David Mott’s journal round-up for 16th September 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.

Opening the ‘black box’: an overview of methods to investigate the decision‑making process in choice‑based surveys. The Patient [PubMed] Published 5th September 2019

Choice-based surveys using methods such as discrete choice experiments (DCEs) and best-worst scaling (BWS) exercises are increasingly being used in health to understand people’s preferences. A lot of time and energy is spent on analysing the data that come out from these surveys but increasingly there is an interest in better understanding respondents’ decision-making processes. Whilst many will be aware of ‘think aloud’ interviews (often used for piloting), other methods may be less familiar as they’re not applied frequently in health. That’s where this fascinating paper by Dan Rigby and colleagues comes in. It provides an overview of five different methods of what they call ‘pre-choice process analysis’ of decision-making, describing the application, state of knowledge, and future research opportunities.

Eye-tracking has been used in health recently. It’s intuitive and provides an insight into where the participants’ focus is (or isn’t). The authors explained that one of the ways it has been used is to explore attribute non-attendance (ANA), which essentially occurs when people are ignoring attributes either because they’re irrelevant to them, or simply because it makes the task easier. However, surprisingly, it has been suggested that ‘visual ANA’ (not looking at the attribute) doesn’t always align with ‘stated ANA’ (participants stating that they ignored the attribute) – which raises some interesting questions!

However, the real highlight for me was the overview of the use of brain imaging techniques to explore choices being made in DCEs. One study highlighted by the authors – which was a DCE about eggs and is now at least #2 on my list of the bizarre preference study topics after this oddly specific one on Iberian ham – predicted choices from an initial ‘passive viewing’ using functional magnetic resonance imaging (fMRI). They found that incorporating changes in blood flow (prompted by changes in attribute levels during ‘passive viewing’) into a random utility model accounted for a lot of the variation in willingness to pay for eggs – pretty amazing stuff.

Whilst I’ve highlighted the more unusual methods here, after reading this overview I have to admit that I’m an even bigger advocate for the ‘think aloud’ technique now. Although it may have some limitations, the amount of insight offered combined with its practicality is hard to beat. Though maybe I’m biased because I know that I won’t get my hands on any eye-tracking or brain imaging devices any time soon. In any case, I highly recommend that any researchers conducting preference studies give this paper a read as it’s really well written and will surely be of interest.

Disentangling public preferences for health gains at end-of-life: further evidence of no support of an end-of-life premium. Social Science & Medicine [PubMed] Published 21st June 2019

The end of life (EOL) policy introduced by NICE in 2009 [PDF] has proven controversial. The policy allows treatments that are not cost-effective within the usual range to be considered for approval, provided that certain criteria are met. Specifically, that the treatment targets patients with a short life expectancy (≤24 months), offers a life extension (of ≥3 months) and is for a ‘small patient population’. One of the biggest issues with this policy is that it is unclear whether the general population actually supports the idea of valuing health gains (specifically life extension) at EOL more than other health gains.

Numerous academic studies, usually involving some form of stated preference exercise, have been conducted to test whether the public might support this EOL premium. A recent review by Koonal Shah and colleagues summarised the existing published studies (up to October 2017), highlighting that evidence is extremely mixed. This recently published Danish study, by Lise Desireé Hansen and Trine Kjær, adds to this literature. The authors conducted an incredibly thorough stated preference exercise to test whether quality of life (QOL) gains and life extension (LE) at EOL are valued differently from other similarly sized health gains. Not only that, but the study also explored the effect of perspective on results (social vs individual), the effect of age (18-35 vs. 65+), and impact of initial severity (25% vs. 40% initial QOL) on results.

Overall, they did not find evidence of support for an EOL premium for QOL gains or for LEs (regardless of perspective) but their results do suggest that QOL gains are preferred over LE. In some scenarios, there was slightly more support for EOL in the social perspective variant, relative to the individual perspective – which seems quite intuitive. Both age and initial severity had an impact on results, with respondents preferring to treat the young and those with worse QOL at baseline. One of the most interesting results for me was within their subgroup analyses, which suggested that women and those with a relation to a terminally ill patient had a significantly positive preference for EOL – but only in the social perspective scenarios.

This is a really well-designed study, which covers a lot of different concepts. This probably doesn’t end the debate on NICE’s use of the EOL criteria – not least because the study wasn’t conducted in England and Wales – but it contributes a lot. I’d consider it a must-read for anyone interested in this area.

How should we capture health state utility in dementia? Comparisons of DEMQOL-Proxy-U and of self- and proxy-completed EQ-5D-5L. Value in Health Published 26th August 2019

Capturing quality of life (QOL) in dementia and obtaining health state utilities is incredibly challenging; which is something that I’ve started to really appreciate recently upon getting involved in a EuroQol-funded ‘bolt-ons’ project. The EQ-5D is not always able to detect meaningful changes in cognitive function and condition-specific preference-based measures (PBMs), such as the DEMQOL, may be preferred as a result. However, this isn’t the only challenge because in many cases patients are not in a position to complete the surveys themselves. This means that proxy-reporting is often required, which could be done by either a professional (formal) carer, or a friend or family member (informal carer). Researchers that want to use a PBM in this population therefore have a lot to consider.

This paper compares the performance of the EQ-5D-5L and the DEMQOL-Proxy when completed by care home residents (EQ-5D-5L only), formal carers and informal carers. The impressive dataset that the authors use contains 1,004 care home residents, across up to three waves, and includes a battery of different cognitive and QOL measures. The overall objective was to compare the performance of the EQ-5D-5L and DEMQOL-Proxy, across the three respondent groups, based on 1) construct validity, 2) criterion validity, and 3) responsiveness.

The authors found that self-reported EQ-5D-5L scores were larger and less responsive to changes in the cognitive measures, but better at capturing residents’ self-reported QOL (based on a non-PBM) relative to proxy-reported scores. It is unclear whether this is a case of adaptation as seen in many other patient groups, or if the residents’ cognitive impairments prevent them from reliably assessing their current status. The proxy-reported EQ-5D-5L scores were generally more responsive to changes in the cognitive measures relative to the DEMQOL-Proxy (irrespective of which type of proxy), which the authors note is probably due to the fact that the DEMQOL-Proxy focuses more on the emotional impact of dementia rather than functional impairment.

Overall, this is a really interesting paper, which highlights the challenges well and illustrates that there is value in collecting these data from both patients and proxies. In terms of the PBM comparison, whilst the authors do not explicitly state it, it does seem that the EQ-5D-5L may have a slight upper hand due to its responsiveness, as well as for pragmatic reasons (the DEMQOL-Proxy has >30 questions). Perhaps a cognition ‘bolt-on’ to the EQ-5D-5L might help to improve the situation in future?

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Rachel Houten’s journal round-up for 22nd 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.

To HTA or not to HTA: identifying the factors influencing the rapid review outcome in Ireland. Value in Health [PubMed] Published 6th March 2019

National health services are constantly under pressure to provide access to new medicines as soon as marketing authorisation is granted. The NCPE in the Republic of Ireland has a rapid review process for selecting medicines that require a full health technology assessment (HTA), and the rest, approximately 45%, are able to be reimbursed without such an in-depth analysis.

Formal criteria do not exist. However, it has previously been suggested that the robustness of clinical evidence of at least equivalence; a drug that costs the same or less; an annual (or estimated) budget impact of less than €0.75 million to €1 million; and the ability of the current health systems to restrict usage are some of what is considered when making the decision.

The authors of this paper used the allocation over the past eight years to explore the factors that drive the decision to embark on a full HTA. They found, unsurprisingly, that first-in-class medicines are more likely to require an HTA as too are those with orphan status. Interestingly, the clinical area influenced the requirement for a full HTA, but the authors consider all of these factors to indicate that high-cost drugs are more likely to require a full assessment. Drug cost information is not publicly available and so the authors used the data available on the Scottish Medicine Consortium website as a surrogate for costs in Ireland. In doing so, they were able to establish a relationship between the cost per person for each drug and the likelihood of the drug having a full HTA, further supporting the idea that more expensive drugs are more likely to require HTA. On the face of it, this seems eminently sensible. However, my concern is that, in a system that is designed to deliberately measure cost per unit of health care (usually QALYs), there is the potential for lower-cost but ineffective drugs to become commonplace while more expensive medicines are subject to more rigor.

The paper provides some insight into what drives a decision to undertake a full HTA in Ireland. The NICE fast-track appraisal system operates as an opt-in system where manufacturers can ask to follow this shorter appraisal route if their drug is likely to produce an ICER of £10,000 or less. As my day job is for an Evidence Review Group (opinions my own), how things are done elsewhere – unsurprisingly – captured my attention. The desire to speed up the HTA process is obvious but the most appropriate mechanisms in which to do so are far from it. Whether or not the same decision is ultimately made is what concerns me.

NHS joint working with industry is out of public sight. BMJ [PubMed] Published 27th March 2019

This paper suggests that ‘joint working arrangements’ – a government-supported initiative between pharmaceutical companies and the NHS – are not being implemented according to guidelines on transparency. These arrangements are designed to promote collaborative research between the NHS and industry and help advance NHS provision of services.

The authors used freedom of information requests to obtain details on how many trusts were involved in joint working arrangements in 2016 and 2017. The declarations of payments made by drug companies are disclosed but the corresponding information from trusts is less readily accessible, and in some cases access to any details was prevented. Theoretically, the joint working arrangements are supposed to be void of any commercial influence on what is prescribed, but my thoughts are echoed in this paper when it asks “what’s in it for the private sector?” The sheer fact that some NHS trusts were unwilling to provide the BMJ with the information requested due to ‘commercial interest’ rings huge alarm bells.

I’m not completely cynical of these arrangements in principle, though, and the paper cites a couple of projects that involved building new facilities for age-related macular generation, which likely offer benefits to patients, and possibly much faster than could have been achieved with NHS funding alone. Some of the arrangements intend to push the implementation of national guidance, which, as a small cog in the guidance generation machine, I unashamedly (and predictably) think is a good thing.

Does it matter to us? As economists, it means that any work based on national practice and costs is likely to be unrepresentative of what actually happens. This, however, has always been the case to some extent, with variations in local service provision and the negotiation power of trusts with large volumes of patients. A national register of the arrangements would have the potential to feed into economic analysis, even if just as a statement of awareness.

Can the NHS survive without getting into bed with industry? Probably not. I think the paper does a good job of presenting the arguments on all sides and pushing for increasing availability of what is happening.

Estimating joint health condition utility values. Value in Health [PubMed] Published 22nd February 2019

I’m really interested in how this area is developing. Multi-morbidity is the norm, especially as we age. Single condition models are criticised for their lack of representation of patients in the real world. Appropriately estimating the quality of life of people with several chronic conditions, when only individual condition data are available, is incredibly difficult.

In this paper, parametric and non-parametric methods were tested on a dataset from a large primary care patient survey in the UK. The multiplicative approach was the best performing for two conditions. When more than two conditions were considered, the linear index (which incorporates additive, multiplicative, and minimum models with the use of linear regression and parameter weights derived from the underlying data) achieved the best results.

Including long-term mental health within the co-morbidities for which utility was estimated produced biased estimates. The authors discuss some possible explanations for this, including the fact that the anxiety and depression question in the EQ-5D is the only one which directly maps to an individual condition, and that mental health may have a causal effect on physical health. This is a fascinating finding, which has left me somewhat scratching my head as to how this oddity could be addressed and if separate methods of estimation will need to be used for any population with multi-morbidity including mental health conditions.

It did make me wonder if more precise EQ-5D data could be helpful to uncover the true interrelationships between joint health conditions and quality of life. The EQ-5D asks patients to think about their health state ‘today’. Although the primary care dataset used includes 16 chronic health conditions, it doesn’t, as far as I know, contain any information on the symptoms apparent on the day of quality of life assessment, which could be flaring or absent at any given time. This is a common problem with the EQ-5D and I don’t think a readily available data source of this type exists, so it’s a thought on ideals. Unsurprisingly, the more joint health conditions to be considered, the larger the error in terms of estimation from individual conditions. This may be due to the increasing likelihood of overlap in the symptoms experienced across conditions and thus a violation of the assumption that quality of life for an individual condition is independent of any other condition.

Whether the methodology remains robust for populations outside of the UK or for other measures of utility would need to be tested, and the authors are keen to highlight the need for caution before running away and using the methods verbatim. The paper does present a nice summary of the evidence to date in this area, what the authors did, and what it adds to the topic, so worth a read.

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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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