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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Sam Watson’s journal round-up for 30th 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.

The Millennium Villages Project: a retrospective, observational, endline evaluation. The Lancet Global Health [PubMedPublished May 2018

There are some clinical researchers who would have you believe observational studies are completely useless. The clinical trial is king, they might say, observation studies are just too biased. And while it’s true that observational studies are difficult to do well and convincingly, they can be a reliable and powerful source of evidence. Similarly, randomised trials are frequently flawed, for example there’s often missing data that hasn’t been dealt with, or a lack of allocation concealment, and many researchers forget that randomisation does not guarantee a balance of covariates, it merely increases the probability of it. I bring this up, as this study is a particularly carefully designed observational data study that I think serves as a good example to other researchers. The paper is an evaluation of the Millennium Villages Project, an integrated intervention program designed to help rural villages across sub-Saharan Africa meet the Millennium Development Goals over ten years between 2005 and 2015. Initial before-after evaluations of the project were criticised for inferring causal “impacts” from before and after data (for example, this Lancet paper had to be corrected after some criticism). To address these concerns, this new paper is incredibly careful about choosing appropriate control villages against which to evaluate the intervention. Their method is too long to summarise here, but in essence they match intervention villages to other villages on the basis of district, agroecological zone, and a range of variables from the DHS – matches were they reviewed for face validity and revised until a satisfactory matching was complete. The wide range of outcomes are all scaled to a standard normal and made to “point” in the same direction, i.e. so an increase indicated economic development. Then, to avoid multiple comparisons problems, a Bayesian hierarchical model is used to pool data across countries and outcomes. Costs data were also reported. Even better, “statistical significance” is barely mentioned at all! All in all, a neat and convincing evaluation.

Reconsidering the income‐health relationship using distributional regression. Health Economics [PubMed] [RePEcPublished 19th April 2018

The relationship between health and income has long been of interest to health economists. But it is a complex relationship. Increases in income may change consumption behaviours and a change in the use of time, promoting health, while improvements to health may lead to increases in income. Similarly, people who are more likely to make higher incomes may also be those who look after themselves, or maybe not. Disentangling these various factors has generated a pretty sizeable literature, but almost all of the empirical papers in this area (and indeed all empirical papers in general) use modelling techniques to estimate the effect of something on the expected value, i.e. mean, of some outcome. But the rest of the distribution is of interest – the mean effect of income may not be very large, but a small increase in income for poorer individuals may have a relatively large effect on the risk of very poor health. This article looks at the relationship between income and the conditional distribution of health using something called “structured additive distribution regression” (SADR). My interpretation of SADR is that, one would model the outcome y ~ g(a,b) as being distributed according to some distribution g(.) indexed by parameters a and b, for example, a normal or Gamma distribution has two parameters. One would then specify a generalised linear model for a and b, e.g. a = f(X’B). I’m not sure this is a completely novel method, as people use the approach to, for example, model heteroscedasticity. But that’s not to detract from the paper itself. The findings are very interesting – increases to income have a much greater effect on health at the lower end of the spectrum.

Ask your doctor whether this product is right for you: a Bayesian joint model for patient drug requests and physician prescriptions. Journal of the Royal Statistical Society: Series C Published April 2018.

When I used to take econometrics tutorials for undergraduates, one of the sessions involved going through coursework about the role of advertising. To set the scene, I would talk about the work of Alfred Marshall, the influential economist from the late 1800s/early 1900s. He described two roles for advertising: constructive and combative. The former is when advertising grows the market as a whole, increasing everyone’s revenues, and the latter is when ads just steal market share from rivals without changing the size of the market. Later economists would go on to thoroughly develop theories around advertising, exploring such things as the power of ads to distort preferences, the supply of ads and their complementarity with the product they’re selling, or seeing ads as a source of consumer information. Nevertheless, Marshall’s distinction is still a key consideration, although often phrased in different terms. This study examines a lot of things, but one of its key objectives is to explore the role of direct to consumer advertising on prescriptions of brands of drugs. The system is clearly complex: drug companies advertise both to consumers and physicians, consumers may request the drug from the physician, and the physician may or may not prescribe it. Further, there may be correlated unobservable differences between physicians and patients, and the choice to advertise to particular patients may not be exogenous. The paper does a pretty good job of dealing with each of these issues, but it is dense and took me a couple of reads to work out what was going on, especially with the mix of Bayesian and Frequentist terms. Examining the erectile dysfunction drug market, the authors reckon that direct to consumer advertising reduces drug requests across the category, while increasing the proportion of requests for the advertised drug – potentially suggesting a “combative” role. However, it’s more complex than that patient requests and doctor’s prescriptions seem to be influenced by a multitude of factors.

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IVF and the evaluation of policies that don’t affect particular persons

Over at the CLAHRC West Midlands blog, Richard Lilford (my boss, I should hasten to add!) writes about the difficulties with the economic evaluation of IVF. The post notes that there are a number of issues that “are not generally considered in the standard canon for health economic assessment” including the problems with measuring benefits, choosing an appropriate discount rate, indirect beneficiaries, and valuing the life of the as yet unborn child. Au contraire! These issues are the very bread and butter of health economics and economic evaluation research. But I would concede that their impact on estimates of cost-effectiveness are not nearly well enough integrated into standard assessments.

We’ve covered the issue of choosing a social discount rate on this blog before with regards to treatments with inter-generational effects. I want instead to consider the last point about how we should, in the most normative of senses, consider the life of the child born as a result of IVF.

It puts me in mind of the work of the late, great Derek Parfit. He could be said to have single-handedly developed the field of ethics about future people. He identified a number of ethical problems that still often don’t have satisfactory answers. Decisions like funding IVF have an impact on the very existence of persons. But these decisions do not affect the well-being or rights of any particular persons, rather, as Parfit terms them, general persons. Few would deny that we have moral obligations not to cause material harm to future generations. Most would reject the narrow view that the only relevant outcomes are those that affect actual, particular persons, the narrow person-centred view. For example, in considering the problem of global warming, we do not reject its consequences on future generations as being irrelevant. But there remains the question about how we morally treat these general, future persons. Parfit calls this the non-identity problem and it applies neatly to the issue of IVF.

To illustrate the problem of IVF consider the choice:

If we choose A Adam and Barbara will not have children Charles will not exist
If we choose B Adam and Barbara will have a child Charles will live to 70

If we ignore evidence that suggests quality of life actually declines after one has children, we will assume that Adam and Barbara having children will in fact raise their quality of life since they are fulfilling their preferences. It would then seem to be clear that the fact of Charles existing and living a healthy life would be better than him not existing at all and the net benefit of Choice B is greater. But then consider the next choice:

If we choose A Adam and Barbara will not have children Charles will not exist Dianne will not exist
If we choose B Adam and Barbara will have a child Charles will live to 70 Dianne will not exist
If we choose C Adam and Barbara will have children Charles will live to 40 Dianne will live to 40

Now, Choice C would still seem to be preferable to Choice B if all life years have the same quality of life. But we could continue adding children with shorter and shorter life expectancies until we have a large population that lives a very short life, which is certainly not a morally superior position. This is a version of Parfit’s repugnant conclusion, in which general utilitarian principles leads us to prefer a situation with a very large, very low quality of life population to a smaller, better off one. No satisfying solution has yet been proposed. For IVF this might imply increasing the probability of multiple births!

We can also consider the “opposite” of IVF, contraception. In providing contraception we are superficially choosing Choice A above, which by the same utilitarian reasoning would be a worse situation than one in which those children are born. However, contraception is often used to be able to delay fertility decisions, so the choice actually becomes between a child being born earlier and living a worse life than a child being born later in better circumstances. So for a couple, things would go worse for the general person who is their first child, if things are worse for the particular person who is actually their first child. So it clearly matters how we frame the question as well.

We have a choice about how to weigh up the different situations if we reject the ‘narrow person-centred view’. On a no difference view, the effects on general and particular persons are weighted the same. On a two-tier view, the effects on general persons only matter a fraction of those on particular persons. For IVF this relates to how we weight Charles’s (and Diane’s) life in an evaluation. But current practice is ambiguous about how we weigh up these lives, and if we have a ‘two-tier view’, how we weight the lives of general persons.

From an economic perspective, we often consider that the values we place on benefits resulting from decisions as being determined by societal preferences. Generally, we ignore the fact that for many treatments the actual beneficiaries do not yet exist, which would suggest a ‘no difference view’. For example, when assessing the benefits of providing a treatment for childhood leukaemia, we don’t value the benefits to those particular children who have the disease differently to those general persons who may have the disease in the future. Perhaps we do not consider this since the provision of the treatment does not cause a difference in who will exist in the future. But equally when assessing the effects of interventions that may cause, in a counterfactual sense, changes in fertility decisions and the existence of persons, like social welfare payments or a lifesaving treatment for a woman of childbearing age, we do not think about the effects on the general persons that may be a child of that person or household. This would then suggest a ‘narrow person-centred view’.

There is clearly some inconsistency in how we treat general persons. For IVF evaluations, in particular, many avoid this question altogether and just estimate the cost per successful pregnancy, leaving the weighing up of benefits to later decision makers. While the arguments clearly don’t point to a particular conclusion, my tentative conclusion would be a ‘no difference view’. At any rate, it is an open question. In my rare lectures, I often remark that we spend a lot more time on empirical questions than questions of normative economics. This example shows how this can result in inconsistencies in how we choose to analyse and report our findings.

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