Chris Sampson’s journal round-up for 25th March 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.

How prevalent are implausible EQ-5D-5L health states and how do they affect valuation? A study combining quantitative and qualitative evidence. Value in Health Published 15th March 2019

The EQ-5D-5L is able to describe a lot of different health states (3,125, to be precise), including some that don’t seem likely to ever be observed. For example, it’s difficult to conceive of somebody having extreme problems in pain/discomfort and anxiety/depression while also having no problems with usual activities. Valuation studies exclude these kinds of states because it’s thought that their inclusion could negatively affect the quality of the data. But there isn’t much evidence to help us understand how ‘implausibility’ might affect valuations, or which health states are seen as implausible.

This study is based on an EQ-5D-5L valuation exercise with 890 students in China. The valuation was conducted using the EQ VAS, rather than the standard EuroQol valuation protocol, with up to 197 states being valued by each student. Two weeks after conducting the valuation, participants were asked to indicate (yes or no) whether or not the states were implausible. After that, a small group were invited to participate in a focus group or interview.

No health state was unanimously identified as implausible. Only four states were unanimously rated as not being implausible. 910 of the 3,125 states defined by the EQ-5D-5L were rated implausible by at least half of the people who rated them. States more commonly rated as implausible were of moderate severity overall, but with divergent severities between states (i.e. 5s and 1s together). Overall, implausibility was associated with lower valuations.

Four broad themes arose from the qualitative work, namely i) reasons for implausibility, ii) difficulties in valuing implausible states, iii) strategies for valuing implausible states, and iv) values of implausible states. Some states were considered to have logical conflicts, with some dimensions being seen as mutually inclusive (e.g. walking around is a usual activity). The authors outline the themes and sub-themes, which are a valuable contribution to our understanding of what people think when they complete a valuation study.

This study makes plain the fact that there is a lot of heterogeneity in perceptions of implausibility. But the paper doesn’t fully address the issue of what plausibility actually means. The authors describe it as subjective. I’m not sure about that. For me, it’s an empirical question. If states are observed in practice, they are plausible. We need meaningful valuations of states that are observed, so perhaps the probability of a state being included in a valuation exercise should correspond to the probability of it being observed in reality. The difficulty of valuing a state may relate to plausibility – as this work shows – but that difficulty is a separate issue. Future research on implausible health states should be aligned with research on respondents’ experience of health states. Individuals’ judgments about the plausibility of health states (and the accuracy of those judgments) will depend on individuals’ experience.

An EU-wide approach to HTA: an irrelevant development or an opportunity not to be missed? The European Journal of Health Economics [PubMed] Published 14th March 2019

The use of health technology assessment is now widespread across the EU. The European Commission recently saw an opportunity to rationalise disparate processes and proposed new regulation for cooperation in HTA across EU countries. In particular, the proposal targets cooperation in the assessment of the relative effectiveness of pharmaceuticals and medical devices. A key purpose is to reduce duplication of efforts, but it should also make the basis for national decision-making more consistent.

The authors of this editorial argue that the regulation needs to provide more clarity, in the definition of clinical value, and of the quality of evidence that is acceptable, which vary across EU Member States. There is also a need for the EU to support early dialogue and scientific advice. There is also scope to support the generation and use of real-world evidence. The authors also argue that the challenges for medical device assessment are particularly difficult because many medical device companies cannot – or are not incentivised to – generate sufficient evidence for assessment.

As the final paragraph argues, EU cooperation in HTA isn’t likely to be associated with much in the way of savings. This is because appraisals will still need to be conducted in each country, as well as an assessment of country-specific epidemiology and other features of the population. The main value of cooperation could be in establishing a stronger position for the EU in negotiating in matters of drug design and evidence requirements. Not that we needed any more reasons to stop Brexit.

Patient-centered item selection for a new preference-based generic health status instrument: CS-Base. Value in Health Published 14th March 2019

I do not believe that we need a new generic measure of health. This paper was always going to have a hard time convincing me otherwise…

The premise for this work is that generic preference-based measures of health (such as the EQ-5D) were not developed with patients. True. So the authors set out to create one that is. A key feature of this study is the adoption of a framework that aligns with the multiattribute preference response model, whereby respondents rate their own health state relative to another. This is run through a mobile phone app.

The authors start by extracting candidate items from existing health frameworks and generic measures (which doesn’t seem to be a particularly patient-centred approach) and some domains were excluded for reasons that are not at all clear. 47 domains were included after overlapping candidates were removed. The 47 were classified as physical, mental, social, or ‘meta’. An online survey was conducted by a market research company. 2,256 ‘patients’ (people with diseases or serious complaints) were asked which 9 domains they thought were most important. Why 9? Because the authors figured it was the maximum that could fit on the screen of a mobile phone.

Of the candidate items, 5 were regularly selected in the survey: pain, personal relationships, fatigue, memory, and vision. Mobility and daily activities were also judged important enough to be included. Independence and self-esteem were added as paired domains and hearing was paired with the vision domain. The authors also added anxiety/depression as a pair of domains because they thought it was important. Thus, 12 items were included altogether, of which 6 were parts of pairs. Items were rephrased according to the researchers’ preferences. Each item was given 4 response levels.

It is true to say (as the authors do) that most generic preference-based measures (most notably the EQ-5D) were not developed with direct patient input. The argument goes that this somehow undermines the measure. But there are a) plenty of patient-centred measures for which preference-based values could be created and b) plenty of ways in which existing measures can be made patient-centred post hoc (n.b. our bolt-on study).

Setting aside my scepticism about the need for a new measure, I have a lot of problems with this study and with the resulting CS-Base instrument. The defining feature of its development seems to be arbitrariness. The underlying framework (as far as it is defined) does not seem well-grounded. The selection of items was largely driven by researchers. The wording was entirely driven by the researchers. The measure cannot justifiably be called ‘patient-centred’. It is researcher-centred, even if the researchers were able to refer to a survey of patients. And the whole thing has nothing whatsoever to do with preferences. The measure may prove fantastic at capturing health outcomes, but if it does it will be in spite of the methods used for its development, not because of them. Ironically, that would be a good advert for researcher-centred outcome development.

Proximity to death and health care expenditure increase revisited: a 15-year panel analysis of elderly persons. Health Economics Review [PubMed] [RePEc] Published 11th March 2019

It is widely acknowledged that – on average – people incur a large proportion of their lifetime health care costs in the last few years of their life. But there’s still a question mark over whether it is proximity to death that drives costs or age-related morbidity. The two have very different implications – we want people to be living for longer, but we probably don’t want them to be dying for longer. There’s growing evidence that proximity to death is very important, but it isn’t clear how important – if at all – ageing is. It’s important to understand this, particularly in predicting the impacts of demographic changes.

This study uses Swiss health insurance claims data for around 104,000 people over the age of 60 between 1996 and 2011. Two-part regression models were used to estimate health care expenditures conditional on them being greater than zero. The author analysed both birth cohorts and age classes to look at age-associated drivers of health care expenditure.

As expected, health care expenditures increased with age. The models imply that proximity-to-death has grown in importance over time. For the 1931-35 birth cohort, for example, the proportion of expenditures explained by proximity-to-death rose from 19% to 31%. Expenditures were partly explained by morbidity, and this effect appeared to be relatively constant over time. Thus, proximity to death is not the only determinant of rising expenditures (even if it is an important one). Looking at different age classes over time, there was no clear picture in the trajectory of health care expenditures. For the oldest age groups (76-85), health care expenditures were growing, but for some of the younger groups, costs appeared to be decreasing over time. This study paints a complex picture of health care expenditures, calling for complex policy responses. Part of this could be supporting people to commence palliative care earlier, but there is also a need for more efficient management of chronic illness over the long term.

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Chris Sampson’s journal round-up for 4th February 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.

Patient choice and provider competition – quality enhancing drivers in primary care? Social Science & Medicine Published 29th January 2019

There’s no shortage of studies in economics claiming to identify the impact (or lack of impact) of competition in the market for health care. The evidence has brought us close to a consensus that greater competition might improve quality, so long as providers don’t compete on price. However, many of these studies aren’t able to demonstrate the mechanism through which competition might improve quality, and the causality is therefore speculative. The research reported in this article was an attempt to see whether the supposed mechanisms for quality improvement actually exist. The authors distinguish between the demand-side mechanisms of competition-increasing quality-improving reforms (i.e. changes in patient behaviour) and the supply-side mechanisms (i.e. changes in provider behaviour), asserting that the supply-side has been neglected in the research.

The study is based on primary care in Sweden’s two largest cities, where patients can choose their primary care practice, which could be a private provider. Key is the fact that patients can switch between providers as often as they like, and with fewer barriers to doing so than in the UK. Prospective patients have access to some published quality indicators. With the goal of maximum variation, the researchers recruited 13 primary health care providers for semi-structured interviews with the practice manager and (in most cases) one or more of the practice GPs. The interview protocol included questions about the organisation of patient visits, information received about patients’ choices, market situation, reimbursement, and working conditions. Interview transcripts were coded and a framework established. Two overarching themes were ‘local market conditions’ and ‘feedback from patient choice’.

Most interviewees did not see competitors in the local market as a threat – conversely, providers are encouraged to cooperate on matters such as public health. Where providers did talk about competing, it was in terms of (speed of) access for patients, or in competition to recruit and keep staff. None of the interviewees were automatically informed of patients being removed from their list, and some managers reported difficulties in actually knowing which patients on their list were still genuinely on it. Even where these data were more readily available, nobody had access to information on reasons for patients leaving. Managers saw greater availability of this information as useful for quality improvement, while GPs tended to think it could be useful in ensuring continuity of care. Still, most expressed no desire to expand their market share. Managers reported using marketing efforts in response to greater competition generally, rather than as a response to observed changes within their practice. But most relied on reputation. Some reported becoming more service-minded as a result of choice reforms.

It seems that practices need more information to be able to act on competitive pressures. But, most practices don’t care about it because they don’t want to expand and they face no risk of there being a shortage of patients (in cities, at least). And, even if they did want to act on the information, chances are it would just create an opportunity for them to improve access as a way of cherry-picking younger and healthier people who demand convenience. Primary care providers (in this study, at least) are not income maximisers, but satisficers (they want to break-even), so there isn’t much scope for reforms to encourage providers to compete for new patients. Patient choice reforms may improve quality, but it isn’t clear that this has anything to do with competitive pressure.

Maximising the impact of patient reported outcome assessment for patients and society. BMJ [PubMed] Published 24th January 2019

Patient-reported outcome measures (PROMs) have been touted as a way of improving patient care. Yet, their use around the world is fragmented. In this paper, the authors make some recommendations about how we might use PROMs to improve patient care. The authors summarise some of the benefits of using PROMs and discuss some of the ways that they’ve been used in the UK.

Five key challenges in the use of PROMs are specified: i) appropriate and consistent selection of the best measures; ii) ethical collection and reporting of PROM data; iii) data collection, analysis, reporting, and interpretation; iv) data logistics; and v) a lack of coordination and efficiency. To address these challenges, the authors recommend an ‘integrated’ approach. To achieve this, stakeholder engagement is important and a governance framework needs to be developed. A handy table of current uses is provided.

I can’t argue with what the paper proposes, but it outlines an idealised scenario rather than any firm and actionable recommendations. What the authors don’t discuss is the fact that the use of PROMs in the UK is flailing. The NHS PROMs programme has been scaled back, measures have been dropped from the QOF, the EQ-5D has been dropped from the GP Patient Survey. Perhaps we need bolder recommendations and new ideas to turn the tide.

Check your checklist: the danger of over- and underestimating the quality of economic evaluations. PharmacoEconomics – Open [PubMed] Published 24th January 2019

This paper outlines the problems associated with misusing methodological and reporting checklists. The author argues that the current number of checklists available in the context of economic evaluation and HTA (13, apparently) is ‘overwhelming’. Three key issues are discussed. First, researchers choose the wrong checklist. A previous review found that the Drummond, CHEC, and Philips checklists were regularly used in the wrong context. Second, checklists can be overinterpreted, resulting in incorrect conclusions. A complete checklist does not mean that a study is perfect, and different features are of varying importance in different studies. Third, checklists are misused, with researchers deciding which items are or aren’t relevant to their study, without guidance.

The author suggests that more guidance is needed and that a checklist for selecting the correct checklist could be the way to go. The issue of updating checklists over time – and who ought to be responsible for this – is also raised.

In general, the tendency seems to be to broaden the scope of general checklists and to develop new checklists for specific methodologies, requiring the application of multiple checklists. As methods develop, they become increasingly specialised and heterogeneous. I think there’s little hope for checklists in this context unless they’re pared down and used as a reminder of the more complex guidance that’s needed to specify suitable methods and achieve adequate reporting. ‘Check your checklist’ is a useful refrain, though I reckon ‘chuck your checklist’ can sometimes be a better strategy.

A systematic review of dimensions evaluating patient experience in chronic illness. Health and Quality of Life Outcomes [PubMed] Published 21st January 2019

Back to PROMs and PRE(xperience)Ms. This study sets out to understand what it is that patient-reported measures are being used to capture in the context of chronic illness. The authors conducted a systematic review, screening 2,375 articles and ultimately including 107 articles that investigated the measurement properties of chronic (physical) illness PROMs and PREMs.

29 questionnaires were about (health-related) quality of life, 19 about functional status or symptoms, 20 on feelings and attitudes about illness, 19 assessing attitudes towards health care, and 20 on patient experience. The authors provide some nice radar charts showing the percentage of questionnaires that included each of 12 dimensions: i) physical, ii) functional, iii) social, iv) psychological, v) illness perceptions, vi) behaviours and coping, vii) effects of treatment, viii) expectations and satisfaction, ix) experience of health care, x) beliefs and adherence to treatment, xi) involvement in health care, and xii) patient’s knowledge.

The study supports the idea that a patient’s lived experience of illness and treatment, and adaptation to that, has been judged to be important in addition to quality of life indicators. The authors recommend that no measure should try to capture everything because there are simply too many concepts that could be included. Rather, researchers should specify the domains of interest and clearly define them for instrument development.

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Chris Sampson’s journal round-up for 7th 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.

Overview, update, and lessons learned from the international EQ-5D-5L valuation work: version 2 of the EQ-5D-5L valuation protocol. Value in Health Published 2nd January 2019

Insofar as there is any drama in health economics, the fallout from the EQ-5D-5L value set for England was pretty dramatic. If you ask me, the criticisms are entirely ill-conceived. Regardless of that, one of the main sticking points was that the version of the EQ-5D-5L valuation protocol that was used was flawed. England was one of the first countries to get a valuation, so it used version 1.0 of the EuroQol Valuation Technique (EQ-VT). We’re now up to version 2.1. This article outlines the issues that arose in using the first version, what EuroQol did to try and solve them, and describes the current challenges in valuation.

EQ-VT 1.0 includes the composite time trade-off (cTTO) task to elicit values for health states better and worse than dead. Early valuation studies showed some unusual patterns. Research into the causes of this showed that in many cases there was very little time spent on the task. Some interviewers had a tendency to skip parts of the explanation for completing the worse-than-dead bit of the cTTO, resulting in no values worse than dead. EQ-VT 1.1 added three practise valuations along with greater monitoring of interviewer performance and a quality control procedure. This dramatically reduced interviewer effects and the likelihood of inconsistent responses. Yet further improvements could be envisioned. And so EQ-VT 2.0 added a feedback module. The feedback module shows respondents the ranking of states implied by their valuations, with which respondents can then agree or disagree. 2.0 was tested against 1.1 and showed further reductions in inconsistencies thanks to the feedback module. Other modifications were not supported by the evaluation. EQ-VT 2.1 added a dynamic question to further improve the warm-up tasks.

There are ongoing challenges with the cTTO, mostly to do with how to model the data. The authors provide a table setting out causes, consequences, and possible solutions for various issues that might arise in the modelling of cTTO data. And then there’s the discrete choice experiment (DCE), which is included in addition to the cTTO, but which different valuation studies used (or did not use) differently in modelling values. Research is ongoing that will probably lead to developments beyond EQ-VT 2.1. This might involve abandoning the cTTO altogether. Or, at least, there might be a reduction in cTTO tasks and a greater reliance on DCE. But more research is needed before duration can be adequately incorporated into DCEs.

Helpfully, the paper includes a table with a list of countries and specification of the EQ-VT versions used. This demonstrates the vast amount of knowledge that has been accrued about EQ-5D-5L valuation and the lack of wisdom in continuing to support the (relatively under-interrogated) EQ-5D-3L MVH valuation.

Do time trade-off values fully capture attitudes that are relevant to health-related choices? The European Journal of Health Economics [PubMed] Published 31st December 2018

Different people have different preferences, so values for health states elicited using TTO should vary from person to person. This study is concerned with how personal circumstances and beliefs influence TTO values and whether TTO entirely captures the impact of these on preferences for health states.

The authors analysed data from an online survey with a UK-representative sample of 1,339. Participants were asked about their attitudes towards quality and quantity of life, before completing some TTO tasks based on the EQ-5D-5L. Based on their response, they were shown two ‘lives’ that – given their TTO response – they should have considered to be of equivalent value. The researchers constructed generalised estimating equations to model the TTO values and logit models for the subsequent choices between states. Age, marital status, education, and attitudes towards trading quality and quantity of life all determined TTO values in addition to the state that was being valued. In the modelling of the decisions about the two lives, attitudes influenced decisions through the difference between the two lives in the number of life years available. That is, an interaction term between the attitudes variable and years variables showed that people who prefer quantity of life over quality of life were more likely to choose the state with a greater number of years.

The authors’ interpretation from this is that TTO reflects people’s attitudes towards quality and quantity of life, but only partially. My interpretation would be that the TTO exercise would have benefitted from the kind of refinement described above. The choice between the two lives is similar to the feedback module of the EQ-VT 2.0. People often do not understand the implications of their TTO valuations. The study could also be interpreted as supportive of ‘head-to-head’ choice methods (such as DCE) rather than making choices involving full health and death. But the design of the TTO task used in this study was quite dissimilar to others, which makes it difficult to say anything generally about TTO as a valuation method.

Exploring the item sets of the Recovering Quality of Life (ReQoL) measures using factor analysis. Quality of Life Research [PubMed] Published 21st December 2018

The ReQoL is a patient-reported outcome measure for use with people experiencing mental health difficulties. The ReQoL-10 and ReQoL-20 both ask questions relating to seven domains: six mental, one physical. There’s been a steady stream of ReQoL research published in recent years and the measures have been shown to have acceptable psychometric properties. This study concerns the factorial structure of the ReQoL item sets, testing internal construct validity and informing scoring procedures. There’s also a more general methodological contribution relating to the use of positive and negative factors in mental health outcome questionnaires.

At the outset of this study, the ReQoL was based on 61 items. These were reduced to 40 on the basis of qualitative and quantitative analysis reported in other papers. This paper reports on two studies – the first group (n=2,262) completed the 61 items and the second group (n=4,266) completed 40 items. Confirmatory factor analysis and exploratory factor analysis were conducted. Six-factor (according to ReQoL domains), two-factor (negative/positive) and bi-factor (global/negative/positive) models were tested. In the second study, participants were either presented with a version that jumbled up the positively and negatively worded questions or a version that showed a block of negatives followed by a block of positives. The idea here is that if a two-factor structure is simply a product of the presentation of questions, it should be more pronounced in the jumbled version.

The results were much the same from the two study samples. The bi-factor model demonstrated acceptable fit, with much higher factor loadings on the general quality of life factor that loaded on all items. The results indicated sufficient unidimensionality to go ahead with reducing the number of items and the two ordering formats didn’t differ, suggesting that the negative and positive loadings weren’t just an artefact of the presentation. The findings show that the six dimensions of the ReQoL don’t stand as separate factors. The justification for maintaining items from each of the six dimensions, therefore, seems to be a qualitative one.

Some outcome measurement developers have argued that items should all be phrased in the same direction – as either positive or negative – to obtain high-quality data. But there’s good reason to think that features of mental health can’t reliably be translated from negative to positive, and this study supports the inclusion (and intermingling) of both within a measure.

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