Chris Sampson’s journal round-up for 23rd December 2019

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 Internet and children’s psychological wellbeing. Journal of Health Economics Published 13th December 2019

Here at the blog, we like the Internet. We couldn’t exist without it. We vie for your attention along with all of the other content factories (or “friends”). But there’s a well-established sense that people – especially children – should moderate their consumption of Internet content. The Internet is pervasive and is now a fundamental part of our day-to-day lives, not simply an information source to which we turn when we need it. Almost all 12-15 year olds in the UK use the Internet. The ubiquity of the Internet makes it difficult to test its effects. But this paper has a good go at it.

This study is based on the idea that broadband speeds are a good proxy for Internet use. In England, a variety of public and private sector initiatives have resulted in a distorted market with quasi-random assigment of broadband speeds. The authors provide a very thorough explanation of children’s wellbeing in relation to the Internet, outlining a range of potential mechanisms.

The analysis combines data from the UK’s pre-eminent household panel survey (Understanding Society) with broadband speed data published by the UK regulator Ofcom. Six wellbeing outcomes are analysed from children’s self-reported responses. The questions ask children how they feel about their lives – measured on a seven-point scale – in relation to school work, appearance, family, friends, school attended, and life as a whole. An unbalanced panel of 6,310 children from 2012-2017 provides 13,938 observations from 3,765 different Lower Layer Super Output Areas (LSOA), with average broadband speeds for each LSOA for each year. Each of the six wellbeing outcomes is modelled with child-, neighbourhood- and time-specific fixed effects. The models’ covariates include a variety of indicators relating to the child, their parents, their household, and their local area.

A variety of models are tested, and the overall finding is that higher broadband speeds are negatively associated with all of the six wellbeing indicators. Wellbeing in relation to appearance shows the strongest effect; a 1% increase in broadband speed reduces happiness with appearance by around 0.6%. The authors explore a variety of potential mechanisms by running pairs of models between broadband speeds and the mechanism and between the mechanism and the outcomes. A key finding is that the data seem to support the ‘crowding out’ hypothesis. Higher broadband speeds are associated with children spending less time on activities such as sports, clubs, and real world social interactions, and these activities are in turn positively associated with wellbeing. The authors also consider different subgroups, finding that the effects are more detrimental for girls.

Where the paper falls down is that it doesn’t do anything to convince us that broadband speeds represent a good proxy for Internet use. It’s also not clear exactly what the proxy is meant to be for – use (e.g. time spent on the Internet) or access (i.e. having the option to use the Internet) – though the authors seem to be interested in the former. If that’s the case, the logic of the proxy is not obvious. If I want to do X on the Internet then higher speeds will enable me to do it in less time, in which case the proxy would capture the inverse of the desired indicator. The other problem I think we have is in the use of self-reported measures in this context. A key supposed mechanism for the effect is through ‘social comparison theory’, which we might reasonably expect to influence the way children respond to questions as well as – or instead of – their underlying wellbeing.

One-way sensitivity analysis for probabilistic cost-effectiveness analysis: conditional expected incremental net benefit. PharmacoEconomics [PubMed] Published 16th December 2019

Here we have one of those very citable papers that clearly specifies a part of cost-effectiveness analysis methodology. A better title for this paper could be Make one-way sensitivity analysis great again. The authors start out by – quite rightly – bashing the tornado diagram, mostly on the basis that it does not intuitively characterise the information that a decision-maker needs. Instead, the authors propose an approach to probabilistic one-way sensitivity analysis (POSA) that is a kind of simplified version of EVPPI (expected value of partially perfect information) analysis. Crucially, this approach does not assume that the various parameters of the analysis are independent.

The key quantity created by this analysis is the conditional expected incremental net monetary benefit (cINMB), conditional, that is, on the value of the parameter of interest. There are three steps to creating a plot of the POSA results: 1) rank the costs and outcomes for the sampled values of the parameter – say from the first to the last centile; 2) plug in a cost-effectiveness threshold value to calculate the cINMB at each sampled value; and 3) record the probability of observing each value of the parameter. You could use this information to present a tornado-style diagram, plotting the credible range of the cINMB. But it’s more useful to plot a line graph showing the cINMB at the different values of the parameter of interest, taking into account the probability that the values will actually be observed.

The authors illustrate their method using three different parameters from a previously published cost-effectiveness analysis, in each case simulating 15,000 Monte Carlo ‘inner loops’ for each of the 99 centiles. It took me a little while to get my head around the results that are presented, so there’s still some work to do around explaining the visuals to decision-makers. Nevertheless, this approach has the potential to become standard practice.

A head-on ordinal comparison of the composite time trade-off and the better-than-dead method. Value in Health Published 19th December 2019

For years now, methodologists have been trying to find a reliable way to value health states ‘worse than dead’. The EQ-VT protocol, used to value the EQ-5D-5L, includes the composite time trade-off (cTTO). The cTTO task gives people the opportunity to trade away life years in good health to avoid having to subsequently live in a state that they have identified as being ‘worse than dead’ (i.e. they would prefer to die immediately than to live in it). An alternative approach to this is the better-than-dead method, whereby people simply compare given durations in a health state to being dead. But are these two approaches measuring the same thing? This study sought to find out.

The authors recruited a convenience sample of 200 students and asked them to value seven different EQ-5D-5L health states that were close to zero in the Dutch tariff. Each respondent completed both a cTTO task and a better-than-dead task (the order varied) for each of the seven states. The analysis then looked at the extent to which there was agreement between the two methods in terms of whether states were identified as being better or worse than dead. Agreement was measured using counts and using polychoric correlations. Unsurprisingly, agreement was higher for those states that lay further from zero in the Dutch tariff. Around zero, there was quite a bit of disagreement – only 65% agreed for state 44343. Both approaches performed similarly with respect to consistency and test-retest reliability. Overall, the authors interpret these findings as meaning that the two methods are measuring the same underlying preferences.

I don’t find that very convincing. States were more often identified as worse than dead in the better-than-dead task, with 55% valued as such, compared with 37% in the cTTO. That seems like a big difference. The authors provide a variety of possible explanations for the differences, mostly relating to the way the tasks are framed. Or it might be that the complexity of the worse-than-dead task in the cTTO is so confusing and counterintuitive that respondents (intentionally or otherwise) avoid having to do it. For me, the findings reinforce the futility of trying to value health states in relation to being dead. If a slight change in methodology prevents a group of biomedical students from giving consistent assessments of whether or not a state is worse than being dead, what hope do we have?

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Chris Sampson’s journal round-up for 19th November 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.

Valuation of health states considered to be worse than death—an analysis of composite time trade-off data from 5 EQ-5D-5L valuation studies. Value in Health Published 12th November 2018

I have a problem with the idea of health states being ‘worse than dead’, and I’ve banged on about it on this blog. Happily, this new article provides an opportunity for me to continue my campaign. Health state valuation methods estimate how much a person prefers being in a more healthy state. Positive values are easy to understand; 1.0 is twice as good as 0.5. But how about the negative values? Is -1.0 twice as bad as -0.5? How much worse than being dead is that? The purpose of this study is to evaluate whether or not negative EQ-5D-5L values meaningfully discriminate between different health states.

The study uses data from EQ-5D-5L valuation studies conducted in Singapore, the Netherlands, China, Thailand, and Canada. Altogether, more than 5000 people provided valuations of 10 states each. As a simple measure of severity, the authors summed the number of steps from full health in all domains, giving a value from 0 (11111) to 20 (55555). We’d expect this measure of severity of states to correlate strongly with the mean utility values derived from the composite time trade-off (TTO) exercise.

Taking Singapore as an example, the mean of positive values (states better than dead) decreased from 0.89 to 0.21 with increasing severity, which is reassuring. The mean of negative values, on the other hand, ranged from -0.98 to -0.89. Negative values were clustered between -0.5 and -1.0. Results were similar across the other countries. In all except Thailand, observed negative values were indistinguishable from random noise. There was no decreasing trend in mean utility values as severity increased for states worse than dead. A linear mixed model with participant-specific intercepts and an ANOVA model confirmed the findings.

What this means is that we can’t say much about states worse than dead except that they are worse than dead. How much worse doesn’t relate to severity, which is worrying if we’re using these values in trade-offs against states better than dead. Mostly, the authors frame this lack of discriminative ability as a practical problem, rather than anything more fundamental. The discussion section provides some interesting speculation, but my favourite part of the paper is an analogy, which I’ll be quoting in future: “it might be worse to be lost at sea in deep waters than in a pond, but not in any way that truly matters”. Dead is dead is dead.

Determining value in health technology assessment: stay the course or tack away? PharmacoEconomics [PubMed] Published 9th November 2018

The cost-per-QALY approach to value in health care is no stranger to assault. The majority of criticisms are ill-founded special pleading, but, sometimes, reasonable tweaks and alternatives have been proposed. The aim of this paper was to bring together a supergroup of health economists to review and discuss these reasonable alternatives. Specifically, the questions they sought to address were: i) what should health technology assessment achieve, and ii) what should be the approach to value-based pricing?

The paper provides an unstructured overview of a selection of possible adjustments or alternatives to the cost-per-QALY method. We’re very briefly introduced to QALY weighting, efficiency frontiers, and multi-criteria decision analysis. The authors don’t tell us why we ought (or ought not) to adopt these alternatives. I was hoping that the paper would provide tentative answers to the normative questions posed, but it doesn’t do that. It doesn’t even outline the thought processes required to answer them.

The purpose of this paper seems to be to argue that alternative approaches aren’t sufficiently developed to replace the cost-per-QALY approach. But it’s hardly a strong defence. I’m a big fan of the cost-per-QALY as a necessary (if not sufficient) part of decision making in health care, and I agree with the authors that the alternatives are lacking in support. But the lack of conviction in this paper scares me. It’s tempting to make a comparison between the EU and the QALY.

How can we evaluate the cost-effectiveness of health system strengthening? A typology and illustrations. Social Science & Medicine [PubMed] Published 3rd November 2018

Health care is more than the sum of its parts. This is particularly evident in low- and middle-income countries that might lack strong health systems and which therefore can’t benefit from a new intervention in the way a strong system could. Thus, there is value in health system strengthening. But, as the authors of this paper point out, this value can be difficult to identify. The purpose of this study is to provide new methods to model the impact of health system strengthening in order to support investment decisions in this context.

The authors introduce standard cost-effectiveness analysis and economies of scope as relevant pieces of the puzzle. In essence, this paper is trying to marry the two. An intervention is more likely to be cost-effective if it helps to provide economies of scope, either by making use of an underused platform or providing a new platform that would improve the cost-effectiveness of other interventions. The authors provide a typology with three types of health system strengthening: i) investing in platform efficiency, ii) investing in platform capacity, and iii) investing in new platforms. Examples are provided for each. Simple mathematical approaches to evaluating these are described, using scaling factors and disaggregated cost and outcome constraints. Numerical demonstrations show how these approaches can reveal differences in cost-effectiveness that arise through changes in technical efficiency or the opportunity cost linked to health system strengthening.

This paper is written with international development investment decisions in mind, and in particular the challenge of investments that can mostly be characterised as health system strengthening. But it’s easy to see how many – perhaps all – health services are interdependent. If anything, the broader impact of new interventions on health systems should be considered as standard. The methods described in this paper provide a useful framework to tackle these issues, with food for thought for anybody engaged in cost-effectiveness analysis.

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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.

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