Brendan Collins’s journal round-up for 18th March 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.

Evaluation of intervention impact on health inequality for resource allocation. Medical Decision Making [PubMed] Published 28th February 2019

How should decision-makers factor equity impacts into economic decisions? Can we trade off an intervention’s cost-effectiveness with its impact on unfair health inequalities? Is a QALY just a QALY or should we weight it more if it is gained by someone from a disadvantaged group? Can we assume that, because people of lower socioeconomic position lose more QALYs through ill health, that most interventions should, by default, reduce inequalities?

I really like the health equity plane. This is where you show health impacts (usually including a summary measure of cost-effectiveness like net health benefit or net monetary benefit) and equity impacts (which might be a change in slope index of inequality [SII] or relative index of inequality) on the same plane. This enables decision-makers to identify potential trade-offs between interventions that produce a greater benefit, but have less impact on inequalities, and those that produce a smaller benefit, but increase equity. I think there has been a debate over whether the ‘win-win’ quadrant should be south-east (which would be consistent with the dominant quadrant of the cost-effectiveness plane) or north-east, which is what seems to have been adopted as the consensus and is used here.

This paper showcases a reproducible method to estimate the equity impact of interventions. It considers public health interventions recommended by NICE from 2006-2016, with equity impacts estimated based on whether they targeted specific diseases, risk factors or populations. The disease distributions were based on hospital episode statistics data by deprivation (IMD). The study used equity weights to convert QALYs gained to different social groups into net social welfare. In this case, valuing the most disadvantaged fifth of people’s health at around 6-7 times that of the least disadvantaged fifth. I think there might still be work to be done around reaching consensus for equity weights.

The total expected effect on inequalities is small – full implementation of all recommendations would produce a reduction of the quality-adjusted life expectancy gap between the healthiest and least healthy from 13.78 to 13.34 QALYs. But maybe this is to be expected; NICE does not typically look at vaccinations or screening and has not looked at large scale public health programmes like the Healthy Child Programme in the whole. Reassuringly, where recommended interventions were likely to increase inequality, the trade-off between efficiency and equity was within the social welfare function they had used. The increase in inequality might be acceptable because the interventions were cost-effective – producing 5.6million QALYs while increasing the SII by 0.005. If these interventions are buying health at a good price, then you would hope this might then release money for other interventions that would reduce inequalities.

I suspect that public health folks might not like equity trade-offs at all – trading off equity and cost-effectiveness might be the moral equivalent of trading off human rights – you can’t choose between them. But the reality is that these kinds of trade-offs do happen, and like a lot of economic methods, it is about revealing these implicit trade-offs so that they become explicit, and having ‘accountability for reasonableness‘.

Future unrelated medical costs need to be considered in cost effectiveness analysis. The European Journal of Health Economics [PubMed] [RePEc] Published February 2019

This editorial says that NICE should include unrelated future medical costs in its decision making. At the moment, if NICE looks at a cardiovascular disease (CVD) drug, it might look at future costs related to CVD but it won’t include changes in future costs of cancer, or dementia, which may occur because individuals live longer. But usually unrelated QALY gains will be implicitly included; so there is an inconsistency. If you are a health economic modeller, you know that including unrelated costs properly is technically difficult. You might weight average population costs by disease prevalence so you get a cost estimate for people with coronary heart disease, diabetes, and people without either disease. Or you might have a general healthcare running cost that you can apply to future years. But accounting for a full matrix of competing causes of morbidity and mortality is very tricky if not impossible. To help with this, this group of authors produced the excellent PAID tool, which helps with doing this for the Netherlands (can we have one for the UK please?).

To me, including unrelated future costs means that in some cases ICERs might be driven more by the ratio of future costs to QALYs gained. Whereas currently, ICERs are often driven by the ratio of the intervention costs to QALYs gained. So it might be that a lot of treatments that are currently cost-effective no longer are, or we need to judge all interventions with a higher ICER willingness to pay threshold or value of a QALY. The authors suggest that, although including unrelated medical costs usually pushes up the ICER, it should ultimately result in better decisions that increase health.

There are real ethical issues here. I worry that including future unrelated costs might be used for an integrated care agenda in the NHS, moving towards a capitation system where the total healthcare spend on any one individual is capped, which I don’t necessarily think should happen in a health insurance system. Future developments around big data mean we will be able to segment the population a lot better and estimate who will benefit from treatments. But I think if someone is unlucky enough to need a lot of healthcare spending, maybe they should have it. This is risk sharing and, without it, you may get the ‘double jeopardy‘ problem.

For health economic modellers and decision-makers, a compromise might be to present analyses with related and unrelated medical costs and to consider both for investment decisions.

Overview of cost-effectiveness analysis. JAMA [PubMed] Published 11th March 2019

This paper probably won’t offer anything new to academic health economists in terms of methods, but I think it might be a useful teaching resource. It gives an interesting example of a model of ovarian cancer screening in the US that was published in February 2018. There has been a large-scale trial of ovarian cancer screening in the UK (the UKCTOCS), which has been extended because the results have been promising but mortality reductions were not statistically significant. The model gives a central ICER estimate of $106,187/QALY (based on $100 per screen) which would probably not be considered cost-effective in the UK.

I would like to explore one statement that I found particularly interesting, around the willingness to pay threshold; “This willingness to pay is often represented by the largest ICER among all the interventions that were adopted before current resources were exhausted, because adoption of any new intervention would require removal of an existing intervention to free up resources.”

The Culyer bookshelf model is similar to this, although as well as the ICER you also need to consider the burden of disease or size of the investment. Displacing a $110,000/QALY intervention for 1000 people with a $109,000/QALY intervention for a million people will bust your budget.

This idea works intuitively – if Liverpool FC are signing a new player then I might hope they are better than all of the other players, or at least better than the average player. But actually, as long as they are better than the worst player then the team will be improved (leaving aside issues around different positions, how they play together, etc.).

However, I think that saying that the reference ICER should be the largest current ICER might be a bit dangerous. Leaving aside inefficient legacy interventions (like unnecessary tonsillectomies etc), it is likely that the intervention being considered for investment and the current maximum ICER intervention to be displaced may both be new, expensive immunotherapies. It might be last in, first out. But I can’t see this happening; people are loss averse, so decision-makers and patients might not accept what is seen as a fantastic new drug for pancreatic cancer being approved then quickly usurped by a fantastic new leukaemia drug.

There has been a lot of debate around what the threshold should be in the UK; in England NICE currently use £20,000 – £30,000, up to a hypothetical maximum £300,000/QALY in very specific circumstances. UK Treasury value QALYs at £60,000. Work by Karl Claxton and colleagues suggests that marginal productivity (the ‘shadow price’) in the NHS is nearer to £5,000 – £15,000 per QALY.

I don’t know what the answer to this is. I don’t think the willingness-to-pay threshold for a new treatment should be the maximum ICER of a current portfolio of interventions; maybe it should be the marginal health production cost in a health system, as might be inferred from the Claxton work. Of course, investment decisions are made on other factors, like impact on health inequalities, not just on the ICER.

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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 11th July 2016

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.

GPs’ implicit prioritization through clinical choices – evidence from three national health services. Journal of Health Economics [RePEcPublished 7th July 2016

Through economic evaluation we inform high-level prioritisation decisions about (for example) which drugs should and should not be available. Meanwhile, GPs are able to prioritise at the individual level through their prescribing behaviour. But do they prioritise? And in what ways? This study reports on a discrete choice experiment carried out with 907 GPs in England, Scotland and Norway to try and elicit prescription behaviour in different decision making contexts. A key aspect that the study considers is the presence of a double agency problem, whereby the GP advocates both maximum patient benefit (‘patient agency’) and cost containment for society (‘social agency’). GPs were asked a generic question about prescribing either ‘Medicine A’ or ‘Medicine B’ and the DCE included 5 attributes: total costs, effect, patient costs, patient preference and physician’s experience. All else equal, GPs in all countries preferred lower total costs. There was variation both within and between countries in the extent to which GPs were willing to accept high societal costs for greater patient benefit. GPs in England seem to exhibit stronger social agency in that they were less willing to accept high costs than GPs in both Norway and Scotland. However, in regard to patient costs and patient preferences, UK GPs were willing to accept greater societal costs. The authors discuss a variety of possible reasons for these findings but suggest that strong governance reinforces social agency, while cultural aspects moderate the effect.

Unrelated future costs and unrelated future benefits: reflections on NICE Guide to the Methods of Technology Appraisal. Health Economics [PubMed] Published 3rd July 2016

NICE would prefer that we disregard UFC. That’s unrelated future costs (not Ultimate Fighting Championship) – for example, the costs of dementia care having prevented death from a heart attack. But the availability of these unrelated treatments will likely confer benefit that is not excluded from the analysis. So it’s easy to see how we could end up with suboptimal allocations of resources. In this editorial, the authors consider the arguments against the inclusion of unrelated future costs, which can be broadly considered as relating to ‘principles’, ‘practicalities’ and ‘implications’. The authors argue that current approaches in principle are no more acceptable than the inclusion of costs and exclusion of benefits, as both are inconsistent in their handling of future payoffs. Practically, the authors argue that it is simpler to incorporate projected future costs than to tease out future benefits. Some have argued that the implications are limited, but the authors highlight that issues such as the level of comorbidity could have a major impact. There’s a lot of research still to be done in this area, but for now we should at least strive for consistency in our handling of future costs and benefits.

The capability approach: a critical review of its application in health economics. Value in Health Published 29th June 2016

Friends know I’ve been guilty of a bit of ICECAP-bashing in the past. Though I like the capability approach in principle, I am not a fan of how it has been applied in health economics. Naturally, I was drawn to this “critical” review. In fact, it was published as a working paper just before I finished writing my chapter for Jeff’s book but I didn’t have time to read it let alone incorporate its findings. So here we are with the real (published) deal. The primary purpose of the review is to evaluate the extent to which current questionnaires (e.g. ICECAP) can actually capture capabilities. The article does an excellent job of concisely identifying fundamental problems in the use of current measures. One issue is that the use of terms like “able to” does not allow for trade-offs between domains. A person may have maximum capability in all domains, but not be able to achieve maximal functionings in all of them simultaneously. As such, unachievable capability sets could be defined. Another problem is that these measures do not capture all of the possible combinations of functionings, only the dominant one. Therefore, these measures fail to capture the key basis for the capability approach – the value in choice. We haven’t yet figured out how to properly value a set, rather than a single combination. The authors suggest a way forward, based on the estimation of ‘approximate capability’. This could identify dominant functionings and the degree of choice. A key benefit of this approach would be the conceptual clarity for which it allows. As I have argued, I think this is the main failure of the application of the capability approach (and indeed health state valuation more broadly) in health economics.

Clinical guidelines: a NICE way to introduce cost-effectiveness considerations? Value in Health Published 28th June 2016

Most UK health economists will be familiar with NICE clinical guidelines. They outline what should (and should not) be taking place as part of care pathways in the NHS. The production of guidelines isn’t (usually) triggered by any new intervention but rather they are designed to improve current standard of care. The recommendations take into account economic considerations. This article outlines some of the advantages of the NICE guidelines programme and describes the role of health economists. One advantage of the guideline development process is that it is a joint enterprise between NICE and the various royal colleges of medicine. But there is also tension in this relationship from an economics perspective as optimal individual patient care may be at odds with broader societal objectives (if you’ve skipped ahead, see the first article summarised above). This article identifies a key advantage of NICE guidelines as being able to make recommendations on disinvestment. A key potential that I see, which isn’t discussed here, is for whole disease modelling studies to be routinely funded as part of the guideline development process.

Photo credit: Antony Theobald (CC BY-NC-ND 2.0)