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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Thesis Thursday: Alastair Irvine

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 Alastair Irvine who has a PhD from the University of Aberdeen. If you would like to suggest a candidate for an upcoming Thesis Thursday, get in touch.

Title
Time preferences and the patient-doctor interaction
Supervisors
Marjon van der Pol, Euan Phimister
Repository link
http://digitool.abdn.ac.uk/webclient/DeliveryManager?pid=238373

How can people’s time preferences affect the way they use health care?

Time preferences are a way of thinking about how people choose between things that happen over time. Some people prefer a treatment with large side effects and a long chain of future benefits; others prefer smaller benefits but less side effects. These influence a wide range of health outcomes and decisions. One of the most interesting questions I had coming into the PhD was around non-adherence.

Non-adherence can’t be captured by ‘standard’ exponential time preferences because there is no way for something you prefer now to be ‘less preferred’ in the future if everything is held constant. Instead, present-bias preferences can capture non-adherent behaviour. With these preferences, people place a higher weight on the ‘current period’ relative to all future periods but weight all future periods consistently. What that means is you can have a situation where you plan to do something – eat healthily, take your medication – but end up not doing it. When planning, you placed less relative weight on the near term ‘cost’ (like medication side effects) than you do when the decision arrives.

In what way might the patient-doctor interaction affect a patient’s adherence to treatment?

There’s asymmetric information between doctors and patient, leading to an agency relationship. Doctors in general know more about treatment options than patients, and don’t know their patient’s preferences. So if doctors are making recommendations to patients, this asymmetry can lead to recommendations that are accepted by the patient but not adhered to. For example, present-biased patients accept the same treatments as exponential discounters. Depending on the treatment parameters, present-biased people will not adhere to some treatments. If the doctor doesn’t anticipate this when making recommendations, it leads to non-adherence.

One of the issues from a contracting perspective is that naive present-bias people don’t anticipate their own non-adherence, so we can’t write traditional ‘separating contracts’ that lead present-bias people to one treatment and exponential discounters to another. However, if the doctor can offer a lower level of treatment to all patients – one that has less side effects and a concomitantly lower benefit – then everyone sticks to that treatment. This clearly comes at the expense of the exponential discounters’ health, but if the proportion of present-bias is high enough it can be an efficient outcome.

Were you able to compare the time preferences of patients and of doctors?

Not this time! It had been the ‘grand plan’ at the start of the PhD to compare matched doctor and patient time preferences then link it to treatment choices but that was far too ambitious for the time, and there had been very little work establishing how time preferences work in the patient-doctor interaction so I felt we had a lot to do.

One interesting question we did ask was whether doctors’ time preferences for themselves were the same as for their patients. A lot of the existing evidence asks doctors for their own time preferences, but surely the important time preference is the one they apply to their patients?

We found that while there was little difference between these professional and private time preferences, a lot of the responses displayed increasing impatience. This means that as the start of treatment gets pushed further into the future, doctors started to prefer shorter-but-sooner benefits for themselves and their patients. We’re still thinking about whether this reflects that in the real world (outside the survey) doctors already account for the time patients have spent with symptoms when assessing how quickly a treatment benefit should arrive.

How could doctors alter their practice to reduce non-adherence?

We really only have two options – to make ‘the right thing’ easier or the ‘wrong thing’ more costly. The implication of present-bias is you need to use less intense treatments because the problem is the (relative) over-weighting of the side effects. The important thing we need for that is good information on adherence.

We could pay people to adhere to treatment. However, my gut feeling is that payments are hard to implement on the patient side without being coercive (e.g making non-adherence costly with charges) or expensive for the implementer when identification of completion is tricky (giving bonuses to doctors based on patient health outcomes). So doctors can reduce non-adherence by anticipating it, and offering less ‘painful’ treatments.

It’s important to say I was only looking at one kind of non-adherence. If patients have bad experiences then whatever we do shouldn’t keep them taking a treatment they don’t want. However, the fact that stopping treatment is always an option for the patient makes non-adherence hard to address because as an economist you would like to separate different reasons for stopping. This is a difficulty for analysing non-adherence as a problem of temptation. In temptation preferences we would like to change the outcome set so that ‘no treatment’ is not a tempting choice, but there are real ethical and practical difficulties with that.

To what extent did the evidence generated by your research support theoretical predictions?

I designed a lab experiment that put students in the role of the doctor with patients that may or may not be present-biased. The participants had to recommend treatments to a series of hypothetical patients and was set up so that adapting to non-adherence with less intense treatments was best. Participants got feedback on their previous patients, to learn about which treatments patients stuck to over the rounds.

We paid one arm a salary, and another a ‘performance payment’. The latter only got paid when patients stuck to treatment and the pay correlated with the patient outcomes. In both arms, patients’ outcomes were reflected with a charity donation.

The main result is that there was a lot of adaptation to non-adherence in both arms. The adaptation was stronger under the performance payment, reflecting the upper limit of the adaptation we can expect because it perfectly aligns patient and doctor preferences.

In the experimental setting, even when there is no direct financial benefit of doing so, participants adapted to non-adherence in the way I predicted.

Chris Sampson’s journal round-up for 2nd July 2018

Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Choice in the presence of experts: the role of general practitioners in patients’ hospital choice. Journal of Health Economics [PubMed] [RePEc] Published 26th June 2018

In the UK, patients are in principle free to choose which hospital they use for elective procedures. However, as these choices operate through a GP referral, the extent to which the choice is ‘free’ is limited. The choice set is provided by the GP and thus there are two decision-makers. It’s a classic example of the principal-agent relationship. What’s best for the patient and what’s best for the local health care budget might not align. The focus of this study is on the applied importance of this dynamic and the idea that econometric studies that ignore it – by looking only at patient decision-making or only at GP decision-making – may give bias estimates. The author outlines a two-stage model for the choice process that takes place. Hospital characteristics can affect choices in three ways: i) by only influencing the choice set that the GP presents to the patient, e.g. hospital quality, ii) by only influencing the patient’s choice from the set, e.g. hospital amenities, and iii) by influencing both, e.g. waiting times. The study uses Hospital Episode Statistics for 30,000 hip replacements that took place in 2011/12, referred by 4,721 GPs to 168 hospitals, to examine revealed preferences. The choice set for each patient is not observed, so a key assumption is that all hospitals to which a GP made referrals in the period are included in the choice set presented to patients. The main findings are that both GPs and patients are influenced primarily by distance. GPs are influenced by hospital quality and the budget impact of referrals, while distance and waiting times explain patient choices. For patients, parking spaces seem to be more important than mortality ratios. The results support the notion that patients defer to GPs in assessing quality. In places, it’s difficult to follow what the author did and why they did it. But in essence, the author is looking for (and in most cases finding) reasons not to ignore GPs’ preselection of choice sets when conducting econometric analyses involving patient choice. Econometricians should take note. And policymakers should be asking whether freedom of choice is sensible when patients prioritise parking and when variable GP incentives could give rise to heterogeneous standards of care.

Using evidence from randomised controlled trials in economic models: what information is relevant and is there a minimum amount of sample data required to make decisions? PharmacoEconomics [PubMed] Published 20th June 2018

You’re probably aware of the classic ‘irrelevance of inference’ argument. Statistical significance is irrelevant in deciding whether or not to fund a health technology, because we ought to do whatever we expect to be best on average. This new paper argues the case for irrelevance in other domains, namely multiplicity (e.g. multiple testing) and sample size. With a primer on hypothesis testing, the author sets out the regulatory perspective. Multiplicity inflates the chance of a type I error, so regulators worry about it. That’s why triallists often obsess over primary outcomes (and avoiding multiplicity). But when we build decision models, we rely on all sorts of outcomes from all sorts of studies, and QALYs are never the primary outcome. So what does this mean for reimbursement decision-making? Reimbursement is based on expected net benefit as derived using decision models, which are Bayesian by definition. Within a Bayesian framework of probabilistic sensitivity analysis, data for relevant parameters should never be disregarded on the basis of the status of their collection in a trial, and it is up to the analyst to properly specify a model that properly accounts for the effects of multiplicity and other sources of uncertainty. The author outlines how this operates in three settings: i) estimating treatment effects for rare events, ii) the number of trials available for a meta-analysis, and iii) the estimation of population mean overall survival. It isn’t so much that multiplicity and sample size are irrelevant, as they could inform the analysis, but rather that no data is too weak for a Bayesian analyst.

Life satisfaction, QALYs, and the monetary value of health. Social Science & Medicine [PubMed] Published 18th June 2018

One of this blog’s first ever posts was on the subject of ‘the well-being valuation approach‘ but, to date, I don’t think we’ve ever covered a study in the round-up that uses this method. In essence, the method is about estimating trade-offs between (for example) income and some measure of subjective well-being, or some health condition, in order to estimate the income equivalence for that state. This study attempts to estimate the (Australian) dollar value of QALYs, as measured using the SF-6D. Thus, the study is a rival cousin to the Claxton-esque opportunity cost approach, and a rival sibling to stated preference ‘social value of a QALY’ approaches. The authors are trying to identify a threshold value on the basis of revealed preferences. The analysis is conducted using 14 waves of the Australian HILDA panel, with more than 200,000 person-year responses. A regression model estimates the impact on life satisfaction of income, SF-6D index scores, and the presence of long-term conditions. The authors adopt an instrumental variable approach to try and address the endogeneity of life satisfaction and income, using an indicator of ‘financial worsening’ to approximate an income shock. The estimated value of a QALY is found to be around A$42,000 (~£23,500) over a 2-year period. Over the long-term, it’s higher, at around A$67,000 (~£37,500), because individuals are found to discount money differently to health. The results also demonstrate that individuals are willing to pay around A$2,000 to avoid a long-term condition on top of the value of a QALY. The authors apply their approach to a few examples from the literature to demonstrate the implications of using well-being valuation in the economic evaluation of health care. As with all uses of experienced utility in the health domain, adaptation is a big concern. But a key advantage is that this approach can be easily applied to large sets of survey data, giving powerful results. However, I haven’t quite got my head around how meaningful the results are. SF-6D index values – as used in this study – are generated on the basis of stated preferences. So to what extent are we measuring revealed preferences? And if it’s some combination of stated and revealed preference, how should we interpret willingness to pay values?

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