Chris Sampson’s journal round-up for 23rd April 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.

What should we know about the person behind a TTO? The European Journal of Health Economics [PubMed] Published 18th April 2018

The time trade-off (TTO) is a staple of health state valuation. Ask someone to value a health state with respect to time and – hey presto! – you have QALYs. This editorial suggests that completing a TTO can be a difficult task for respondents and that, more importantly, individuals’ characteristics may determine the way that they respond and therefore the nature of the results. One of the most commonly demonstrated differences, in this respect, is the fact that valuations of people’s own health states tend to be higher than health states valued hypothetically. But this paper focuses on indirect (hypothetical) valuations. The authors highlight mixed evidence for the influence of age, gender, marital status, having children, education, income, expectations about the future, and of one’s own health state. But why should we try and find out more about respondents when conducting TTOs? The authors offer 3 reasons: i) to inform sampling, ii) to inform the design and standardisation of TTO exercises, and iii) to inform the analysis. I agree – we need to better understand these sources of heterogeneity. Not to over-engineer responses, but to aid our interpretation, even if we want societally-representative valuations that include all of these variations in response behaviour. TTO valuation studies should collect data relating to the individual respondents. Unfortunately, what those data should be aren’t listed in this study, so the research question in the title isn’t really answered. But maybe that’s something the authors have in hand.

Computer modeling of diabetes and its transparency: a report on the eighth Mount Hood Challenge. Value in Health Published 9th April 2018

The Mount Hood Challenge is a get-together for people working on the (economic) modelling of diabetes. The subject of the 2016 meeting was transparency, with two specific goals: i) to evaluate the transparency of two published studies, and ii) to develop a diabetes-specific checklist for transparent reporting of modelling studies. Participants were tasked (in advance of the meeting) with replicating the two published studies and using the replicated models to evaluate some pre-specified scenarios. Both of the studies had some serious shortcomings in the reporting of the necessary data for replication, including the baseline characteristics of the population. Five modelling groups replicated the first model and seven groups replicated the second model. Naturally, the different groups made different assumptions about what should be used in place of missing data. For the first paper, none of the models provided results that matched the original. Not even close. And the differences between the results of the replications – in terms of costs incurred and complications avoided – were huge. The performance was a bit better on the second paper, but hardly worth celebrating. In general, the findings were fear-confirming. Informed by these findings, the Diabetes Modeling Input Checklist was created, designed to complement existing checklists with more general applications. It includes specific data requirements for the reporting of modelling studies, relating to the simulation cohort, treatments, costs, utilities, and model characteristics. If you’re doing some modelling in diabetes, you should have this paper to hand.

Setting dead at zero: applying scale properties to the QALY model. Medical Decision Making [PubMed] Published 9th April 2018

In health state valuation, whether or not a state is considered ‘worse than dead’ is heavily dependent on methodological choices. This paper reviews the literature to answer two questions: i) what are the reasons for anchoring at dead=0, and ii) how does the position of ‘dead’ on the utility-scale impact on decision making? The authors took a standard systematic approach to identify literature from databases, with 7 papers included. Then the authors discuss scale properties and the idea that there are interval scales (such as temperature) and ratio scales (such as distance). The difference between these is the meaningfulness of the reference point (or origin). This means that you can talk about distance doubling, but you can’t talk about temperature doubling, because 0 metres is not arbitrary, whereas 0 degrees Celsius is. The paper summarises some of the arguments put forward for using dead=0. They aren’t compelling. The authors argue that the duration part of the QALY (i.e. time) needs to have ratio properties for the QALY model to function. Time obviously holds this property and it’s clear that duration can be anchored at zero. The authors then demonstrate that, for the QALY model to work, the health-utility scale must also exhibit ratio scale properties. The basis for this is the assumption that zero duration nullifies health states and that ‘dead’ nullifies duration. But the paper doesn’t challenge the conceptual basis for using dead in health state valuation exercises. Rather, it considers the mathematical properties that must hold to allow for dead=0, and asserts them. The authors’ conclusion that dead “needs to have the value of 0 in a QALY model” is correct, but only within the existing restrictions and assumptions underlying current practice. Nevertheless, this is a very useful study for understanding the challenge of anchoring and explicating the assumptions underlying the QALY model.


What does a health value of zero mean?

Submission from David Parkin

There’s a problem with the way that health economists and others describe the properties that a health state index should have.  The main reason we want such an index is to calculate Quality Adjusted Life Years.  So, we need the index’s possible values to run from zero to one, though we can also tolerate negative values.  But what does zero mean in this context?

There aren’t too many problems with saying what we mean by a health state that has a value of 1.  It’s described using terms like ‘full health’.  That’s interpreted to mean that one year spent in ‘full health’ will generate one Quality Adjusted Life Year.  1 QALY is as much health as any one person can have in a year.  There’s room for debate about what ‘full health’ means, in particular its subjective interpretation, but this is a detail about a coherent concept based on the idea of what a QALY is.

But it’s much more difficult to define what the value 0 means.  Health economics texts often define it as ‘dead’ or even ‘death’.  This is then followed by an explanation of what negative values mean, leading to the concept of ‘worse than dead’ or ‘worse than death’.  I think that this definition is wrong.  It’s misleading and may bias the results of health state valuation studies.

The most usual applications of the QALY model don’t aim to compare health states among the living with ‘being dead’.  They compare different health states amongst the living.  If 0 and 1 are intended to be health state values, they should be defined with respect to health states.  ‘Full health’ is a health state, but ‘being dead’ is not, except perhaps to zombies and vampires.  In fact, if you rate a dead person in EQ-5D health state terms, they will be a 33311.  Unable to do anything, but in no pain and not anxious or depressed.

Of course, it’s important that ‘dead’ is valued at 0.  ‘Full health’ must be valued at 1, because an essential QALY property is that every year of life spent in full health produces 1 QALY.  Similarly, ‘being dead’ must be valued at 0 because another essential QALY property is that dead people produce no QALYs.  But are there are other ways of producing no QALYs?

No QALYs are produced if there are no life years, but being dead is not even the only way to achieve that.  It can also be achieved by not being alive in the first place.  More importantly, a living person will also enjoy no QALYs in any year that they spend in health states that are valued at 0.  In the same way that the health state value 1 implies as much health that we can have at a given time, the health state value 0 implies a complete absence of health at a given time.  But what does that mean? They are not the worst health states that people can have, since to some people those worse states generate negative values.  So, what do health states with a value of 0 look like?

One way to solve this problem is to observe that they are health states that are as bad as being dead.  That gives us a way of thinking about them and of describing them to people for valuation studies.  This has an additional advantage.  It is more consistent with the idea of negative health values than is the idea that being dead defines zero. Negative values refer to levels of healthiness not deadness. It’s OK to describe health states with negative values as being worse than dead.  But that isn’t their essential feature, which is that they generate negative QALY values over time.  Negative numbers mean a very bad health state, not an extremely dead state.

Health state valuation studies in general use the term ‘dead’ for comparison with health states, explicitly or implicitly meaning 0.  But when being asked to value health states, can people really imagine ‘being dead’ in any meaningful way?  Some studies also use an even worse term, ‘death’.  The only route to being dead is to die.  But dead, dying and death are not the same things.  Dead is a state, though not a health state, but dying is a process and death an event.  Death and dying may be valued as negative in welfare terms even if dead is correctly valued at 0.  Using death as a comparison when valuing health states is likely to distort the values obtained.

Of course, many people recognise the issue that I’ve raised, but presumably they think that it is not important.  I think that it’s worth researching whether or not it matters empirically.  If not, I guess it’s a dead issue.