Sam Watson’s journal round-up for Monday 20th August 2018

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.

A Randomized Trial of Epinephrine in Out-of-Hospital Cardiac Arrest. New England Journal of Medicine. Published July 2018.

Adrenaline (epinephrine) is often administered to patients in cardiac arrest in order to increase blood flow and improve heart rhythm. However, there had been some concern about the potential adverse effects of using adrenaline, and a placebo controlled trial was called for. This article presents the findings of this trial. While there is little economics in this article, it is an interesting example of what I believe to be erroneous causal thinking, especially in the way it was reported in the media. For example, The Guardian‘s headline was,

Routine treatment for cardiac arrest doubles risk of brain damage – study

while The Telegraph went for the even more inflammatory

Cardiac arrest resuscitation drug has needlessly brain-damaged thousands

But what did the study itself say about their findings:

the use of epinephrine during resuscitation for out-of-hospital cardiac arrest resulted in a significantly higher rate of survival at 30 days than the use of placebo. […] although the rate of survival was slightly better, the trial did not show evidence of a between-group difference in the rate of survival with a favorable neurologic outcome. This result was explained by a higher proportion of patients who survived with severe neurologic disability in the epinephrine group.

Clearly, a slightly more nuanced view, but nevertheless it leaves room for the implication that the adrenaline is causing the neurological damage. Indeed the authors go on to say that “the use of epinephrine did not improve neurologic outcome.” But a counterfactual view of causation should lead us to ask what would have happened to those who survived with brain damage had they not been given adrenaline.

We have a competing risks set up: (A) survival with favourable neurologic outcome, (B) survival with neurologic impairment, and (C) death. The proportion of patients with outcome (A) was slightly higher in the adrenaline group (although not statistically significant so apparently no effect eyes roll), the proportion of patients with outcome (B) was a lot higher in the adrenaline group, and the proportion of patients with outcome (C) was lower in the adrenaline group. This all suggests to me that the adrenaline caused patients who would have otherwise died to mostly survive with brain damage, and a few to survive impairment free, not that adrenaline caused those who would have otherwise been fine to have brain damage. So the question in response to the above quotes is then, is death a preferable neurologic outcome to brain damage? As trite as this may sound, it is a key health economics question – how do we value these health states?

Incentivizing Safer Sexual Behavior: Evidence from a Lottery Experiment on HIV Prevention. American Economic Review: Applied Economics. [RePEcPublished July 2018. 

This article presents a randomised trial testing an interesting idea. People who are at high risk of HIV and other sexually transmitted infections (STIs) and often those who engage in riskier sexual behaviour. A basic decision theoretic conception would be that those individuals don’t consider the costs to be high enough relative to the benefits (although there is clearly some divide between this explanation and how people actually think in terms of risky sexual behaviour, much like any other seemingly irrational behaviour). Conditional cash transfers can change the balance of the decision to incentivise people to act differently, what this study looks at is using a conditional lottery with the chance of high winnings instead, since this should be more attractive still to risk-seeking individuals. While the trial was designed to reduce HIV prevalance, entry into the lottery in the treatment arm was conditional on being free of two curable STIs at each round – this enabled people who fail to be eligible again, and also allowed the entry of HIV-positive individuals whose sexual behaviour is perhaps the most important to reducing HIV transmission. The lottery arm of the trial was found to have 20% lower incidence over the study period compared to the control arm – quite impressive. However, the cost-effectiveness of the program was estimated to be $882 per HIV infection averted on the basis of lottery payments alone, and around $3,300 per case averted all in. This seems quite high to me. Despite a plethora of non-comparable outcomes in cost-effectiveness studies of HIV public health interventions other studies have reported costs per cases averted an order of magnitude lower than this. The conclusions seems to be then that the idea works well – it’s just too costly to be of much use.

Monitoring equity in universal health coverage with essential services for neglected tropical diseases: an analysis of data reported for five diseases in 123 countries over 9 years. The Lancet: Global Health. [PubMedPublished July 2018. 

Universal health coverage (UHC) is one the key parts of Sustainable Development Goal (SDG) 3, good health and well-being. The text of the SDG identifies UHC as being about access to services – but this word “access” in the context of health care is often vague and nebulous. Many people mistakenly treat access to health services as synonymous with use of health services, but having access to something is not dependent on whether you actually use it or not. Barriers to a person’s ability to use health care for a given complaint are numerous: financial cost, time cost, lack of education, language barrier, and so forth. It is therefore difficult to quantify and measure access. Hogan and co-authors proposed an index to quantify and monitor UHC across the world that was derived from a number of proxies such as women with four or more antenatal visits, children with vaccines, blood pressure, and health worker density. Their work is useful but of course flawed – these proxies all capture something different, either access, use, or health outcomes – and it is unclear that they are all sensitive to the same underlying construct. Needless to say, we should still be able to diagnose access issues from some combination of these data. This article extends the work of Hogan et al to look at neglected tropical diseases, which affect over 1.5 billion, yet which are, obviously, neglected. The paper uses ‘preventative chemotherapy coverage’ as its key measure, which is the proportion of those needed the chemotherapy who actually receive it. This is a measure of use and not access (although they should be related), for example, there may be near universal availability of the chemotherapy, but various factors on the demand side limiting use. Needless to say, the measure should still be a useful diagnostic tool and it is interesting to see how much worse countries perform on this metric for neglected tropical diseases than general health care.

 

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Transformative treatments: a big methodological challenge for health economics

Social scientists, especially economists, are concerned with causal inference: understanding whether and how an event causes a certain effect. Typically, we subscribe to the view that causal relations are reducible to sets of counterfactuals, and we use ever more sophisticated methods, such as instrumental variables and propensity score matching, to estimate these counterfactuals. Under the right set of assumptions, like that unobserved differences between study subjects are time invariant or that a treatment causes its effect through a certain mechanism, we can derive estimators for average treatment effects. All uncontroversial stuff indeed.

A recent paper from L.A. Paul and Kieran Healy introduces an argument of potential importance to how we can interpret studies investigating causal relations. In particular, they make the argument that we don’t know if individual preferences persist in a study through treatment. It is in general not possible to distinguish between the case where a treatment has satisfied an underlying revealed preference, or transformed an individual’s preferences. If preferences are changed or transformed, rather than revealed, then they are, in effect, a different population and in a causal inference type study, no longer comparable to the control population.

To quote their thought experiment:

Vampires: In the 21st century, vampires begin to populate North America. Psychologists decide to study the implications this could have for the human population. They put out a call for undergraduates to participate in a randomized controlled experiment, and recruit a local vampire with scientific interests. After securing the necessary permissions, they randomize and divide their population of undergraduates into a control group and a treatment group. At t1, members of each group are given standard psychological assessments measuring their preferences about vampires in general and about becoming a vampire in particular. Then members of the experimental group are bitten by the lab vampire.

Members of both groups are left to go about their daily lives for a period of time. At t2, they are assessed. Members of the control population do not report any difference in their preferences at t2. All members of the treated population, on the other hand, report living richer lives, enjoying rewarding new sensory experiences, and having a new sense of meaning at t2. As a result, they now uniformly report very strong pro-vampire preferences. (Some members of the treatment group also expressed pro-vampire preferences before the experiment, but these were a distinct minority.) In exit interviews, all treated subjects also testify that they have no desire to return to their previous condition.

Should our psychologists conclude that being bitten by a vampire somehow satisfies people’s underlying, previously unrecognized, preferences to become vampires? No. They should conclude that being bitten by a vampire causes you to become a vampire (and thus, to prefer being one). Being bitten by a vampire and then being satisfied with the result does not satisfy or reveal your underlying preference to be a vampire. Being bitten by a vampire transforms you: it changes your preferences in a deep and fundamental way, by replacing your underlying human preferences with vampire preferences, no matter what your previous preferences were.

In our latest journal round-up, I featured a paper that used German reunification in 1989 as a natural experiment to explore the impact of novel food items in the market on consumption and weight gain. The transformative treatments argument comes into play here. Did reunification reveal the preferences of East Germans for the novel food stuffs, or did it change their preferences for foodstuffs overall due to the significant cultural change? If the latter case is true then West Germans do not constitute an appropriate control group. The causal mechanism at play is also important to the development of policy: for example, without reunification there may not have been any impact from novel food products.

This argument is also sometimes skirted around with regards to the valuing of health states. Should it be the preferences of healthy people, or the experienced utility of sick people, that determine health state values? Do physical trauma and disease reveal our underlying preferences for different health states, or do they transform us to have different preferences entirely? Any study looking at the effect of disease on health status or quality of life could not distinguish between the two. Yet the two cases are akin to using the same or different groups of people to do the valuation of health states.

Consider also something like estimating the impact of retirement on health and quality of life. If self-reported quality of life is observed to improve in one of these studies, we don’t know if that is because retirement has satisfied a pre-existing preference for the retired lifestyle, or retirement has transformed a person’s preferences. In the latter case, the appropriate control group to evaluate the causal effect of retirement is not non-retired persons.

Paul and Healy do not make their argument to try to prevent or undermine research in the social sciences, they interpret their conclusion as a “methodological challenge”. The full implications of the above arguments have not been explored but could be potentially great and new innovations in methodology to estimate average causal effects could be warranted. How this may be achieved, I’ll have to admit, I do not know.

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