Chris Sampson’s journal round-up for 4th December 2017

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.

Funding breakthrough therapies: a systematic review and recommendation. Health Policy Published 2nd December 2017

One of the (numerous) financial pressures on health care funders in the West is the introduction of innovative (and generally very expensive) new therapies. Some of these can be considered curative, which isn’t necessarily the best way for manufacturers to create a steady income. New funding arrangements have been proposed to facilitate patient access while maintaining financial sustainability. This article focuses on a specific group of innovative therapies known as ‘Advanced Therapy Medicinal Products’ (ATMPs), which includes gene therapies. The authors conducted a systematic review of papers proposing funding models and considered their appropriateness for ATMPs. There were 48 papers included in the review that proposed payment mechanisms for high-cost therapies. Three top-level groups were identified: i) financial agreements, ii) performance-based agreements, and iii) healthcoin (a tradable currency representing the value of outcomes). The different mechanisms are compared in terms of their feasibility, acceptability, burden, ‘financial attractiveness’ and their appeal to payers and manufacturers. Annuity payments are identified as relatively attractive compared to other options, but each mechanism is summarily shown to be imperfect in the ATMP context. So, instead, the authors propose an ATMP-specific fund. For UK readers, this will likely smell a bit too much like the disastrous Cancer Drugs Fund. It isn’t clear why such a programme would be superior to annuity payments or more inventive mechanisms, or even whether it would be theoretically sound. Thus, the proposal is not convincing.

Supply-side effects from public insurance expansions: evidence from physician labor markets. Health Economics [PubMed] Published 1st December 2017

Crazy though American health care may be, its inconsistency in coverage can make for good research fodder. The Child Health Insurance Program (CHIP) was set up in 1997 and then, when the initial money ran out 10 years later, the program was (eventually) expanded. In this study, the authors use the changes in CHIP to examine the impact of expanded public coverage on provider behaviour, namely; subspecialty training (which could become more attractive with a well-insured customer base), practice setting and prevailing wage offers. The data for the study relate to the physician labour market for New York state for 2002-2013, as collected in the Graduate Medical Education survey. A simple difference-in-differences analysis is conducted with reference to the 2009 CHIP expansion, controlling for physician demographics. Paediatricians are the treatment group and the control group is adult physician generalists (mostly internal medicine). 2009 seems to be associated with a step-change in the proportion of paediatricians choosing to subspecialise – an increased probability of about 8 percentage points. There is also an upward shift in the proportion of paediatricians entering private practice, with some (weak) evidence that there is an increased preference for rural areas. These changes don’t seem to be driven by relative wage increases, with no major change in trends. So it seems that the expanded coverage did have important supply-side effects. But the waters are muddy here. In particular, we have the Great Recession and Obamacare as possible alternative explanations. Though it’s difficult to come up with good reasons for why these might better explain the observed changes.

Reflections on the NICE decision to reject patient production losses. International Journal of Technology Assessment in Health Care [PubMedPublished 20th November 2017

When people conduct economic evaluations ‘from a societal perspective’, this often just means a health service perspective with productivity losses added. NICE explicitly exclude the inclusion of these production losses in health technology appraisals. This paper reviews the issues at play, focussing on the normative question of why they should (or should not) be included. Findings from a literature review are summarised with reference to the ethical, theoretical and policy questions. Unethical discrimination potentially occurs if people are denied health care on the basis of non-health-related characteristics, such as the ability to work. All else equal, should health care for men be prioritised over health care for women because men have higher wages? Are the unemployed less of a priority because they’re unemployed? The only basis on which to defend the efficiency of an approach that includes productivity losses seems to be a neoclassical welfarist one, which is hardly tenable in the context of health care. If we adopt the extra-welfarist understanding of opportunity cost as foregone health then there is really no place for production losses. The authors also argue that including production losses may be at odds with policy objectives, at least in the context of the NHS in the UK. Health systems based on privately-funded care or social insurance may have different priorities. The article concludes that taking account of production losses is at odds with the goal of health maximisation and therefore the purpose of the NHS in the UK. Personally, I think priority setting in health care should take a narrow health perspective. So I agree with the authors that production losses shouldn’t be included. I’m not sure this article will convince those who disagree, but it’s good to have a reference to vindicate NICE’s position.

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Chris Sampson’s journal round-up for 20th November 2017

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.

Effects of health and social care spending constraints on mortality in England: a time trend analysis. BMJ Open [PubMed] Published 15th November 2017

I’d hazard a guess that I’m not the only one here who gets angry about the politics of austerity. Having seen this study’s title, it’s clear that the research could provide fuel for that anger. It doesn’t disappoint. Recent years have seen very low year-on-year increases in public expenditure on health in England. Even worse, between 2010 and 2014, public expenditure on social care actually fell in real terms. This is despite growing need for health and social care. In this study, the authors look at health and social care spending and try to estimate the impact that reduced expenditure has had on mortality in England. The analysis uses spending and mortality data from 2001 onwards and also incorporates mortality projections for 2015-2020. Time trend analyses are conducted using Poisson regression models. From 2001-2010, deaths decreased by 0.77% per year (on average). The mortality rate was falling. Now it seems to be increasing; from 2011-2014, the average number of deaths per year increased by 0.87%. This corresponds to 18,324 additional deaths in 2014, for example. But everybody dies. Extra deaths are really sooner deaths. So the question, really, is how much sooner? The authors look at potential years of life lost and find this figure to be 75,496 life-years greater than expected in 2014, given pre-2010 trends. This shouldn’t come as much of a surprise. Spending less generally achieves less. What makes this study really interesting is that it can tell us who is losing these potential years of life as a result of spending cuts. The authors find that it’s the over-60s. Care home deaths were the largest contributor to increased mortality. A £10 cut in social care spending per capita resulted in 5 additional care home deaths per 100,000 people. When the authors looked at deaths by local area, no association was found with the level of deprivation. If health and social care expenditure are combined in a single model, we see that it’s social care spending that is driving the number of excess deaths. The impact of health spending on hospital deaths was less robust. The number of nurses acted as a mediator for the relationship between spending and mortality. The authors estimate that current spending projections will result in 150,000 additional deaths compared with pre-2010 trends. There are plenty of limitations to this study. It’s pretty much impossible (though the authors do try) to separate the effects of austerity from the effect of a weak economy. Still, I’m satisfied with the conclusion that austerity kills older people (no jokes about turkeys and Christmas, please). For me, the findings also highlight the need for more research in the context of social care, and how we (as researchers) might effectively direct policy to prevent ‘excess’ deaths.

Should cost effectiveness analyses for NICE always consider future unrelated medical costs? BMJ [PubMed] Published 10th November 2017

The question of whether or not ‘unrelated’ future medical costs should be included in economic evaluation is becoming a hot topic. So much so that the BMJ has published this Head To Head, which introduces some of the arguments for and against. NICE currently recommends excluding unrelated future medical costs. An example given in this article is the case of the expected costs of dementia care having saved someone’s life by heart transplantation. The argument in favour of including unrelated costs is quite obvious – these costs can’t be ignored if we seek to maximise social welfare. Their inclusion is described as “not difficult” by the authors defending this move. By ignoring unrelated future costs (but accounting for the benefit of longer life), the relative cost-effectiveness of life-extending treatments, compared with life-improving treatments, is artificially inflated. The argument against including unrelated medical costs is presented as one of fairness. The author suggests that their inclusion could preclude access to health care for certain groups of people that are likely to have high needs in the future. So perhaps NICE should ignore unrelated medical costs in certain circumstances. I sympathise with this view, but I feel it is less a fairness issue and more a demonstration of the current limits of health-related quality of life measurement, which don’t reflect adaptation and coping. However, I tend to disagree with both of the arguments presented here. I really don’t think NICE should include or exclude unrelated future medical costs according to the context because that could create some very perverse incentives for certain stakeholders. But then, I do not agree that it is “not difficult” to include all unrelated future costs. ‘All’ is an important qualifier here because the capacity for analysts to pick and choose unrelated future costs creates the potential to pick and choose results. When it comes to unrelated future medical costs, NICE’s position needs to be all-or-nothing, and right now the ‘all’ bit is a high bar to clear. NICE should include unrelated future medical costs – it’s difficult to formulate a sound argument against that – but they should only do so once more groundwork has been done. In particular, we need to develop more valid methods for valuing quality of life against life-years in health technology assessment across different patient groups. And we need more reliable methods for estimating future medical costs in all settings.

Oncology modeling for fun and profit! Key steps for busy analysts in health technology assessment. PharmacoEconomics [PubMed] Published 6th November 2017

Quite a title(!). The subject of this essay is ‘partitioned survival modelling’. Honestly,  I never really knew what that was until I read this article. It seems the reason for my ignorance could be that I haven’t worked on the evaluation of cancer treatments, for which it’s a popular methodology. Apparently, a recent study found that almost 75% of NICE cancer drug appraisals were informed by this sort of analysis. Partitioned survival modelling is a simple means by which to extrapolate outcomes in a context where people can survive (or not) with or without progression. Often this can be on the basis of survival analyses and standard trial endpoints. This article seeks to provide some guidance on the development and use of partitioned survival models. Or, rather, it provides a toolkit for calling out those who might seek to use the method as a means of providing favourable results for a new therapy when data and analytical resources are lacking. The ‘key steps’ can be summarised as 1) avoiding/ignoring/misrepresenting current standards of economic evaluation, 2) using handpicked parametric approaches for extrapolation in order to maximise survival benefits, 3) creatively estimating relative treatment effects using indirect comparisons without adjustment, 4) make optimistic assumptions about post-progression outcomes, and 5) deny the possibility of any structural uncertainty. The authors illustrate just how much an analyst can influence the results of an evaluation (if they want to “keep ICERs in the sweet spot!”). Generally, these tactics move the model far from being representative of reality. However, the prevailing secrecy around most models means that it isn’t always easy to detect these shortcomings. Sometimes it is though, and the authors make explicit reference to technology appraisals that they suggest demonstrate these crimes. Brilliant!

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Thesis Thursday: Koonal Shah

On the third Thursday of every month, we speak to a recent graduate about their thesis and their studies. This month’s guest is Dr Koonal Shah who has a PhD from the University of Sheffield. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Valuing health at the end of life
Supervisors
Aki Tsuchiya, Allan Wailoo
Repository link
http://etheses.whiterose.ac.uk/17579

What were the key questions you wanted to answer with your research?

My key research question was: Do members of the general public wish to place greater weight on a unit of health gain for end of life patients than on that for other types of patients? Or put more concisely: Is there evidence of public support for an end of life premium?

The research question was motivated by a policy introduced by NICE in 2009 [PDF], which effectively gives special weighting to health gains generated by life-extending end of life treatments. This represents an explicit departure from the Institute’s reference case position that all equal-sized health gains are of equal social value (the ‘a QALY is a QALY’ rule). NICE’s policy was justified in part by claims that it represented the preferences of society, but little evidence was available to either support or refute that premise. It was this gap in the evidence that inspired my research question.

I also sought to answer other questions, such as whether the focus on life extensions (rather than quality of life improvements) in NICE’s policy is consistent with public preferences, and whether people’s stated end of life-related preferences depend on the ways in which the preference elicitation tasks are designed, framed and presented.

Which methodologies did you use to elicit people’s preferences?

All four of my empirical studies used hypothetical choice exercises to elicit preferences from samples of the UK general public. NICE’s policy was used as the framework for the designs in each case. Three of the studies can be described as having used simple choice tasks, while one study specifically applied the discrete choice experiment methodology. The general approach was to ask survey respondents which of two hypothetical patients they thought should be treated, assuming that the health service had only enough funds to treat one of them.

In my final study, which focused on framing effects and study design considerations, I included attitudinal questions with Likert item responses alongside the hypothetical choice tasks. The rationale for including these questions was to examine the consistency of respondents’ views across two different approaches (spoiler: most people are not very consistent).

Your study included face-to-face interviews. Did these provide you with information that you weren’t able to obtain from a more general survey?

The surveys in my first two empirical studies were both administered via face-to-face interviews. In the first study, I conducted the interviews myself, while in the second study the interviews were subcontracted to a market research agency. I also conducted a small number of face-to-face interviews when pilot testing early versions of the surveys for my third and fourth studies. The piloting process was useful as it provided me with first-hand information about which aspects of the surveys did and did not work well when administered in practice. It also gave me a sense of how appropriate my questions were. The subject matter – prioritising between patients described as having terminal illnesses and poor prognoses – had the potential to be distressing for some people. My view was that I shouldn’t be including questions that I did not feel comfortable asking strangers in an interview setting.

The use of face-to-face interviews was particularly valuable in my first study as it allowed me to ask debrief questions designed to probe respondents and elicit qualitative information about the thinking behind their responses.

What factors influence people’s preferences for allocating health care resources at the end of life?

My research suggests that people’s preferences regarding the value of end of life treatments can depend on whether the treatment is life-extending or quality of life-improving. This is noteworthy because NICE’s end of life criteria accommodate life extensions but not quality of life improvements.

I also found that the amount of time that end of life patients have to ‘prepare for death’ was a consideration for a number of respondents. Some of my results suggest that observed preferences for prioritising the treatment of end of life patients may be driven by concern about how long the patients have known their prognosis rather than by concern about how long they have left to live, per se.

The wider literature suggests that the age of the end of life patients (which may act as a proxy for their role in their household or in society) may also matter. Some studies have reported evidence that respondents become less concerned about the number of remaining life years when the patients in question are relatively old. This is consistent with the ‘fair innings’ argument proposed by Alan Williams.

Given the findings of your study, are there any circumstances under which you would support an end of life premium?

My findings offer limited support for an end of life premium (though it should be noted that the wider literature is more equivocal). So it might be considered appropriate for NICE to abandon its end of life policy on the grounds that the population health losses that arise due to the policy are not justified by the evidence on societal preferences. However, there may be arguments for retaining some form of end of life weighting irrespective of societal preferences. For example, if the standard QALY approach systematically underestimates the benefits of end of life treatments, it may be appropriate to correct for this (though whether this is actually the case would itself need investigating).

Many studies reporting that people wish to prioritise the treatment of the severely ill have described severity in terms of quality of life rather than life expectancy. And some of my results suggest that support for an end of life premium would be stronger if it applied to quality of life-improving treatments. This suggests that weighting QALYs in accordance with continuous variables capturing quality of life as well as life expectancy may be more consistent with public preferences than the current practice of applying binary cut-offs based only on life expectancy information, and would address some of the criticisms of the arbitrariness of NICE’s policy.