Meeting round-up: Health Economists’ Study Group (HESG) Winter 2019

2019 started with aplomb with the HESG Winter meeting, superbly organised by the Centre for Health Economics, University of York.

Andrew Jones kicked off proceedings with his brilliant course on data visualisation in health econometrics. The eager audience learnt about Edward Tufte’s and others’ ideas about how to create charts that help to make it much easier to understand information. The course was tremendously well received by the HESG audience. And I know that I’ll find it incredibly useful too, as there were lots of ideas that apply to my work. So I’m definitely going to be looking further into Andrew’s chapter on data visualisation to know more.

The conference proper started in the afternoon. I had the pleasure to chair the fascinating paper by Manuela Deidda et al on an economic evaluation using observational data on the Healthy Start Voucher, which was discussed by Anne Ludbrook. We had an engaging discussion, that not only delved into the technical aspects of the paper, such as the intricacies of implementing propensity score matching and regression discontinuity, but also about the policy implications of the results.

I continued with the observational data theme by enjoying the discussion led by Panos Kasteridis on the Andrew McCarthy et al paper. Then I quickly followed this by popping over to catch Attakrit Leckcivilize’s excellent discussion of Padraig Dixon’s et al paper on the effect of obesity on hospital costs. This impressive paper uses Mendelian randomisation, which is a fascinating approach using a type of instrumental variable analysis with individuals’ genetic variants as the instrument.

The meeting continued in the stunning setting of the Yorkshire Museum for the plenary session, which also proved a fitting location to pay tribute to the inspirational Alan Maynard, who sadly passed away in 2018. Unfortunately, I was unable to hear the tributes to Alan Maynard in person, but fellow attendees were able to paint a moving portrait of the event on Twitter, that kept me in touch.

The plenary was chaired by Karen Bloor and included presentations by Kalipso Chalkidou, Brian Ferguson, Becky Henderson and Danny PalnochJane Hall, Steve Birch and Maria Goddard gave personal tributes.

The health economics community was united in gratitude to Professor Alan Maynard, who did so much to advance and disseminate the discipline. It made for a wonderful way to finish day 1!

Day 2 started bright and was full of stimulating sessions to choose from.

I chose to zone in on the cost-effectiveness topic in particular. I started with the David Glynn et al paper about using “back of the envelope” calculations to inform funding and research decisions, discussed by Ed Wilson. This paper is an excellent step towards making value of information easy to use.

I then attended Matthew Quaife’s discussion of Matthew Taylor’s paper on the consequences of assuming independence of parameters to decision uncertainty. This is a relevant paper for the cost-effectiveness world, in particular for those tasked with building and appraising cost-effectiveness models.

Next up it was my turn in the hot seat, as I presented the Jose Robles-Zurita et al paper on the economic evaluation of diagnostic tests. This thought-provoking paper presents a method to account for the effect of accuracy on the uptake of the test, in the context of maximising health.

As always, we were spoilt for choice in the afternoon. The paper “Drop dead: is anchoring at ‘dead’ a theoretical requirement in health state valuation” by Chris Sampson et al, competed very strongly with “Is it really ‘Grim up North’? The causes and consequences of inequalities on health and wider outcomes” by Anna Wilding et al, for the most provocative title. “Predicting the unpredictable? Using discrete choice experiments in economic evaluation to characterise uncertainty and account for heterogeneity”, from Matthew Quaife et al, also gave them a run for their money! I’ll leave a sample here of the exciting papers in discussion, so you can make your own mind up:

Dinner was in the splendid Merchant Adventurers’ Hall. Built in 1357, it is one of the finest Medieval buildings in the UK. Another stunning setting that provided a beautiful backdrop for a wonderful evening!

Andrew Jones presented the ‘Health Economics’ PhD Poster Prize, sponsored by Health Economics Wiley. Rose Atkins took the top honours by winning the Wiley prize for best poster. With Ashleigh Kernohan’s poster being highly commended, given its brilliant use of technology. Congratulations both!

Unfortunately, the vagaries of public transport meant I had to go home straight after dinner, but I heard from many trustworthy sources, on the following day, that the party continued well into the early hours. Clearly, health economics is a most energising topic!

For me, day 3 was all about cost-effectiveness decision rules. I started with the paper by Mark Sculpher et al, discussed by Chris Sampson. This remarkable paper sums up the evidence on the marginal productivity of the NHS, discussing how to use it to inform decisions, and proposes an agenda for research. There were many questions and comments from the floor, showing how important and challenging this topic is. As are so many papers in HESG, this is clearly one to look out for when it appears in print!

The next paper was on a very different way to solve the problem of resource allocation in health care. Philip Clarke and Paul Frijters propose an interesting system of auctions to set prices. The paper was well discussed by James Lomas, which kick-started an animated discussion with the audience about practicalities and implications for investment decisions by drug companies. Great food for thought!

Last, but definitely not least, I took in the paper by Bernarda Zamora et al on the relationship between health outcomes and expenditure across geographical areas in England. David Glynn did a great job discussing the paper, and especially in explaining data envelopment analysis. As ever, the audience was highly engaged and put forward many questions and comments. Clearly, the productivity of the NHS is a central question for health economics and will keep us busy for some time to come.

As always, this was a fantastic HESG meeting that was superbly organised, providing an environment where authors, discussants and participants alike were able to excel.

I really felt a feeling of collegiality, warmth and energy permeate the event. We are part of such an amazing scientific community. Next stop, HESG Summer meeting, hosted by the University of East Anglia. I’m already looking forward to it!


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.


Sam Watson’s journal round-up for 12th 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.

Estimating health opportunity costs in low-income and middle-income countries: a novel approach and evidence from cross-country data. BMJ Global Health. Published November 2017.

The relationship between health care expenditure and population health outcomes is a topic that comes up often on this blog. Understanding how population health changes in response to increases or decreases in the health system budget is a reasonable way to set a cost-effectiveness threshold. Purchasing things above this threshold will, on average, displace activity with greater benefits. But identifying this effect is hard. Commonly papers use some kind of instrumental variable method to try to get at the causal effect with aggregate, say country-level, data. These instruments, though, can be controversial. Years ago I tried to articulate why I thought using socio-economic variables as instruments was inappropriate. I also wrote a short paper a few years ago, which remains unpublished, that used international commodity price indexes as an instrument for health spending in Sub-Saharan Africa, where commodity exports are a big driver of national income. This was rejected from a journal because of the choice of instruments. Commodity prices may well influence other things in the country that can influence population health. And a similar critique could be made of this article here, which uses consumption:investment ratios and military expenditure in neighbouring countries as instruments for national health expenditure in low and middle income countries.

I remain unconvinced by these instruments. The paper doesn’t present validity checks on them, which is forgiveable given medical journal word limitations, but does mean it is hard to assess. In any case, consumption:investment ratios change in line with the general macroeconomy – in an economic downturn this should change (assuming savings = investment) as people switch from consumption to investment. There are a multitude of pathways through which this will affect health. Similarly, neighbouring military expenditure would act by displacing own-country health expenditure towards military expenditure. But for many regions of the world, there has been little conflict between neighbours in recent years. And at the very least there would be a lag on this effect. Indeed, in all the models of health expenditure and population health outcomes I’ve seen, barely a handful take into account dynamic effects.

Now, I don’t mean to let the perfect be the enemy of the good. I would never have suggested this paper should not be published as it is, at the very least, important for the discussion of health care expenditure and cost-effectiveness. But I don’t feel there is strong enough evidence to accept these as causal estimates. I would even be willing to go as far to say that any mechanism that affects health care expenditure is likely to affect population health by some other means, since health expenditure is typically decided in the context of the broader public sector budget. That’s without considering what happens with private expenditure on health.

Strategic Patient Discharge: The Case of Long-Term Care Hospitals. American Economic Review. [RePEcPublished November 2018.

An important contribution of health economics has been to undermine people’s trust that doctors act in their best interest. Perhaps that’s a little facetious, nevertheless there has been ample demonstration that health care providers will often act in their own self-interest. Often this is due to trying to maximise revenue by gaming reimbursement schemes, but also includes things like doctors acting differently near the end of their shift so they can go home on time. So when I describe a particular reimbursement scheme that Medicare in the US uses, I don’t think there’ll be any doubt about the results of this study of it.

In the US, long-term acute care hospitals (LTCHs) specialise in treating patients with chronic care needs who require extended inpatient stays. Medicare reimbursement typically works on a fixed rate for each of many diagnostic related groups (DRGs), but given the longer and more complex care needs in LTCHs, they get a higher tariff. To discourage admitting patients purely to get higher levels of reimbursement, the bulk of the payment only kicks in after a certain length of stay. Like I said – you can guess what happened.

This article shows 26% of patients are discharged in the three days after the length of stay threshold compared to just 7% in the three days prior. This pattern is most strongly observed in discharges to home, and is not present in patients who die. But this may still be just by chance that the threshold and these discharges coincide. Fortunately for the authors the thresholds differ between DRGs and even move around within a DRG over time in a way that appears unrelated to actual patient health. They therefore estimate a set of decision models for patient discharge to try to estimate the effect of different reimbursement policies.

Estimating misreporting in condom use and its determinants among sex workers: Evidence from the list randomisation method. Health Economics. Published November 2018.

Working on health and health care research, especially if you conduct surveys, means you often want to ask people about sensitive topics. These could include sex and sexuality, bodily function, mood, or other ailments. For example, I work a fair bit on sanitation, where frequently self-reported diarrhoea in under fives (reported by the mother that is) is the primary outcome. This could be poorly reported particularly if an intervention includes any kind of educational component that suggests it could be the mother’s fault for, say, not washing her hands, if the child gets diarrhoea. This article looks at condom use among female sex workers in Senegal, another potentially sensitive topic, since unprotected sex is seen as risky. To try and get at the true prevalence of condom use, the authors use a ‘list randomisation’ method. This randomises survey participants to two sets of questions: a set of non-sensitive statements, or the same set of statements with the sensitive question thrown in. All respondents have to do is report the number of the statements they agree with. This means it is generally not possible to distinguish the response to the sensitive question, but the difference in average number of statements reported between the two groups gives an unbiased estimator for the population proportion. Neat, huh? Ultimately the authors report an estimate of 80% of sex workers using condoms, which compares to the 97% who said they used a condom when asked directly.