Chris Sampson’s journal round-up for 2nd December 2019

from The Academic Health Economists’ Blo… at http://bit.ly/35SIl3G on December 2, 2019 at 12:00PM

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 treatment decision under uncertainty: the effects of health, wealth and the probability of death. Journal of Health Economics Published 16th November 2019

It’s important to understand how people make decisions about treatment. At the end of life, the question can become a matter of whether to have treatment or to let things take their course such that you end up dead. In order to consider this scenario, the author of this paper introduces the probability of death to some existing theoretical models of decision-making under uncertainty.

The diagnostic risk model and the therapeutic risk model can be used to identify risk thresholds that determine decisions about treatment. The diagnostic model relates to the probability that disease is present and the therapeutic model relates to the probability that treatment is successful. The new model described in this paper builds on these models to consider the impact on the decision thresholds of i) initial health state, ii) probability of death, and iii) wealth. The model includes wealth after death, in the form of a bequest. Limited versions of the model are also considered, excluding the bequest and excluding wealth (described as a ‘QALY model’). Both an individual perspective and an aggregate perspective are considered by excluding and including the monetary cost of diagnosis and treatment, to allow for a social insurance type setting.

The comparative statics show a lot of ambiguity, but there are a few things that the model can tell us. The author identifies treatment as having an ‘insurance effect’, by reducing diagnostic risk, a ‘protective effect’, by lowering the probability of death, and a risk-increasing effect associated with therapeutic risk. A higher probability of death increases the propensity for treatment in both the no-bequest model and the QALY model, because of the protective effect of treatment. In the bequest model, the impact is ambiguous, because treatment costs reduce the bequest. In the full model, wealthier individuals will choose to undergo treatment at a lower probability of success because of a higher marginal utility for survival, but the effect becomes ambiguous if the marginal utility of wealth depends on health (which it obviously does).

I am no theoretician, so it can take me a long time to figure these things out in my head. For now, I’m not convinced that it is meaningful to consider death in this way using a one-period life model. In my view, the very definition of death is a loss of time, which plays little or no part in this model. But I think my main bugbear is the idea that anybody’s decision about life saving treatment is partly determined by the amount of money they will leave behind. I find this hard to believe. The author links the finding that a higher probability of death increases treatment propensity to NICE’s end of life premium. Though I’m not convinced that the model has anything to do with NICE’s reasoning on this matter.

Moving toward evidence-based policy: the value of randomization for program and policy implementation. JAMA [PubMed] Published 15th November 2019

Evidence-based policy is a nice idea. We should figure out whether something works before rolling it out. But decision-makers (especially politicians) tend not to think in this way, because doing something is usually seen to be better than doing nothing. The authors of this paper argue that randomisation is the key to understanding whether a particular policy creates value.

Without evidence based on random allocation, it’s difficult to know whether a policy works. This, the authors argue, can undermine the success of effective interventions and allow harmful policies to persist. A variety of positive examples are provided from US healthcare, including trials of Medicare bundled payments. Apparently, such trials increased confidence in the programmes’ effects in a way that post hoc evaluations cannot, though no evidence of this increased confidence is actually provided. Policy evaluation is not always easy, so the authors describe four preconditions for the success of such studies: i) early engagement with policymakers, ii) willingness from policy leaders to support randomisation, iii) timing the evaluation in line with policymakers’ objectives, and iv) designing the evaluation in line with the realities of policy implementation.

These are sensible suggestions, but it is not clear why the authors focus on randomisation. The paper doesn’t do what it says on the tin, i.e. describe the value of randomisation. Rather, it explains the value of pre-specified policy evaluations. Randomisation may or may not deserve special treatment compared with other analytical tools, but this paper provides no explanation for why it should. The authors also suggest that people are becoming more comfortable with randomisation, as large companies employ experimental methods, particularly on the Internet with A/B testing. I think this perception is way off and that most people feel creeped out knowing that the likes of Facebook are experimenting on them without any informed consent. In the authors’ view, it being possible to randomise is a sufficient basis on which to randomise. But, considering the ethics, as well as possible methodological contraindications, it isn’t clear that randomisation should become the default.

A new tool for creating personal and social EQ-5D-5L value sets, including valuing ‘dead’. Social Science & Medicine Published 30th November 2019

Nobody can agree on the best methods for health state valuation. Or, at least, some people have disagreed loud enough to make it seem that way. Novel approaches to health state valuation are therefore welcome. Even more welcome is the development and testing of methods that you can try at home.

This paper describes the PAPRIKA method (Potentially All Pairwise RanKings of all possible Alternatives) of discrete choice experiment, implemented using 1000Minds software. Participants are presented with two health states that are defined in terms of just two dimensions, each lasting for 10 years, and asked to choose between them. Using the magical power of computers, an adaptive process identifies further choices, automatically ranking states using transitivity so that people don’t need to complete unnecessary tasks. In order to identify where ‘dead’ sits on the scale, a binary search procedure asks participants to compare EQ-5D states with being dead. What’s especially cool about this process is that everybody who completes it is able to view their own personal value set. These personal value sets can then be averaged to identify a social value set.

The authors used their tool to develop an EQ-5D-5L value set for New Zealand (which is where the researchers are based). They recruited 5,112 people in an online panel, such that the sample was representative of the general public. Participants answered 20 DCE questions each, on average, and almost half of them said that they found the questions difficult to answer. The NZ value set showed that anxiety/depression was associated with the greatest disutility, though each dimension has a notably similar level of impact at each level. The value set correlates well with numerous existing value sets.

The main limitation of this research seems to be that only levels 1, 3, and 5 of each EQ-5D-5L domain were included. Including levels 2 and 4 would more than double the number of questions that would need to be answered. It is also concerning that more than half of the sample was excluded due to low data quality. But the authors do a pretty good job of convincing us that this is for the best. Adaptive designs of this kind could be the future of health state valuation, especially if they can be implemented online, at low cost. I expect we’ll be seeing plenty more from PAPRIKA.

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