Chris Sampson’s journal round-up for 18th November 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 conceptual map of health-related quality of life dimensions: key lessons for a new instrument. Quality of Life Research [PubMed] Published 1st November 2019

EQ-5D, SF-6D, HUI3, AQoL, 15D; they’re all used to describe health states for the purpose of estimating health state utility values, to get the ‘Q’ in the QALY. But it’s widely recognised (and evidenced) that they measure different things. This study sought to better understand the challenge by doing two things: i) ‘mapping’ the domains of the different instruments and ii) advising on the domains to be included in a new measure.

The conceptual model described in this paper builds on two standard models of health – the ICF (International Classification of Functioning, Disability, and Health), which is endorsed by the WHO, and the Wilson and Cleary model. The new model is built around four distinctions, which can be used to define the dimensions included in health state utility instruments: cause vs effect, specific vs broad, physical vs psychological, and subjective vs objective. The idea is that each possible dimension of health can relate, with varying levels of precision, to one or the other of these alternatives.

The authors argue that, conveniently, cause/effect and specific/broad map to one another, as do physical/psychological and objective/subjective. The framework is presented visually, which makes it easy to interpret – I recommend you take a look. Each of the five instruments previously mentioned is mapped to the framework, with the HUI and 15D coming out as ‘symptom’ oriented, EQ-5D and SF-6D as ‘functioning’ oriented, and the AQoL as a hybrid of a health and well-being instrument. Based (it seems) on the Personal Wellbeing Index, the authors also include two social dimensions in the framework, which interact with the health domains. Based on the frequency with which dimensions are included in existing instruments, the authors recommend that a new measure should include three physical dimensions (mobility, self-care, pain), three mental health dimensions (depression, vitality, sleep), and two social domains (personal relationships, social isolation).

This framework makes no sense to me. The main problem is that none of the four distinctions hold water, let alone stand up to being mapped linearly to one another. Take pain as an example. It could be measured subjectively or objectively. It’s usually considered a physical matter, but psychological pain is no less meaningful. It may be a ‘causal’ symptom, but there is little doubt that it matters in and of itself as an ‘effect’. The authors themselves even offer up a series of examples of where the distinctions fall down.

It would be nice if this stuff could be drawn-up on a two-dimensional plane, but it isn’t that simple. In addition to oversimplifying complex ideas, I don’t think the authors have fully recognised the level of complexity. For instance, the work seems to be inspired – at least in part – by a desire to describe health state utility instruments in relation to subjective well-being (SWB). But the distinction between health state utility instruments and SWB isn’t simply a matter of scope. Health state utility instruments (as we use them) are about valuing states in relation to preferences, whereas SWB is about experienced utility. That’s a far more important and meaningful distinction than the distinction between symptoms and functioning.

Careless costs related to inefficient technology used within NHS England. Clinical Medicine Journal [PubMed] Published 8th November 2019

This little paper – barely even a single page – was doing the rounds on Twitter. The author was inspired by some frustration in his day job, waiting for the IT to work. We can all relate to that. This brief analysis sums the potential costs of what the author calls ‘careless costs’, which is vaguely defined as time spent by an NHS employee on activity that does not relate to patient care. Supposing that all doctors in the English NHS wasted an average of 10 minutes per day on such activities, it would cost over £143 million (per year, I assume) based on current salaries. The implication is that a little bit of investment could result in massive savings.

This really bugs me, for at least two reasons. First, it is normal for anybody in any profession to have a bit of downtime. Nobody operates at maximum productivity for every minute of every day. If the doctor didn’t have their downtime waiting for a PC to boot, it would be spent queuing in Costa, or having a nice relaxed wee. Probably both. Those 10 minutes that are displaced cannot be considered equivalent in value to 10 minutes of patient contact time. The second reason is that there is no intervention that can fix this problem at little or no cost. Investments cost money. And if perfect IT systems existed, we wouldn’t all find these ‘careless costs’ so familiar. No doubt, the NHS lags behind, but the potential savings of improvement may very well be closer to zero than to the estimates in this paper.

When it comes to clinical impacts, people insist on being able to identify causal improvements from clearly defined interventions or changes. But when it comes to costs, too many people are confident in throwing around huge numbers of speculative origin.

Socioeconomic disparities in unmet need for student mental health services in higher education. Applied Health Economics and Health Policy [PubMed] Published 5th November 2019

In many countries, the size of the student population is growing, and this population seems to have a high level of need for mental health services. There are a variety of challenges in this context that make it an interesting subject for health economists to study (which is why I do), including the fact that universities are often the main providers of services. If universities are going to provide the right services and reach the right people, a better understanding of who needs what is required. This study contributes to this challenge.

The study is set in the context of higher education in Ireland. If you have no idea how higher education is organised in Ireland, and have an interest in mental health, then the Institutional Context section of this paper is worth reading in its own right. The study reports on findings from a national survey of students. This analysis is a secondary analysis of data collected for the primary purpose of eliciting students’ preferences for counselling services, which has been described elsewhere. In this paper, the authors report on supplementary questions, including measures of psychological distress and use of mental health services. Responses from 5,031 individuals, broadly representative of the population, were analysed.

Around 23% of respondents were classified as having unmet need for mental health services based on them reporting both a) severe distress and b) not using services. Arguably, it’s a sketchy definition of unmet need, but it seems reasonable for the purpose of this analysis. The authors regress this binary indicator of unmet need on a selection of sociodemographic and individual characteristics. The model is also run for the binary indicator of need only (rather than unmet need).

The main finding is that people from lower social classes are more likely to have unmet need, but that this is only because these people have a higher level of need. That is, people from less well-off backgrounds are more likely to have mental health problems but are no less likely to have their need met. So this is partly good news and partly bad news. It seems that there are no additional barriers to services in Ireland for students from a lower social class. But unmet need is still high and – with more inclusive university admissions – likely to grow. Based on the analyses, the authors recommend that universities could reach out to male students, who have greater unmet need.

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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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