta <0

L(a^-,\theta) = - \theta if \theta >0 and L(a^+,\theta) = 0 if \theta <0

So we should decide that the incremental net benefits are positive if

E_\theta(L(a^+,\theta)) - E_\theta(L(a^-,\theta)) > 0

which is equivalent to

\int_0^\infty \theta dF^{\pi(\theta|x)}(\theta) - \int_{-\infty}^0 -\theta dF^{\pi(\theta|x)}(\theta) = \int_{-\infty}^\infty \theta dF^{\pi(\theta|x)}(\theta) > 0

which is obviously equivalent to E(\theta|x)>0 – the posterior mean!

If our aim is simply the estimation of net benefits (so \mathcal{A} \subseteq \mathbb{R}), different loss functions lead to different estimators. If we have a squared loss function L(a, \theta)=|\theta-a|^2 then again we should choose the posterior mean. However, other choices of loss function lead to other estimators. The linear loss function, L(a, \theta)=|\theta-a| leads to the posterior median. And a ‘0-1’ loss function: L(a, \theta)=0 if a=\theta and L(a, \theta)=1 if a \neq \theta, gives the posterior mode, which is also the maximum likelihood estimator (MLE) if we have a uniform prior. This latter point does suggest that MLEs will not give the ‘correct’ answer if the net benefit distribution is asymmetric. The loss function is therefore important. But for the purposes of the decision between technologies I see no good reason to reject our initial loss function.

Claxton also noted that equity considerations could be incorporated through ‘adjustments to the measure of outcome’. This could be some kind of weighting scheme. However, this is where I might begin to depart from the claim of the irrelevance of inference. I prefer a social decision maker approach to evaluation in the vein of cost-benefit analysis as discussed by the brilliant Alan Williams. This approach allows for non-market outcomes that extra-welfarism might include but classical welfarism would exclude; their valuations could be arrived at by a political, democratic process or by other means. It also permits inequality aversion and other features that I find are a perhaps more accurate reflection of a political decision making approach. However, one must be aware of all the flaws and failures of this approach, which Williams so neatly describes.

In a social decision maker framework, the decision that should be made is the one that maximises a social welfare function. A utility function expresses social preferences over the distribution of utility in the population, the social welfare function aggregates utility and is usually assumed to be linear (utilitarian). If the utility function is inequality averse then the variance obviously does matter. But, in making this claim I am moving away from the arguments of Claxton’s paper and towards a discussion of the relative merits extra-welfarism and other approaches.

Perhaps the statement that inference was irrelevant was made just to capture our attention. After all the process of updating our knowledge of the net benefits of alternatives from data is inference. But Claxton’s statement refers more to the process of hypothesis testing and p-values (or Bayesian ranges of equivalents), the use of which has no place in decision making. On this point I wholeheartedly agree.