Could a reduction in the cost-effectiveness threshold stymie medical research and development?

In previous posts (here and here) the comprehensive work undertaken by Claxton et al on the returns to medical expenditure in the NHS was discussed. Claxton and colleagues estimated the average change in quality adjusted life years (QALY) that have resulted from marginal budget changes in the NHS. Their estimate was £9,000 per QALY, much lower than the current threshold of £20,000 to £30,000 per QALY, so they argued that the threshold should be reduced. While we discussed some possible criticisms of the research it nevertheless remains the best empirical work on the topic and does provide some evidence for a reduction in the threshold. Many national news outlets picked up on the story when the paper was published. Nevertheless, changes to NHS reimbursement decisions and associated policies may have effects beyond that of altering the cost-effectiveness of the portfolio of treatments provided by the NHS.

A recent paper by Koijen, Philipson, and Uhlig argues that uncertainty about government healthcare policy in the United States has reduced medical research and development (R&D) and as a consequence reduced overall medical spending as a share of GDP. The equity returns of firms in the health care sector are higher than in other sectors: they find there is a ‘premium’ of around 4-6%. This, they argue, is a consequence of there being higher risk in the health care sector.

Figure from Koijen, Philipson, and Uhlig (2016)

Koijen, Philipson, and Uhlig suggest that this higher risk is a result of uncertainty about government regulation in the health care sector. The figure above demonstrates a large drawdown in the healthcare sector, not related to other sectors, at the time when the Clinton health care reform was being discussed. However, the figure does not show an equivalent decrease specific to the health care sector for Obamacare (around 2008-10). Obamacare likely improves health care sector revenues by adding a large number of health care consumers to the market. This quantity effect outweighs any reduction in markup. Indeed, the healthcare sector spent around $150 million dollars lobbying in support of the Affordable Care Act. However, the effect of the Clinton health reform would have been to impose price controls, thus reducing returns to the health care sector.

That medical innovation is directed towards the areas with the highest returns is an uncontroversial idea. It is the reason so few new treatments are developed for tropical diseases and tuberculosis, for example. Changes in government policy that affect returns of investment are likely to affect investment decisions. A reduction in the threshold for reimbursement will reduce return on investment in the UK. Uncertainty about future health care policy may also do so for the above reasons. The health care sector is particularly sensitive to American health care policy since the US accounts for 48% of global medical spending, so such changes in the UK may not lead to large effects. But for firms whose primary customer is the NHS, this may well be an issue.

A potential solution is to increase subsidies for medical R&D in the event of a reduction in the cost-effectiveness threshold. Indeed, this is one potential solution to encouraging the development of treatments for those diseases that predominantly affect people in the global South. However, what the article above demonstrates is that if there is a large amount of uncertainty about health care policies then any R&D stimuli may not have their intended effects.


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E-cigarettes and the role of science in public health policy

E-cigarettes have become, without doubt, one of the public health issues du jour. Many countries and states have been quick to prohibit them, while others continue to debate the issue. The debate ostensibly revolves around the relative harms of e-cigarettes: Are they dangerous? Will they reduce the harms caused by smoking tobacco? Will children take them up? Questions which would typically be informed by the available evidence. However, there is a growing schism within the scientific community about what indeed the evidence does say. On the one hand, there is the view that the evidence, when taken altogether, overwhelmingly suggests that e-cigarettes are significantly less harmful than cigarettes and would reduce the harms caused by nicotine use. On the other hand, there is vocal group that doubt the veracity of the available evidence and are critical of e-cigarette availability in general. Indeed, this latter view has been adopted by the biggest journals in medicine, The Lancet, the BMJ, the New England Journal of Medicine, and JAMA, each of whom have published either research or editorials along this line.

The evidence around e-cigarettes was recently summarised and reviewed by Public Health England. The conclusion of the review was the e-cigarettes were 95% less harmful than smoking tobacco. So why might these journals take a position that is arguably contrary to the evidence? From a sociological perspective, epistemological conflicts in science are also political conflicts. Actions within the scientific field are directed to acquiring scientific authority, and that authority requires social recognition. However, e-cigarette policy is also a political issue, and as such actions in this area are also directed at gaining political capital. If the e-cigarette issue can be delimited to a purely scientific problem then scientific capital can be translated into political capital. One way of achieving this is to try to establish oneself as the authoritative scientific voice on such matters and to doubt the claims made by others.

We can also view the issue in a broader context. The traditional journal format is under threat from other models of scientific publishing, including blogs, open access publishers, pre-print archives, and post-publication peer review. Much of the debate around e-cigarettes has come from these new sources. Dominant producers in the scientific field must necessarily be conservative since it is the established structure of scientific field that grants these producers their dominant status. But this competition is the scientific field may have wider, pernicious consequences.

Typically, we try to formulate policies that maximise social welfare. But, as Acemoglu and Robinson point out, the policy that may maximise social welfare now, may not maximise welfare in the long run. Different policies today affect the political equilibrium tomorrow and thus the policies that are available to policy makers tomorrow. Prohibiting e-cigarettes today may be socially optimal if there were no reliable evidence on their harms or benefits and there were suspicions that they could cause public harm. But, it is very difficult politically to reverse prohibition policies, even if evidence were to later emerge that e-cigarettes were an effective harm reduction product. Thus, even if the journals were to doubt the evidence around e-cigarettes, then the best policy position would arguably be to remain agnostic and await further evidence. But, this would not be a position that would grant them socially recognised scientific capital.

Perhaps this e-cigarette debate is reflective of a broader shift in the way in which scientific evidence and those with scientific capital are engaged in public health policy decisions. Different forms of evidence beyond RCTs are being more widely accepted in biomedical research and methods of evidence synthesis are being developed. New forums are also becoming available for their dissemination. This, I would say, can only be a positive thing.

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Posted by on November 23, 2015 in Public Health


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What’s wrong with a simple model?

Healthcare institutions are large, complex systems and evaluating the effects of policies or structural interventions within these systems is challenging. In many cases it is not possible to directly measure the effect of the intervention at the patient level. The impact on any one patient is too small, necessitating a prohibitively large sample size in any one study, and yet when applied across all patients the effects of the intervention may be both clinically and economically significant (Lilford et al, 2010). Consider the frequently discussed “Seven day NHS”: at the margin, the patients’ risk of mortality may only change by four tenths of a percentage point at best (if the intervention works as discussed in parliament). We may therefore rely on measuring changes to more “upstream” outcomes which may act as proxies for the clinically important outcomes such as mortality. But then how do we make sense of the various pieces of evidence produced and evaluate the intervention in terms that can be compared to other interventions? A model!

In the not so distant past, I presented a simple model of how an electronic prescribing system may impact on patient clinical and economic outcomes. The commentary on this model was that one should not trust a model so simple. Simple models do not capture this or that aspect of the world and cannot account for this or that observation. But, I would argue, this critique does not hold water.

Kieran Healey distinguishes, in the excellent essay Fuck Nuance that explores the value of nuance in sociological theory, three “nuance traps” that the person who views nuance as an important virtue may fall into. Firstly, the fine-grain nuance that is the detailed, merely empirical description of the world. Secondly, the conceptual framework, which is the “extensive expansion of some theoretical system in a way that effectively closes it off from rebuttal or disconfirmation by anything in the world.” And, thirdly, the connoisseur, the valuing of nuance to demonstrate one’s ability “to grasp and express the richness, texture, and flow of social reality itself.” Any of these manifestations of the nuance trap could be applied to any model presented to an audience, but I think the criticisms that most often arise in the context of healthcare systems research is the nuance of the fine-grain.

The aim of models in our context is to predict and explain phenomena in the healthcare system. Phenomena are distinct from the data from which they are inferred (Bogen & Woodward, 1988). Phenomena are generally stable and are the result of the confluence of a manageable number of causal factors, whereas data are noisy measures of the phenomena that are generated by a very large number of factors, including measurement error and bias. To quote Bogen and Woodward:

In undertaking to explain phenomena rather than data, a scientist can avoid having to tell an enormous number of independent, highly local, and idiosyncratic causal stories involving the (often inaccessible and intractable) details of specific experimental and observational contexts. He can focus instead on what is constant and stable across different contexts. This opens up the possibility of explaining a wide range of cases in terms of a few factors or general principles. It also facilitates derivability and the systematic exhibition of dependency-relations.

The models can and should reflect important aspects of the system, such as why there are nonlinearities in the system, but ultimately we are trying to explain why an intervention works and predict it effects. The function of the data is to help with this task. Simple models are not bad merely by virtue of being simple.

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Posted by on October 23, 2015 in Public Health


Review: The Passionate Economist (Sally Sheard)

Credit: Policy Press

Credit: Policy Press

The Passionate Economist: How Brian Abel-Smith shaped global health and social welfare

Hardcover, 581 pages, ISBN: 9781447314844, published 21 November 2013

Amazon / Google Books / Policy Press

I enjoy reading about the lives of effective academics. Sally Sheard’s biography of Brian Abel-Smith drew me in with its title: ‘The Passionate Economist’. It hints at a life to which some of us might aspire.

Brian Abel-Smith was an economist who straddled academia and politics. He spent much of his career with the LSE, but his global impact came through work with the WHO, Europe and developing nations’ governments.

Before embarking on this hefty hardback, my prior knowledge of Brian Abel-Smith was sparse. I delved into my reference manager – sure I’d read some of his work – to find a chapter in a book first published before I was born. The latest generation of health economists could easily miss Abel-Smith’s work. Sheard’s book reveals some reasons why. He never participated in the Health Economists’ Study Group and dedicated much of his career to practical work with an international focus. He was reluctant to engage with the theory-heavy work by the likes of Tony Culyer, which helps explain why many MSc courses don’t include him.

Sheard starts out by describing the early years of a boy with whom most readers will have little in common. Born in 1926 to a Brigadier-General and distant relation to the royal family, Brian was surrounded by people who were already – or were soon to be – key players in politics and academia. Like Brian, Clement Attlee had attended Haileybury school and promoted several Old Haileyburians in his government. At Cambridge University – where Brian was taught by the likes of Joan Robinson – Labour minister Hugh Dalton was trying to recruit promising students. He invited Brian for supper in 1951. Brian’s privilege is suitably framed and is not likely to alienate the reader, but the torrent of name-dropping might. It is difficult to keep up with so many acquaintances, and difficult to care. Though we at least observe Brian’s adept social skills.

It was charm (coupled with good connections) that enabled Brian to meet the father of social policy, Richard Titmuss. Theirs was to be a long and fruitful professional relationship and friendship, and the basis of Brian’s commitment to the LSE. The Beveridge Report was published when Brian was 16, and readers will find its principles reverberating through his life. Brian was dedicated to the NHS, and his first major research project involved costing the health service. His findings informed the 1956 Guillebaud Report, which concluded that the NHS was pretty good value for money. As Brian made quick progress in his academic career, he prudently maintained relationships with the political world.

It was Brian’s political writing that really got him noticed; his first publication was a pamphlet for the Fabian Society. Despite their incongruence, Brian handled the two worlds of politics and academia masterfully. Sheard likewise gives each their due attention in the retelling. The book is particularly enjoyable when it recounts episodes where Brian’s academic integrity shines through; possibly to the detriment of his political ties and socialist credentials. He regularly criticised Labour policy and shocked colleagues like Julian Tudor Hart when he wrote in support of competition, consumer choice and user fees. I rarely traverse political history as I find it pretty dull, but in staging this history as a backdrop to Brian’s life, Sheard was able to keep me engaged. My limited knowledge of the academic and political history of social policy in Britain left me with much to learn; better informed (or older) readers might not gain so much from the revision.

Brian’s interests extended beyond the ivory tower and beyond Westminster, and so he took roles in hospital management and governance. His work formed part of the basis for the formation of the Child Poverty Action Group, to which he dedicated a lot of time. He also had an entrepreneurial side, setting up a successful men’s clothing business, Just Men. His work is not shown to be of special relevance to the study of health economics, but rather to health and social policy more generally. Seasoned observers might not be surprised to read many parallels between the debates of Brian’s time and those currently storming on Twitter.

For me, the biography does not quite live up to its title. Abel-Smith is presented as many things – conscientious, charming, virtuous, humble, loyal – but his passion does not shine through. The book is written in a matter-of-fact style, and the fact of the matter seems to be that Brian was not particularly demonstrative. But a historical account – more a biography of global health policy than of Brian Abel-Smith – might be fitting. Brian’s life is a history of global health policy. His career tracks international developments and the book will be a treasure trove for historians.

In The Passionate Economist, Sheard has produced a valuable overview of the history of global health policy that many young health economists (like myself) might lack. Part of the joy in reading a biography – for me, at least – is identifying those tantalising gaps in the evidence, which require or encourage the author to use their knowledge of the subject to suggest what might have been. The Passionate Economist contains little of this and Sheard seems to struggle to get under the skin of her subject. But maybe that’s the point; Brian’s passion is veiled by his self-effacing nature. His impact on social policy and health services, both in the UK and internationally, was prodigious. And yet many health economists have never heard of him. There is wisdom in this.

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Posted by on August 10, 2015 in Reviews


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The economics of a 7-day NHS

The recently delivered Queen’s speech set out the government’s plan for “a 7-day NHS”. This vision is a reaction to alarming statistics that mortality rates are increased by 11% for patients admitted to hospital on a Saturday, and 16% if admitted on a Sunday, compared to patients admitted during the week. In a recent paper, I (along with my co-authors) examine the evidence base being used to support this policy move in more detail, and estimate the economic consequences in terms of the potential costs and benefits. The paper focuses on emergency hospital admissions, as this is the area in which the majority of these deaths occur and has been the focus of much of the policy debate.

The evidence base for seven day services

The highly quoted figure of a 16% increase in the risk of mortality is in fact a relative risk, which we all know too well can be misleading. When interpreting risk statistics the key piece of information is the baseline level of risk; figures which are omitted from the case for seven-day services. The most recent figures from England put the elevated mortality risk experienced by patients admitted to hospital in an emergency during the weekend at 0.3 percentage points. Whilst by no means trivial, it is doubtful that this alternative interpretation of the statistics would have summoned quite the same passion for a reorganisation of the entire English healthcare system.

The classic confusion between correlation and causation is the next mistake made when interpreting the ‘weekend effect’ literature. The association between reduced staffing levels in hospitals at weekends and elevated mortality has been cited as the root of the problem, despite a lack of causal evidence to this effect. In spite of this absence of supportive evidence, making routine services available seven days a week has been declared as the solution to tackling the observed weekend effect. The crucial question then, is what are the likely costs and benefits of such service extensions?

“it’s about saving lives”

As economists we are familiar with the concept of opportunity cost, yet sadly it appears that politicians and policy makers have yet to grasp this key notion. Regardless of whether seven-day services are funded through a redistribution of current NHS budgets or an injection of new cash, this decision implicitly diverts potential resources away from patients admitted during the week. The average daily volume of patients admitted to hospital in an emergency is significantly higher on weekdays than during weekends. This means that staff would be diverted away from working at times of high patient volumes to times when there are fewer patients needing treatment. Yet these patients from whom resources are diverted away are never mentioned in arguments of fairness or equity. If, as the government suggest, staffing levels really are the key to reducing mortality, then the introduction of seven-day services may well narrow the gap between weekday and weekend mortality rates. However, it could easily do so by causing the weekday death rate to rise.

Potential benefits and costs of seven-day services

As healthcare policies such as seven-day services are funded from the same NHS budget as new treatments, they should be subject to the same cost-effectiveness evaluation as technologies seeking NICE approval. This requires rigorous evaluation of hard evidence, something seemingly neglected in favour of headline-hitting policy promises. In the paper we use the available evidence, albeit somewhat rudimentary, on the costs and benefits of introducing seven-day services in this setting to assess whether the policy change would likely pass a NICE assessment. We do so under the most optimistic assumption that this service change has the potential to completely eradicate the weekend effect.

Using methods described in detail in the paper, we estimate that reducing the mortality rate experienced by patients admitted in an emergency at the weekends to that observed during the week would result in an annual reduction of between 4,355 and 5,353 deaths occurring nationally (ceteris paribus, of course). This translates into a potential health gain of 29,727 – 36,539 QALYs per year if all of these deaths could be averted. Using the NICE threshold of £20,000 per QALY, the NHS should spend no more than £595m – £731m to achieve a health gain of this size.

Whilst the potential benefits of extending services appear large, they must be compared with the additional costs of doing so. Although caution was emphasised when producing the figures, the best available estimates of the costs of implementing seven-day services are those published by the NHS Seven Days a Week Forum. They estimate this to be 1.5% to 2% of total hospital income, equivalent to a 5% to 6% increase in the cost of emergency admissions. This translates to an annual cost of between £1.07bn and £1.43bn, exceeding our estimates of the maximum amount that the NHS should spend to eradicate the weekend effect by a factor of 1.5 to 2.4, or between £339m and £831m. To make matters worse, all of these calculations take place under the rather optimistic assumption that benefits to patients admitted at the weekend could be achieved without any detrimental effect on outcomes for those admitted during the week.

The way forward

Although alarming, the statistics on elevated weekend mortality are insufficient by themselves to justify a policy change towards extending normal hours of operation into the weekend. There is as yet no clear evidence: that seven-day working will, in isolation, reduce the weekend death rate; that lower weekend mortality rates can be achieved without increasing weekday death rates; or that such reorganisation is cost-effective.

A move towards a fully operational NHS service seven days a week has the potential to have impacts beyond reducing mortality, but these must be evidenced if the policy is to be supported. Mere suggestions that it may reduce factors such as readmission rates and hospital length of stay are not enough to justify a policy change, just as the verbal reassurance of a drug manufacturer that their product was able to cure cancer would not alone secure them NICE approval. Rigorous evidence and evaluation is needed in the policy sphere if we are truly to get the best use from our limited NHS resources. Evaluations of the implementation of seven-day services in the thirteen early adopters should be performed before national implementation is considered, just as any potential new treatment would be trialled before approval.

Disclaimer: The views and opinions expressed are those of the author and do not necessarily reflect those of the HS&DR programme, NIHR, NHS or the Department of Health.


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Identifying the effect of expenditure on health outcomes: another small comment on Claxton et al

In a previous post I asked whether the study by Claxton et al can or should inform the cost-effectiveness threshold used by NICE. The authors argued that, “it is the expected health effects … of the average displacement within the current NHS … that is relevant to the estimate of the threshold.” Accepting this premise, the authors aimed to estimate the average net effect (in terms of QALYs) that has historically resulted from contraction and expansion of the healthcare budget. I want to explore here briefly whether their empirical estimates can indeed be interpreted as such.

When the healthcare budget contracts we may remove services or technologies with a low cost-effectiveness and when it expands we may implement services or technologies with a high cost-effectiveness. The argument is that we don’t want to reimburse a new technology that has a lower cost-effectiveness than what the healthcare service already achieves in practice when the budget changes, which is the net effect of contraction and expansion. The approach taken by Claxton and colleagues to estimate this effect was to use local healthcare authority level data on healthcare expenditure across different programmes of healthcare (e.g. respiratory healthcare or oncology) and examine how changes in this expenditure affected healthcare outcomes, such as mortality. Since healthcare expenditure is likely to be correlated with unobservable determinants of mortality, the authors adopted an instrumental variables approach.

At this point it is important to note that total healthcare expenditure may vary for two reasons. On the supply side, there may be changes in unit costs or shifts in the overall budget constraint; on the demand side, population health may change affecting the need for healthcare, the identity of the patients, and the types of services demanded as well as reductions in the use of healthcare by current patients. I would argue that it is the supply side changes that we are interested in here. Demand side changes may shift which programmes are utilised, and the resulting productivity of those programmes, since the characteristics of the patients will change.

The estimates from an instrumental variables estimator can be interpreted as the local average treatment effect (LATE) which is the average effect of a change in the variable of interest (in this case healthcare expenditure) resulting from a change in the instrumental variable (IV). The IVs utilised by Claxton et al are socio-economic variables (such as the index of multiple deprivation and the proportion of the population providing unpaid care). These variables are arguably on the demand and supply sides since they both affect population healthcare needs leading to different populations being treated and may affect the healthcare utilisation of current patients.

The empirical estimates of Claxton et al may therefore possibly be interpreted as the effect of both changes due to contractions and expansions of the budget (the effect of interest) and a change in the programmes of care provided, the treatments within them, and their productivity resulting from changes to population health needs and the identity of patients.*

Overall, I think that even if we accept the authors’ arguments about why they are trying to identify this effect, their empirical strategy may possibly not identify it.


*It may be argued that a test of over-identifying restrictions (OID), which tests if the instruments are correlated with the errors, would detect if these instruments were related to health needs. However, note that we are looking within a programme of care, for example, cancer expenditure on cancer mortality so that we are conditioning on having cancer (and being diagnosed with it) in this analysis. Socio-economic variables may be determinants of getting cancer or which type of cancer a patient gets but may not be determinants of (i.e. are independent of) the health outcomes from cancer once we’ve conditioned on having cancer and some other factors determining health outcomes. They may therefore pass the OID test.


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Do we really need to change the cost-effectiveness threshold?

The cost-effectiveness threshold utilised by health technology assessment agencies, such as NICE in the United Kingdom, below which new medical technologies and interventions are considered cost-effective, is frequently discussed. NICE currently use a threshold of £20,000 to £30,000 per quality adjusted life year (QALY) gained. However, this threshold was arrived at in somewhat of an ad hoc manner, being simply a reflection of past recommendations made by the agency. As a result, much time has been spent trying to identify what the threshold ought to be in order to best ensure the efficiency of the health service.

To much media attention, a study by Claxton et al was published recently, in which the authors attempt to estimate the returns currently being achieved by the NHS in England. The argument goes that the consideration to adopt a new medical technology should take into account the opportunity cost of doing so; any new technology, given a fixed budget, will displace resources used to achieve health benefits elsewhere in the healthcare service. If the new technology is not as cost-effective as the returns currently being achieved for the money, then the overall efficiency of the healthcare service will be reduced. Claxton and colleagues arrived at the figure of approximately £13,000 per QALY and have argued that NICE should adopt this as their threshold.

The opportunity cost is the benefit foregone by not spending a certain amount of money or deploying resources elsewhere. Within the health service we do not want to reimburse a new treatment when we could alternatively use those resources to achieve greater health gains otherwise; hence, the argument that the threshold should reflect the opportunity cost. What then is the opportunity cost in the health service? Assuming a fixed budget, it is the health gains made from increasing the expenditure on the most cost-effective programme of care minus the health losses from reducing the expenditure on the least cost-effective programme of care, when the healthcare budget is contracted and then expanded.

Does the Claxton et al study estimate this opportunity cost? Only if we assume that there is allocative efficiency in the healthcare service, i.e. that the most cost-effective programme funded when the budget is expanded has the same cost-effectiveness as the least cost-effective programme removed when the budget is contracted, and when there is optimal displacement, i.e. that the displaced technologies are the least cost-effective (Eckerman and Pekarsky, 2015). Neither of these conditions are likely to hold in the health service given the nature of the healthcare market, which may suggest that the Claxton et al results are underestimates of the true opportunity cost.

The above discussion assumes that the goal is the maximisation of population health. However, equity considerations play a role in reimbursement decisions such that we might be willing to maintain funding for a less cost-effective service if it preserves some measure of equity. Incorporation of equity concerns into economic evaluation is often not done in practice but methods do exist. In such a case, we may wish to adopt an equity weighted threshold that reflects an equity weighted opportunity cost. Alternatively, we could allow a different threshold for different patient groups, where the difference between the thresholds reflects society’s willingness to pay for benefits accruing to different persons. Either way we may prefer a threshold higher than the Claxton et al  figure to make room for equity considerations.

A final point is that profit-maximising manufacturers strategically price their products at the cost-effectiveness threshold. Under these conditions, even if displacement is optimal, then there will be no net gain to population health from adopting the new products despite them meeting the cost-effectiveness threshold.

What this all may suggest is that, methodological issues aside, the Claxton et al study does not provide us with strong enough evidence to change the cost-effectiveness threshold. Further research is required to understand which services are actually displaced, the cost-effectiveness of services currently utilised, and incorporation of equity considerations in reimbursement decisions.

Update: As an addendum and in response to a comment below, Claxton et al do write that, “Given NICE’s remit, it is the expected health effects … of the average displacement within the current NHS … that is relevant to the estimate of the threshold.” This average effect, they arguably do estimate; nevertheless, I think it is important to note that under allocative inefficiency and suboptimal displacement, setting this as the threshold may possibly lead us to either (i) reimburse technologies that are worse than the best alternative (the opportunity cost), or (ii) reject technologies that are more cost-effective than the least cost-effective technology removed under a budget contraction.


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