Chris Sampson’s journal round-up for 13th January 2020

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 vision ‘bolt-on’ increases the responsiveness of EQ-5D: preliminary evidence from a study of cataract surgery. The European Journal of Health Economics [PubMed] Published 4th January 2020

The EQ-5D is insensitive to differences in how well people can see, despite this seeming to be an important aspect of health. In contexts where the impact of visual impairment may be important, we could potentially use a ‘bolt-on’ item that asks about a person’s vision. I’m working on the development of a vision bolt-on at the moment. But ours won’t be the first. A previously-developed bolt-on has undergone some testing and has been shown to be sensitive to differences between people with different levels of visual function. However, there is little or no evidence to support its responsiveness to changes in visual function, which might arise from treatment.

For this study, 63 individuals were recruited prior to receiving cataract surgery in Singapore. Participants completed the EQ-5D-3L and EQ-5D-5L, both with and without a vision bolt-on, which matched the wording of other EQ-5D dimensions. Additionally, the SF-6D, HUI3, and VF-12 were completed along with a LogMAR assessment of visual acuity. The authors sought to compare the responsiveness of the EQ-5D with a vision bolt-on compared with the standard EQ-5D and the other measures. Therefore, all measures were completed before and after cataract surgery. Preference weights can be generated for the EQ-5D-3L with a vision bolt-on, but they can’t for the EQ-5D-5L, so the authors looked at rescaled sum scores to compare across all measures. Responsiveness was measured using indicators such as standardised effect size and response mean.

Visual acuity changed dramatically before and after surgery, for almost everybody. The authors found that the vision bolt-on does seem to provide a great deal more in the way of response to this, compared to the EQ-5D without the bolt-on. For instance, the mean change in the EQ-5D-3L index score was 0.018 without the vision bolt-on, and 0.031 with it. The HUI3 came out with a mean change of 0.105 and showed the highest responsiveness across all analyses.

Does this mean that we should all be using a vision bolt-on, or perhaps the HUI3? Not exactly. Something I see a lot in papers of this sort – including in this one – is the framing of a “superior responsiveness” as an indication that the measure is doing a better job. That isn’t true if the measure is responding to things to which we don’t want it to respond. As the authors point out, the HUI3 has quite different foundations to the EQ-5D. We also don’t want a situation where analysts can pick and choose measures according to which ever is most responsive to the thing to which they want it to be most responsive. In EuroQol parlance, what goes into the descriptive system is very important.

The causal effect of social activities on cognition: evidence from 20 European countries. Social Science & Medicine Published 9th January 2020

Plenty of studies have shown that cognitive abilities are correlated with social engagement, but few have attempted to demonstrate causality in a large sample. The challenge, of course, is that people who engage in more social activities are likely to have greater cognitive abilities for other reasons, and people’s decision to engage in social activities might depend on their cognitive abilities. This study tackles the question of causality using a novel (to me, at least) methodology.

The analysis uses data from five waves of SHARE (the Survey of Health, Ageing and Retirement in Europe). Survey respondents are asked about whether they engage in a variety of social activities, such as voluntary work, training, sports, or community-related organisations. From this, the authors generate an indicator for people participating in zero, one, or two or more of these activities. The survey also uses a set of tests to measure people’s cognitive abilities in terms of immediate recall capacity, delayed recall capacity, fluency, and numeracy. The authors look at each of these four outcomes, with 231,407 observations for the first three and 124,381 for numeracy (for which the questions were missing from some waves). Confirming previous findings, a strong positive correlation is found between engagement in social activities and each of the cognition indicators.

The empirical strategy, which I had never heard of, is partial identification. This is a non-parametric method that identifies bounds for the average treatment effect. Thus, it is ‘partial’ because it doesn’t identify a point estimate. Fewer assumptions means wider and less informative bounds. The authors start with a model with no assumptions, for which the lower bound for the treatment effect goes below zero. They then incrementally add assumptions. These include i) a monotone treatment response, assuming that social participation does not reduce cognitive abilities on average; ii) monotone treatment selection, assuming that people who choose to be socially active tend to have higher cognitive capacities; iii) a monotone instrumental variable assumption that body mass index is negatively associated with cognitive abilities. The authors argue that their methodology is not likely to be undermined by unobservables, as previous studies might.

The various models show that engaging in social activities has a positive impact on all four of the cognitive indicators. The assumption of monotone treatment response had the highest identifying power. For all models that included this, the 95% confidence intervals in the estimates showed a statistically significant positive impact of social activities on cognition. What is perhaps most interesting about this approach is the huge amount of uncertainty in the estimates. Social activities might have a huge effect on cognition or they might have a tiny effect. A basic OLS-type model, assuming exogenous selection, provides very narrow confidence intervals, whereas the confidence intervals on the partial identification models are almost as wide as the lower and upper band themselves.

One shortcoming of this study for me is that it doesn’t seek to identify the causal channels that have been proposed in previous literature (e.g. loneliness, physical activity, self-care). So it’s difficult to paint a clear picture of what’s going on. But then, maybe that’s the point.

Do research groups align on an intervention’s value? Concordance of cost-effectiveness findings between the Institute for Clinical and Economic Review and other health system stakeholders. Applied Health Economics and Health Policy [PubMed] Published 10th January 2020

Aside from having the most inconvenient name imaginable, ICER has been a welcome edition to the US health policy scene, appraising health technologies in order to provide guidance on coverage. ICER has become influential, with some pharmacy benefit managers using their assessments as a basis for denying coverage for low value medicines. ICER identify technologies as falling in one of three categories – high, low, or intermediate long-term value – according to whether the ICER (grr) falls below, above, or between the threshold range of $50,000-$175,000 per QALY. ICER conduct their own evaluations, but so do plenty of other people. This study sought to find out whether other analyses in the literature agree with ICER’s categorisations.

The authors consider 18 assessments by ICER, including 76 interventions, between 2015 and 2017. For each of these, the authors searched the literature for other comparative studies. Specifically, they went looking for cost-effectiveness analyses that employed the same perspectives and outcomes. Unfortunately, they were only able to identify studies for six disease areas and 14 interventions (of the 76), across 25 studies. It isn’t clear whether this is because there is a lack of literature out there – which would be an interesting finding in itself – or because their search strategy or selection criteria weren’t up to scratch. Of the 14 interventions compared, 10 get a more favourable assessment in the published studies than in their corresponding ICER evaluations, with most being categorised as intermediate value instead of low value. The authors go on to conduct one case study, comparing an ICER evaluation in the context of migraine with a published study by some of the authors of this paper. There were methodological differences. In some respects, it seems as if ICER did a more thorough job, while in other respects the published study seemed to use more defensible assumptions.

I agree with the authors that these kinds of comparisons are important. Not least, we need to be sure that ICER’s approach to appraisal is valid. The findings of this study suggest that maybe ICER should be looking at multiple studies and combining all available data in a more meaningful way. But the authors excluded too many studies. Some imperfect comparisons would have been more useful than exclusion – 14 of 76 is kind of pitiful and probably not representative. And I’m not sure why the authors set out to identify studies that are ‘more favourable’, rather than just different. That perspective seems to reveal an assumption that ICER are unduly harsh in their assessments.

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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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Chris Sampson’s journal round-up for 1st April 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.

Toward a centralized, systematic approach to the identification, appraisal, and use of health state utility values for reimbursement decision making: introducing the Health Utility Book (HUB). Medical Decision Making [PubMed] Published 22nd March 2019

Every data point reported in research should be readily available to us all in a structured knowledge base. Most of us waste most of our time retreading old ground, meaning that we don’t have the time to do the best research possible. One instance of this is in the identification of health state utility values to plug into decision models. Everyone who builds a model in a particular context goes searching for utility values – there is no central source. The authors of this paper are hoping to put an end to that.

The paper starts with an introduction to the importance of health state utility values in cost-effectiveness analysis, which most of us don’t need to read. Of course, the choice of utility values in a model is very important and can dramatically alter estimates of cost-effectiveness. The authors also discuss issues around the identification of utility values and the assessment of their quality and applicability. Then we get into the objectives of the ‘Health Utility Book’, which is designed to tackle these issues.

The Health Utility Book will consist of a registry (I like registries), backed by a systematic approach to the identification and inclusion (registration?) of utility values. The authors plan to develop a quality assessment tool for studies that report utility values, using a Delphi panel method to identify appropriate indicators of quality to be included. The quality assessment tool will be complemented by a tool to assess applicability, which will be developed through interviews with stakeholders involved in the reimbursement process.

In the first place, the Health Utility Book will only compile utility values for cancer, and some of the funding for the project is cancer specific. To survive, the project will need more money from more sources. To be sustainable, the project will need to attract funding indefinitely. Or perhaps it could morph into a crowd-sourced platform. Either way, the Health Utility Book has my support.

A review of attitudes towards the reuse of health data among people in the European Union: the primacy of purpose and the common good. Health Policy Published 21st March 2019

We all agree that data protection is important. We all love the GDPR. Organisations such as the European Council and the OECD are committed to facilitating the availability of health data as a means of improving population health. And yet, there often seem to be barriers to accessing health data, and we occasionally hear stories of patients opposing data sharing (e.g. care.data). Maybe people don’t want researchers to be using their data, and we just need to respect that. Or, more likely, we need to figure out what it is that people are opposed to, and design systems that recognise this.

This study reviews research on attitudes towards the sharing of health data for purposes other than treatment, among people living in the EU, employing a ‘configurative literature synthesis’ (a new one for me). From 5,691 abstracts, 29 studies were included. Most related to the use of health data in research in general, while some focused on registries. A few studies looked at other uses, such as for planning and policy purposes. And most were from the UK.

An overarching theme was a low awareness among the population about the reuse of health data. However, in some studies, a desire to be better informed was observed. In general, views towards the use of health data were positive. But this was conditional on the data being used to serve the common good. This includes such purposes as achieving a better understanding of diseases, improving treatments, or achieving more efficient health care. Participants weren’t so happy with health data reuse if it was seen to conflict with the interests of patients providing the data. Commercialisation is a big concern, including the sale of data and private companies profiting from the data. Employers and insurance companies were also considered a threat to patients’ interests. There were conflicting views about whether it is positive for pharmaceutical companies to have access to health data. A minority of people were against sharing data altogether. Certain types of data are seen as being particularly sensitive, including those relating to mental health or sexual health. In general, people expressed concern about data security and the potential for leaks. The studies also looked at the basis for consent that people would prefer. A majority accepted that their data could be used without consent so long as the data were anonymised. But there were no clear tendencies of preference for the various consent models.

It’s important to remember that – on the whole – patients want their data to be used to further the common good. But support can go awry if the data are used to generate profits for private firms or used in a way that might be perceived to negatively affect patients.

Health-related quality of life in injury patients: the added value of extending the EQ-5D-3L with a cognitive dimension. Quality of Life Research [PubMed] Published 18th March 2019

I’m currently working on a project to develop a cognition ‘bolt-on’ for the EQ-5D. Previous research has demonstrated that a cognition bolt-on could provide additional information to distinguish meaningful differences between health states, and that cognition might be a more important candidate than other bolt-ons. Injury – especially traumatic brain injury – can be associated with cognitive impairments. This study explores the value of a cognition bolt-on in this context.

The authors sought to find out whether cognition is sufficiently independent of other dimensions, whether the impact of cognitive problems is reflected in the EuroQol visual analogue scale (EQ VAS), and how a cognition bolt-on affects the overall explanatory power of the EQ-5D-3L. The data used are from the Dutch Injury Surveillance System, which surveys people who have attended an emergency department with an injury, including EQ-5D-3L. The survey adds a cognitive bolt-on relating to memory and concentration.

Data were available for 16,624 people at baseline, with 5,346 complete responses at 2.5-month follow-up. The cognition item was the least affected, with around 20% reporting any problems (though it’s worth noting that the majority of the cohort had injuries to parts of the body other than the head). The frequency of different responses suggests that cognition is dominant over other dimensions in the sense that severe cognitive problems tend to be observed alongside problems in other dimensions, but not vice versa. The mean EQ VAS for people reporting severe cognitive impairment was 41, compared with a mean of 75 for those reporting no problems. Regression analysis showed that moderate and severe cognitive impairment explained 8.7% and 6.2% of the variance of the EQ VAS. Multivariate analysis suggested that the cognitive dimension added roughly the same explanatory power as any other dimension. This was across the whole sample. Interestingly (or, perhaps, worryingly) when the authors looked at the subset of people with traumatic brain injury, the explanatory power of the cognitive dimension was slightly lower than overall.

There’s enough in this paper to justify further research into the advantages and disadvantages of using a cognition bolt-on. But I would say that. Whether or not the bolt-on descriptors used in this study are meaningful to patients remains an open question.

Developing the role of electronic health records in economic evaluation. The European Journal of Health Economics [PubMed] Published 14th March 2019

One way that we can use patients’ routinely collected data is to support the conduct of economic evaluations. In this commentary, the authors set out some of the ways to make the most of these data and discuss some of the methodological challenges. Large datasets have the advantage of being large. When this is combined with the collection of sociodemographic data, estimates for sub-groups can be produced. The data can also facilitate the capture of outcomes not otherwise available. For example, the impact of bariatric surgery on depression outcomes could be identified beyond the timeframe of a trial. The datasets also have the advantage of being representative, where trials are not. This could mean more accurate estimates of costs and outcomes. But there are things to bear in mind when using the data, such as the fact that coding might not always be very accurate, and coding practices could vary between observations. Missing data are likely to be missing for a reason (i.e. not at random), which creates challenges for the analyst. I had hoped that this paper would discuss novel uses of routinely collected data systems, such as the embedding of economic evaluations within them, rather than simply their use to estimate parameters for a model. But if you’re just getting started with using routine data, I suppose you could do worse than start with this paper.

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