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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Chris Sampson’s journal round-up for 31st July 2017

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.

An exploratory study on using principal-component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value in Health Published 14th July 2017

I’m not convinced by the idea of using bolt-on dimensions for multi-attribute utility instruments. A state description with a bolt-on refers to a different evaluative space, and therefore is not comparable with the progenitor, thus undermining its purpose. Maybe this study will persuade me otherwise. The authors analyse data from the Multi Instrument Comparison database, including responses to EQ-5D-5L, SF-6D, HUI3, AQoL 8D and 15D questionnaires, as well as the ICECAP and 3 measures of subjective well-being. Content analysis was used to allocate items from the measures to underlying constructs of health-related quality of life. The sample of 8022 was randomly split, with one half used for principal-component analysis and confirmatory factor analysis, and the other used for validation. This approach looks at the underlying constructs associated with health-related quality of life and the extent to which individual items from the questionnaires influence them. Candidate items for bolt-ons are those items from questionnaires other than the EQ-5D that are important and not otherwise captured by the EQ-5D questions. The principal-component analysis supported a 9-component model: physical functioning, psychological symptoms, satisfaction, pain, relationships, speech/cognition, hearing, energy/sleep and vision. The EQ-5D only covered physical functioning, psychological symptoms and pain. Therefore, items from measures that explain the other 6 components represent bolt-on candidates for the EQ-5D. This study succeeds in its aim. It demonstrates what appears to be a meaningful quantitative approach to identifying items not fully captured by the EQ-5D, which might be added as bolt-ons. But it doesn’t answer the question of which (if any) of these bolt-ons ought to be added, or in what circumstances. That would at least require pre-definition of the evaluative space, which might not correspond to the authors’ chosen model of health-related quality of life. If it does, then these findings would be more persuasive as a reason to do away with the EQ-5D altogether.

Endogenous information, adverse selection, and prevention: implications for genetic testing policy. Journal of Health Economics Published 13th July 2017

If you can afford it, there are all sorts of genetic tests available nowadays. Some of them could provide valuable information about the risk of particular health problems in the future. Therefore, they can be used to guide individuals’ decisions about preventive care. But if the individual’s health care is financed through insurance, that same information could prove costly. It could reinforce that classic asymmetry of information and adverse selection problem. So we need policy that deals with this. This study considers the incentives and insurance market outcomes associated with four policy options: i) mandatory disclosure of test results, ii) voluntary disclosure, iii) insurers knowing the test was taken, but not the results and iv) complete ban on the use of test information by insurers. The authors describe a utility model that incorporates the use of prevention technologies, and available insurance contracts, amongst people who are informed or uninformed (according to whether they have taken a test) and high or low risk (according to test results). This is used to estimate the value of taking a genetic test, which differs under the four different policy options. Under voluntary disclosure, the information from a genetic test always has non-negative value to the individual, who can choose to only tell their insurer if it’s favourable. The analysis shows that, in terms of social welfare, mandatory disclosure is expected to be optimal, while an information ban is dominated by all other options. These findings are in line with previous studies, which were less generalisable according to the authors. In the introduction, the authors state that “ethical issues are beyond the scope of this paper”. That’s kind of a problem. I doubt anybody who supports an information ban does so on the basis that they think it will maximise social welfare in the fashion described in this paper. More likely, they’re worried about the inequities in health that mandatory disclosure could reinforce, about which this study tells us nothing. Still, an information ban seems to be a popular policy, and studies like this indicate that such decisions should be reconsidered in light of their expected impact on social welfare.

Returns to scientific publications for pharmaceutical products in the United States. Health Economics [PubMedPublished 10th July 2017

Publication bias is a big problem. Part of the cause is that pharmaceutical companies have no incentive to publish negative findings for their own products. Though positive findings may be valuable in terms of sales. As usual, it isn’t quite that simple when you really think about it. This study looks at the effect of publications on revenue for 20 branded drugs in 3 markets – statins, rheumatoid arthritis and asthma – using an ‘event-study’ approach. The authors analyse a panel of quarterly US sales data from 2003-2013 alongside publications identified through literature searches and several drug- and market-specific covariates. Effects are estimated using first difference and difference in first difference models. The authors hypothesise that publications should have an important impact on sales in markets with high generic competition, and less in those without or with high branded competition. Essentially, this is what they find. For statins and asthma drugs, where there was some competition, clinical studies in high-impact journals increased sales to the tune of $8 million per publication. For statins, volume was not significantly affected, with mediation through price. In rhematoid arthritis, where competition is limited, the effect on sales was mediated by the effect on volume. Studies published in lower impact journals seemed to have a negative influence. Cost-effectiveness studies were only important in the market with high generic competition, increasing statin sales by $2.2 million on average. I’d imagine that these impacts are something with which firms already have a reasonable grasp. But this study provides value to public policy decision makers. It highlights those situations in which we might expect manufacturers to publish evidence and those in which it might be worthwhile increasing public investment to pick up the slack. It could also help identify where publication bias might be a bigger problem due to the incentives faced by pharmaceutical companies.

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