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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Weekend effect explainer: why we are not the ‘climate change deniers of healthcare’

The statistics underlying the arguments around the weekend effect are complicated. Despite over a hundred empirical studies on the topic, and an observed increase in the risk of mortality for weekend admissions in multiple countries, there is still no real consensus on what is going on. We have previously covered the arguments on this blog and suggested that the best explanation for the weekend effect is that healthier patients are less likely to be admitted to hospital at the weekend. Nevertheless, a little knowledge can be a dangerous thing (the motto of the Dunning-Kruger effect), and some people can be very confident about the interpretation of the statistics despite their complicated nature. For example, one consultant nephrologist wrote in a comment on a recent article that those who attribute the weekend effect to differences in admission are becoming ‘the climate change deniers of healthcare’ as they are not taking into account all the risk-adjusted analyses!

It may certainly be the case that there is a reduction in healthcare quality at the weekend. But it is also important for policy makers to understand that it is still possible to observe a weekend effect even with quite comprehensive mortality risk adjustment. The image below links to an app that simulates multiple weekend effect studies from a model where there is no weekend effect but potentially different chances of admission at the weekend and on weekdays. We are assuming that those who turn up to A&E but are not admitted and sent home are the healthiest patients. In the app, you can change the parameters: the proportion of attendances that are admitted on weekends and weekdays, the mortality rate among patients who are admitted, and, crucially, the amount of variation in patient mortality explained (a sort of “R-squared”) by our risk adjustment. It will display crude and adjusted odds ratios as well as a distribution of possible results from similar studies. (Be patient though, simulating lots of large studies seems to take a while on the server!).

weekendeffect

As is evident, even with a very high proportion of variance explained, we can still get an odds ratio not equal to one and an observed weekend effect if the proportion of attendances who are admitted differs between weekend and weekday. And, with the very large sample sizes often used for these studies, these will likely appear “statistically significant“. Recent evidence from the UK has suggested that 27% of A&E attendances are admitted at the weekend compared to 30% on a weekday. Even when we can explain 90% of the variation in mortality, we can still get a ‘weekend effect’ with these small differences in propensity for admission. And, if there is any element of publication bias, or the ‘garden of forking paths’ [PDF] we will see lots of statistically significant weekend effect studies published.

When there is a misunderstanding about statistics, one often blames the audience for not understanding, but it is often the case that an idea has just not been explained well enough. I can’t judge whether little web apps will actually help explain concepts like this, but hopefully it’s a step in the right direction.

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