Sam Watson’s journal round up for 16th October 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.

Effect of forced displacement on health. Journal of the Royal Statistical Society: Series A [RePEcPublished October 2017

History, as they say, is doomed to repeat itself. Despite repeated cries of ‘never again’, war and conflict continue to harm and displace people around the world. The mass displacement of the Rohingya in Myanmar is leading to the formation of the world’s largest refugee camp in Bangladesh. Aside from the obvious harm from conflict itself, displacement is likely to have pernicious effects on health. Livelihoods and well-being is lost as well as access to basic amenities and facilities. The conflict in Croatia and wider Eastern Europe created a mass displacement of people, however, many went to relatively wealthy neighbouring countries across Europe. Thus, the health effects of displacement in this conflict should provide an understanding of the lower bound of what happens. This paper looks into this question using a health survey of Croatians from 2003. An empirical issue the authors spend a substantial amount of time addressing is that displacement status is likely to be endogenous: beyond choices about protecting their household and possessions, health and the ability to travel may play a large role in decisions to move. The mortality rate from conflict is used as an instrument for displacement, being a proxy for the intensity of war. However, conflict intensity is obviously likely to have a effect itself on health status. A method of relaxing the exclusion restriction is used, which tempers the estimates somewhat. Nevertheless, there is evidence that displacement impacts upon hypertension, self-assessed health, and emotional and physical dimensions of the SF-36. However, it seems to me that there may be another empirical issue not dealt with – the sample selection problem. While the number of casualties was low relative to the size of the population and numbers of displaced people, those who died obviously don’t feature in the sample. And those who died may have also been more likely or not to be displaced and be in worse health. Maybe only a bias of second order but a point it seems is left unconsidered.

Can variation in subgroups’ average treatment effects explain treatment effect heterogeneity? Evidence from a social experiment. Review of Economics and Statistics [RePEcPublished October 2017

A common approach to explore treatment effect heterogeneity is to estimate mean impacts by subgroups. In applied health economics studies I have most often seen this done by pooling data and adding interactions of the treatment with subgroups of interest to a regression model. For example, there is a large interest in differences in access to care across socioeconomic groups – in the UK we often use quintiles, or other division, of the Index of Multiple Deprivation, which is estimated at small area level, to look at this. However, this paper looks at the question of whether this approach to estimating heterogeneity is any good. Using data from a large jobs treatment program, they compare estimates of quantile treatment effects, which are considered to fully capture treatment effect heterogeneity, to results from various specifications of models that assume constant treatment effects within subgroups. If they found there was little difference in the two methods, I doubt the paper would have been published in such a good journal, so it’s no surprise that their conclusions are that the subgroup models perform poorly. Even allowing for more flexibility, such as by allowing effects to vary over time, and adding submodels for a point mass at zero, they still don’t do that well. Interestingly, subgroups defined according to different variables, e.g. education or pre-treatment earnings, fare differently – so comparisons across types of subgroups is important when the analyst is looking at heterogeneity. The takeaway message though is that constant effects subgroups models aren’t that good – more flexible semi or nonparametric methods may be preferred.

The hidden costs of terrorism: The effects on health at birth. Journal of Health Economics [PubMedPublished October 2017

We here at the blog have covered a long series of papers on the effects of in utero stressors on birth and later life health and economic outcomes. The so-called fetal-origins hypothesis posits that the nine months in the womb are some of the most important in predicting later life health outcomes. This may be one of the main mechanisms explaining intergenerational transmission of health. Some of these previous studies have covered reduced maternal nutrition, exposure to conditions of famine, or unemployment shocks in the household. This study examines the effect of the mother being pregnant in a province in Spain during which a terrorist attack by ETA occurred. At first glance, one might be forgiven for being sceptical at first, given (i) terrorist attacks were rare, (ii) the chances of actually being affected by an attack in a province if an attack occurred is low, so (iii) the chances are that the effect of feeling stressed on birth weight is small and likely to be swamped by a multitude of other factors (see all the literature we’ve covered on the topic!) All credit to the authors for doing a thorough job of trying to address all these concerns, but I’ll admit I remain sceptical. The effect sizes are very small indeed, as we suspected, and unfortunately there is not enough evidence to examine whether those women who had low birth weight live births were stressed or demonstrating adverse health behaviours.

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Ambulance and economics

I have recently been watching the BBC series AmbulanceIt is a fly-on-the-wall documentary following the West Midlands Ambulance Service interspersed with candid interviews with ambulance staff, much in the same vein as other health care documentaries like 24 Hours in A&EAs much as anything it provides a (stylised) look at the conditions on the ground for staff and illustrates how health care institutions are as much social institutions as essential services. In a recent episode, the cost of a hoax call was noted as some thousands of pounds. Indeed, the media and health services often talk about the cost of hoax calls in this way:

Warning for parents as one hoax call costs public £2,465 and diverts ambulance from real emergency call.

Frequent 999 callers cost NHS millions of pounds a year.

Nuisance caller cost the taxpayer £78,000 by making 408 calls to the ambulance service in two years.

But these are accounting costs, not the full economic cost. The first headline almost captures this by suggesting the opportunity cost was attendance at a real emergency call. However, given the way that ambulance resources are deployed and triaged across calls, it is very difficult to say what the opportunity cost is: what would be the marginal benefit of having an additional ambulance crew for the duration of a hoax call? What is the shadow price of an ambulance unit?

Few studies have looked at this question. The widely discussed study by Claxton et al. in the UK, looked at shadow prices of health care across different types of care, but noted that:

Expenditure on, for example, community care, A&E, ambulance services, and outpatients can be difficult to attribute to a particular [program budget category].

One review identified a small number of studies examining the cost-benefit and cost-effectiveness of emergency response services. Estimates of the marginal cost per life saved ranged from approximately $5,000 to $50,000. However, this doesn’t really tell us the impact of an additional crew, nor were many of these studies comparable in terms of the types of services they looked at, and these were all US-based.

There does exist the appropriately titled paper Ambulance EconomicsThis paper approaches the question we’re interested in, in the following way:

The centrepiece of our analysis is what we call the Ambulance Response Curve (ARC). This shows the relationship between the response time for an individual call (r) and the number of ambulances available and not in use (n) at the time the call was made. For example, let us suppose that 35 ambulances are on duty and 10 of them are being used. Then n has the value of 25 when the next call is taken. Ceteris paribus, as increases, we expect that r will fall.

On this basis, one can look at how an additional ambulance affects response times, on average. One might then be able to extrapolate the health effects of that delay. This paper suggests that an additional ambulance would reduce response times by around nine seconds on average for the service they looked at – not actually very much. However, the data are 20 years old, and significant changes to demand and supply over that period are likely to have a large effect on the ARC. Nevertheless, changes in response time of the order of minutes are required in order to have a clinically significant impact on survival, which are unlikely to occur with one additional ambulance.

Taken altogether, the opportunity cost of a hoax call is not likely to be large. This is not to downplay the stupidity of such calls, but it is perhaps reassuring that lives are not likely to be in the balance and is a testament to the ability of the service to appropriately deploy their limited resources.

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Journal Club Briefing: Dolan and Kahneman (2008)

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

Why this paper?

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

Summary

Experienced vs. decision utility

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

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

Adaptation

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

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

Health valuation and adaptation

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

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

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

Public vs. patient preference

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

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

Discussion

Which yardstick?

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

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

Public vs. private preferences revisited

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

Intertemporal welfare

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

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

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

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

The person is you.

Do you care?

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

Should I let you?

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