Chris Sampson’s journal round-up for 17th June 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.

Mental health: a particular challenge confronting policy makers and economists. Applied Health Economics and Health Policy [PubMed] Published 7th June 2019

This paper has a bad title. You’d never guess that its focus is on the ‘inconsistency of preferences’ expressed by users of mental health services. The idea is that people experiencing certain mental health problems (e.g. depression, conduct disorders, ADHD) may express different preferences during acute episodes. Preference inconsistency, the author explains, can result in failures in prediction (because behaviour may contradict expectations) and failures in evaluation (because… well, this is a bit less clear). Because of preference inconsistency, a standard principal-agent model cannot apply to treatment decisions. Conventional microeconomic theory cannot apply. If this leaves you wondering “so what has this got to do with economists?” then you’re not alone. The author of this article believes that our role is to identify suitable agents who can interpret patients’ inconsistent preferences and make appropriate decisions on their behalf.

But, after introducing this challenge, the framing of the issue seems to change and the discussion becomes about finding an agent who can determine a patient’s “true preferences” from “conflicting statements”. That seems to me to be a bit different from the issue of ‘inconsistent preferences’, and the phrase “true preferences” should raise an eyebrow of any sceptical economist. From here, the author describes some utility models of perfect agency and imperfect agency – the latter taking account of the agent’s opportunity cost of effort. The models include error in judging whether the patient is exhibiting ‘true preferences’ and the strength of the patient’s expression of preference. Five dimensions of preference with respect to treatment are specified: when, what, who, how, and where. Eight candidate agents are specified: family member, lay helper, worker in social psychiatry, family physician, psychiatrist/psychologist, health insurer, government, and police/judge. The knowledge level of each agent in each domain is surmised and related to the precision of estimates for the utility models described. The author argues that certain agents are better at representing a patient’s ‘true preferences’ within certain domains, and that no candidate agent will serve an optimal role in every domain. For instance, family members are likely to be well-placed to make judgements with little error, but they will probably have a higher opportunity cost than care professionals.

The overall conclusion that different agents will be effective in different contexts seems logical, and I support the view of the author that economists should dedicate themselves to better understanding the incentives and behaviours of different agents. But I’m not convinced by the route to that conclusion.

Exploring the impact of adding a respiratory dimension to the EQ-5D-5L. Medical Decision Making [PubMed] Published 16th May 2019

I’m currently working on a project to develop and test EQ-5D bolt-ons for cognition and vision, so I was keen to see the methods reported in this study. The EQ-5D-5L has been shown to have only a weak correlation with clinically-relevant changes in the context of respiratory disease, so it might be worth developing a bolt-on (or multiple bolt-ons) that describe relevant functional changes not captured by the core dimensions of the EQ-5D. In this study, the authors looked at how the inclusion of respiratory dimensions influenced utility values.

Relevant disease-specific outcome measures were reviewed. The researchers also analysed EQ-5D-3L data and disease-specific outcome measure data from three clinical studies in asthma and COPD, to see how much variance in visual analogue scores was explained by disease-specific items. The selection of potential bolt-ons was also informed by principal-component analysis to try to identify which items form constructs distinct from the EQ-5D dimensions. The conclusion of this process was that two other dimensions represented separate constructs and could be good candidates for bolt-ons: ‘limitations in physical activities due to shortness of breath’ and ‘breathing problems’. Some think-aloud interviews were conducted to ensure that the bolt-ons made sense to patients and the general public.

A valuation study using time trade-off and discrete choice experiments was conducted in the Netherlands with a representative sample of 430 people from the general public. The sample was split in two, with each half completing the EQ-5D-5L with one or the other bolt-on. The Dutch EQ-5D-5L valuation study was used as a comparator data set. The inclusion of the bolt-ons seemed to extend the scale of utility values; the best-functioning states were associated with higher utility values when the bolt-ons were added and the worst-functioning states were associated with lower values. This was more pronounced for the ‘breathing problems’ bolt-on. The size of the coefficients on the two bolt-ons (i.e. the effect on utility values) was quite different. The ‘physical activities’ bolt-on had coefficients similar in size to self-care and usual activities. The coefficients on the ‘breathing problems’ bolt-on were a bit larger, comparable in size with those of the mobility dimension.

The authors raise an interesting question in light of their findings from the development process, in which the quantitative analysis supported a ‘symptoms’ dimension and patients indicated the importance of a dimension relating to ‘physical activities’. They ask whether it is more important for an item to be relevant or for it to be quantitatively important for valuation. Conceptually, it seems to me that the apparent added value of a ‘physical activity’ bolt-on is problematic for the EQ-5D. The ‘physical activity’ bolt-on specifies “climbing stairs, going for a walk, carrying things, gardening” as the types of activities it is referring to. Surely, these should be reflected in ‘mobility’ and ‘usual activities’. If they aren’t then I think the ‘usual activities’ descriptor, in particular, is not doing its job. What we might be seeing here, more than anything, is the flaws in the development process for the original EQ-5D descriptors. Namely, that they didn’t give adequate consideration to the people who would be filling them in. Nevertheless, it looks like a ‘breathing problems’ bolt-on could be a useful part of the EuroQol armoury.

Technology and college student mental health: challenges and opportunities. Frontiers in Psychiatry [PubMed] Published 15th April 2019

Universities in the UK and elsewhere are facing growing demand for counselling services from students. That’s probably part of the reason that our Student Mental Health Research Network was funded. Some researchers have attributed this rising demand to the use of personal computing technologies – smartphones, social media, and the like. No doubt, their use is correlated with mental health problems, certainly through time and probably between individuals. But causality is uncertain, and there are plenty of ways in which – as set out in this article – these technologies might be used in a positive way.

Most obviously, smartphones can be a platform for mental health programmes, delivered via apps. This is particularly important because there are perceived and actual barriers for students to accessing face-to-face support. This is an issue for all people with mental health problems. But the opportunity to address this issue using technology is far greater for students, who are hyper-connected. Part of the problem, the authors argue, is that there has not been a focus on implementation, and so the evidence that does exist is from studies with self-selecting samples. Yet the opportunity is great here, too, because students are often co-located with service providers and already engaged with course-related software.

Challenges remain with respect to ethics, privacy, accountability, and duty of care. In the UK, we have the benefit of being able to turn to GDPR for guidance, and universities are well-equipped to assess the suitability of off-the-shelf and bespoke services in terms of their ethical implications. The authors outline some possible ways in which universities can approach implementation and the challenges therein. Adopting these approaches will be crucial if universities are to address the current gap between the supply and demand for services.

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Chris Sampson’s journal round-up for 20th May 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.

A new method to determine the optimal willingness to pay in cost-effectiveness analysis. Value in Health Published 17th May 2019

Efforts to identify a robust estimate of the willingness to pay for a QALY have floundered. Mostly, these efforts have relied on asking people about their willingness to pay. In the UK, we have moved away from using such estimates as a basis for setting cost-effectiveness thresholds in the context of resource allocation decisions. Instead, we have attempted to identify the opportunity cost of a QALY, which is perhaps even more difficult, but more easy to justify in the context of a fixed budget. This paper seeks to inject new life into the willingness-to-pay approach by developing a method based on relative risk aversion.

The author outlines the relationship between relative risk aversion and the rate at which willingness-to-pay changes with income. Various candidate utility functions are described with respect to risk preferences, with a Weibull function being adopted for this framework. Estimates of relative risk aversion have been derived from numerous data sources, including labour supply, lottery experiments, and happiness surveys. These estimates from the literature are used to demonstrate the relationship between relative risk aversion and the ‘optimal’ willingness to pay (K), calibrated using the Weibull utility function. For an individual with ‘representative’ parameters plugged into their utility function, K is around twice the income level. K always increases with relative risk aversion.

Various normative questions are raised, including whether a uniform K should be adopted for everybody within the population, and whether individuals should be able to spend on health care on top of public provision. This approach certainly appears to be more straightforward than other approaches to estimating willingness-to-pay in health care, and may be well-suited to decentralised (US-style) resource allocation decision-making. It’s difficult to see how this framework could gain traction in the UK, but it’s good to see alternative approaches being proposed and I hope to see this work developed further.

Striving for a societal perspective: a framework for economic evaluations when costs and effects fall on multiple sectors and decision makers. Applied Health Economics and Health Policy [PubMed] Published 16th May 2019

I’ve always been sceptical of a ‘societal perspective’ in economic evaluation, and I have written in favour of a limited health care perspective. This is mostly for practical reasons. Being sufficiently exhaustive to identify a truly ‘societal’ perspective is so difficult that, in attempting to do so, there is a very high chance that you will produce estimates that are so inaccurate and imprecise that they are more dangerous than useful. But the fact is that there is no single decision-maker when it comes to public expenditure. Governments are made up of various departments, within which there are many levels and divisions. Not everybody will care about the health care perspective, so other objectives ought to be taken into account.

The purpose of this paper is to build on the idea of the ‘impact inventory’, described by the Second Panel on Cost-Effectiveness in Health and Medicine, which sought to address the challenge of multiple objectives. The extended framework described in this paper captures effects and opportunity costs associated with an intervention within various dimensions. These dimensions could (or should) align with decision-makers’ objectives. Trade-offs invariably require aggregation, and this aggregation could take place either within individuals or within dimensions – something not addressed by the Second Panel. The authors describe the implications of each approach to aggregation, providing visual representations of the impact inventory in each case. Aggregating within individuals requires a normative judgement about how each dimension is valued to the individual and then a judgement about how to aggregate for overall population net benefit. Aggregating across individuals within dimensions requires similar normative judgements. Where the chosen aggregation functions are linear and additive, both approaches will give the same results. But as soon as we start to consider equity concerns or more complex aggregation, we’ll see different decisions being indicated.

The authors adopt an example used by the Second Panel to demonstrate the decisions that would be made within a health-only perspective and then decisions that consider other dimensions. There could be a simple extension beyond health, such as including the impact on individuals’ consumption of other goods. Or it could be more complex, incorporating multiple dimensions, sectors, and decision-makers. For the more complex situation, the authors consider the inclusion of the criminal justice sector, introducing the number of crimes averted as an object of value.

It’s useful to think about the limitations of the Second Panel’s framing of the impact inventory and to make explicit the normative judgements involved. What this paper seems to be saying is that cross-sector decision-making is too complex to be adequately addressed by the Second Panel’s impact inventory. The framework described in this paper may be too abstract to be practically useful, and too vague to be foundational. But the complexities and challenges in multi-sector economic evaluation need to be spelt out – there is no simple solution.

Advanced data visualisation in health economics and outcomes research: opportunities and challenges. Applied Health Economics and Health Policy [PubMed] Published 4th May 2019

Computers can make your research findings look cool, which can help make people pay attention. But data visualisation can also be used as part of the research process and provide a means of more intuitively (and accurately) communicating research findings. The data sets used by health economists are getting bigger, which provides more opportunity and need for effective visualisation. The authors of this paper suggest that data visualisation techniques could be more widely adopted in our field, but that there are challenges and potential pitfalls to consider.

Decision modelling is an obvious context in which to use data visualisation, because models tend to involve large numbers of simulations. Dynamic visualisations can provide a means by which to better understand what is going on in these simulations, particularly with respect to uncertainty in estimates associated with alternative model structures or parameters. If paired with interactive models and customised dashboards, visualisation can make complex models accessible to non-expert users. Communicating patient outcomes data is also highlighted as a potential application, aiding the characterisation of differences between groups of individuals and alternative outcome measures.

Yet, there are barriers to wider use of visualisation. There is some scepticism about bias in underlying analyses, and end users don’t want to be bamboozled by snazzy graphics. The fact that journal articles are still the primary mode of communicating research findings is a problem, as you can’t have dynamic visualisations in a PDF. There’s also a learning curve for analysts wishing to develop complex visualisations. Hopefully, opportunities will be identified for two-way learning between the health economics world and data scientists more accustomed to data visualisation.

The authors provide several examples (static in the publication, but with links to live tools), to demonstrate the types of visualisations that can be created. Generally speaking, complex visualisations are proposed as complements to our traditional presentations of results, such as cost-effectiveness acceptability curves, rather than as alternatives. The key thing is to maintain credibility by ensuring that data visualisation is used to describe data in a more accurate and meaningful way, and to avoid exaggeration of research findings. It probably won’t be long until we see a set of good practice guidelines being developed for our field.

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Rita Faria’s journal round-up for 28th January 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.

Appraising the value of evidence generation activities: an HIV modelling study. BMJ Global Health [PubMed] Published 7th December 2018

How much should we spend on implementing our health care strategy versus getting more information to devise a better strategy? Should we devolve budgets to regions or administer the budget centrally? These are difficult questions and this new paper by Beth Woods et al has a brilliant stab at answering them.

The paper looks at the HIV prevention and treatment policies in Zambia. It starts by finding the most cost-effective strategy and the corresponding budget in each region, given what is currently known about the prevalence of the infection, the effectiveness of interventions, etc. The idea is that the regions receive a cost-effective budget to implement a cost-effective strategy. The issue is that the cost-effective strategy and budget are devised according to what we currently know. In practice, regions might face a situation on the ground which is different from what was expected. Regions might not have enough budget to implement the strategy or might have some leftover.

What if we spend some of the budget to get more information to make a better decision? This paper considers the value of perfect information given the costs of research. Depending on the size of the budget and the cost of research, it may be worthwhile to divert some funds to get more information. But what if we had more flexibility in the budgetary policy? This paper tests 2 more budgetary options: a national hard budget but with the flexibility to transfer funds from under- to overspending regions, and a regional hard budget with a contingency fund.

The results are remarkable. The best budgetary policy is to have a national budget with the flexibility to reallocate funds across regions. This is a fascinating paper, with implications not only for prioritisation and budget setting in LMICs but also for high-income countries. For example, the 2012 Health and Social Care Act broke down PCTs into smaller CCGs and gave them hard budgets. Some CCGs went into deficit, and there are reports that some interventions have been cut back as a result. There are probably many reasons for the deficit, but this paper shows that hard regional budgets clearly have negative consequences.

Health economics methods for public health resource allocation: a qualitative interview study of decision makers from an English local authority. Health Economics, Policy and Law [PubMed] Published 11th January 2019

Our first paper looked at how to use cost-effectiveness to allocate resources between regions and across health care services and research. Emma Frew and Katie Breheny look at how decisions are actually made in practice, but this time in a local authority in England. Another change of the 2012 Health and Social Care Act was to move public health responsibilities from the NHS to local authorities. Local authorities are now given a ring-fenced budget to implement cost-effective interventions that best match their needs. How do they make decisions? Thanks to this paper, we’re about to find out.

This paper is an enjoyable read and quite an eye-opener. It was startling that health economics evidence was not much used in practice. But the barriers that were cited are not insurmountable. And the suggestions by the interviewees were really useful. There were suggestions about how economic evaluations should consider the local context to get a fair picture of the impact of the intervention to services and to the population, and to move beyond the trial into the real world. Equity was mentioned too, as well as broadening the outcomes beyond health. Fortunately, the health economics community is working on many of these issues.

Lastly, there was a clear message to make economic evidence accessible to lay audiences. This is a topic really close to my heart, and something I’d like to help improve. We have to make our work easy to understand and use. Otherwise, it may stay locked away in papers rather than do what we intended it for. Which is, at least in my view, to help inform decisions and to improve people’s lives.

I found this paper reassuring in that there is clearly a need for economic evidence and a desire to use it. Yes, there are some teething issues, but we’re working in the right direction. In sum, the future for health economics is bright!

Survival extrapolation in cancer immunotherapy: a validation-based case study. Value in Health Published 13th December 2018

Often, the cost-effectiveness of cancer drugs hangs in the method to extrapolate overall survival. This is because many cancer drugs receive their marketing authorisation before most patients in the trial have died. Extrapolation is tested extensively in the sensitivity analysis, and this is the subject of many discussions in NICE appraisal committees. Ultimately, at the point of making the decision, the correct method to extrapolate is a known unknown. Only in hindsight can we know for sure what the best choice was.

Ash Bullement and colleagues take advantage of hindsight to know the best method for extrapolation of a clinical trial of an immunotherapy drug. Survival after treatment with immunotherapy drugs is more difficult to predict because some patients can survive for a very long time, while others have much poorer outcomes. They fitted survival models to the 3-year data cut, which was available at the time of the NICE technology appraisal. Then they compared their predictions to the observed survival in the 5-year data cut and to long-term survival trends from registry data. They found that the piecewise model and a mixture-cure model had the best predictions at 5 years.

This is a relevant paper for those of us who work in the technology appraisal world. I have to admit that I can be sceptical of piecewise and mixture-cure models, but they definitely have a role in our toolbox for survival extrapolation. Ideally, we’d have a study like this for all the technology appraisals hanging on the survival extrapolation so that we can take learnings across cancers and classes of drugs. With time, we would get to know more about what works best for which condition or drug. Ultimately, we may be able to get to a stage where we can look at the extrapolation with less inherent uncertainty.

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