Chris Sampson’s journal round-up for 11th June 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.

End-of-life healthcare expenditure: testing economic explanations using a discrete choice experiment. Journal of Health Economics Published 7th June 2018

People incur a lot of health care costs at the end of life, despite the fact that – by definition – they aren’t going to get much value from it (so long as we’re using QALYs, anyway). In a 2007 paper, Gary Becker and colleagues put forward a theory for the high value of life and high expenditure on health care at the end of life. This article sets out to test a set of hypotheses derived from this theory, namely: i) higher willingness-to-pay (WTP) for health care with proximity to death, ii) higher WTP with greater chance of survival, iii) societal WTP exceeds individual WTP due to altruism, and iv) societal WTP may exceed individual WTP due to an aversion to restricting access to new end-of-life care. A further set of hypotheses relating to the ‘pain of risk-bearing’ is also tested. The authors conducted an online discrete choice experiment (DCE) with 1,529 Swiss residents, which asked respondents to suppose that they had terminal cancer and was designed to elicit WTP for a life-prolonging novel cancer drug. Attributes in the DCE included survival, quality of life, and ‘hope’ (chance of being cured). Individual WTP – using out-of-pocket costs – and societal WTP – based on social health insurance – were both estimated. The overall finding is that the hypotheses are on the whole true, at least in part. But the fact is that different people have different preferences – the authors note that “preferences with regard to end-of-life treatment are very heterogeneous”. The findings provide evidence to explain the prevailing high level of expenditure in end of life (cancer) care. But the questions remain of what we can or should do about it, if anything.

Valuation of preference-based measures: can existing preference data be used to generate better estimates? Health and Quality of Life Outcomes [PubMed] Published 5th June 2018

The EuroQol website lists EQ-5D-3L valuation studies for 27 countries. As the EQ-5D-5L comes into use, we’re going to see a lot of new valuation studies in the pipeline. But what if we could use data from one country’s valuation to inform another’s? The idea is that a valuation study in one country may be able to ‘borrow strength’ from another country’s valuation data. The author of this article has developed a Bayesian non-parametric model to achieve this and has previously applied it to UK and US EQ-5D valuations. But what about situations in which few data are available in the country of interest, and where the country’s cultural characteristics are substantially different. This study reports on an analysis to generate an SF-6D value set for Hong Kong, firstly using the Hong Kong values only, and secondly using the UK value set as a prior. As expected, the model which uses the UK data provided better predictions. And some of the differences in the valuation of health states are quite substantial (i.e. more than 0.1). Clearly, this could be a useful methodology, especially for small countries. But more research is needed into the implications of adopting the approach more widely.

Can a smoking ban save your heart? Health Economics [PubMed] Published 4th June 2018

Here we have another Swiss study, relating to the country’s public-place smoking bans. Exposure to tobacco smoke can have an acute and rapid impact on health to the extent that we would expect an immediate reduction in the risk of acute myocardial infarction (AMI) if a smoking ban reduces the number of people exposed. Studies have already looked at this effect, and found it to be large, but mostly with simple pre-/post- designs that don’t consider important confounding factors or prevailing trends. This study tests the hypothesis in a quasi-experimental setting, taking advantage of the fact that the 26 Swiss cantons implemented smoking bans at different times between 2007 and 2010. The authors analyse individual-level data from Swiss hospitals, estimating the impact of the smoking ban on AMI incidence, with area and time fixed effects, area-specific time trends, and unemployment. The findings show a large and robust effect of the smoking ban(s) for men, with a reduction in AMI incidence of about 11%. For women, the effect is weaker, with an average reduction of around 2%. The evidence also shows that men in low-education regions experienced the greatest benefit. What makes this an especially nice paper is that the authors bring in other data sources to help explain their findings. Panel survey data are used to demonstrate that non-smokers are likely to be the group benefitting most from smoking bans and that people working in public places and people with less education are most exposed to environmental tobacco smoke. These findings might not be generalisable to other settings. Other countries implemented more gradual policy changes and Switzerland had a particularly high baseline smoking rate. But the findings suggest that smoking bans are associated with population health benefits (and the associated cost savings) and could also help tackle health inequalities.

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Chris Sampson’s journal round-up for 7th May 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.

Building an international health economics teaching network. Health Economics [PubMedPublished 2nd May 2018

The teaching on my health economics MSc (at Sheffield) was very effective. Experts from our subdiscipline equipped me with the skills that I went on to use on a daily basis in my first job, and to this day. But not everyone gets the same opportunity. And there were only 8 people on my course. Part of the background to the new movement described in this editorial is the observation that demand for health economists outstrips supply. Great for us jobbing health economists, but suboptimal for society. The shortfall has given rise to people teaching health economics (or rather, economic evaluation methods) without any real training in economics. The main purpose of this editorial is to call on health economists (that’s me and you) to pull our weight and contribute to a collective effort to share, improve, and ultimately deliver high-quality teaching resources. The Health Economics education website, which is now being adopted by iHEA, should be the starting point. And there’s now a Teaching Health Economics Special Interest Group. So chip in! This paper got me thinking about how the blog could play its part in contributing to the infrastructure of health economics teaching, so expect to see some developments on that front.

Including future consumption and production in economic evaluation of interventions that save life-years: commentary. PharmacoEconomics – Open [PubMed] Published 30th April 2018

When people live longer, they spend their extra life-years consuming and producing. How much consuming and producing they do affects social welfare. The authors of this commentary are very clear about the point they wish to make, so I’ll just quote them: “All else equal, a given number of quality-adjusted life-years (QALYs) from life prolongation will normally be more costly from a societal perspective than the same number of QALYs from programmes that improve quality of life”. This is because (in high-income countries) most people whose life can be extended are elderly, so they’re not very productive. They’re likely to create a net cost for society (given how we measure value). Asserting that the cost is ‘worth it’ at any level, or simply ignoring the matter, isn’t really good enough because providing life extension will be at the expense of some life-improving treatments which may – were these costs taken into account – improve social welfare. The authors’ estimates suggest that the societal cost of life-extension is far greater than current methods admit. Consumption costs and production gains should be estimated and should be given some weight in decision-making. The question is not whether we should measure consumption costs and production gains – clearly, we should. The question is what weight they ought to be given in decision-making.

Methods for the economic evaluation of changes to the organisation and delivery of health services: principal challenges and recommendations. Health Economics, Policy and Law [PubMedPublished 20th April 2018

The late, great, Alan Maynard liked to speak about redisorganisations in the NHS: large-scale changes to the way services are organised and delivered, usually without a supporting evidence base. This problem extends to smaller-scale service delivery interventions. There’s no requirement for policy-makers to demonstrate that changes will be cost-effective. This paper explains why applying methods of health technology assessment to service interventions can be tricky. The causal chain of effects may be less clear when interventions are applied at the organisational level rather than individual level, and the results will be heavily dependent on the present context. The author outlines five challenges in conducting economic evaluations for service interventions: i) conducting ex-ante evaluations, ii) evaluating impact in terms of QALYs, iii) assessing costs and opportunity costs, iv) accounting for spillover effects, and v) generalisability. Those identified as most limiting right now are the challenges associated with estimating costs and QALYs. Cost data aren’t likely to be readily available at the individual level and may not be easily identifiable and divisible. So top-down programme-level costs may be all we have to work with, and they may lack precision. QALYs may be ‘attached’ to service interventions by applying a tariff to individual patients or by supplementing the analysis with simulation modelling. But more methodological development is still needed. And until we figure it out, health spending is likely to suffer from allocative inefficiencies.

Vog: using volcanic eruptions to estimate the health costs of particulates. The Economic Journal [RePEc] Published 12th April 2018

As sources of random shocks to a system go, a volcanic eruption is pretty good. A major policy concern around the world – particularly in big cities – is the impact of pollution. But the short-term impact of particulate pollution is difficult to identify because there is high correlation amongst pollutants. In this study, the authors use the eruption activity of Kīlauea on the island of Hawaiʻi as a source of variation in particulate pollution. Vog – volcanic smog – includes sulphur dioxide and is similar to particulate pollution in cities, but the fact that Hawaiʻi does not have the same levels of industrial pollutants means that the authors can more cleanly identify the impact on health outcomes. In 2008 there was a big increase in Kīlauea’s emissions when a new vent opened, and the level of emissions fluctuates daily, so there’s plenty of variation to play with. The authors have two main sources of data: emergency admissions (and their associated charges) and air quality data. A parsimonious OLS model is used to estimate the impact of air quality on the total number of admissions for a given day in a given region, with fixed effects for region and date. An instrumental variable approach is also used, which looks at air quality on a neighbouring island and uses wind direction to specify the instrumental variable. The authors find that pulmonary-related emergency admissions increased with pollution levels. Looking at the instrumental variable analysis, a one standard deviation increase in particulate pollution results in 23-36% more pulmonary-related emergency visits (depending on which measure of particulate pollution is being used). Importantly, there’s no impact on fractures, which we wouldn’t expect to be influenced by the particulate pollution. The impact is greatest for babies and young children. And it’s worth bearing in mind that avoidance behaviours – e.g. people staying indoors on ‘voggy’ days – are likely to reduce the impact of the pollution. Despite the apparent lack of similarity between Hawaiʻi and – for example – London, this study provides strong evidence that policy-makers should consider the potential savings to the health service when tackling particulate pollution.

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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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