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.

Credits

Chris Sampson’s journal round-up for 17th December 2018

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

Health related quality of life aspects not captured by EQ-5D-5L: results from an international survey of patients. Health Policy Published 14th December 2018

Generic preference-based measures, such as the EQ-5D, cannot capture all aspects of health-related quality of life. They’re not meant to. Rather, their purpose is to capture just enough information to be able to adequately distinguish between health states with respect to the domains deemed normatively relavent to decisionmakers. The stated aim of this paper is to determine whether people with a variety of chronic conditions believe that their experiences can be adequately represented by the EQ-5D-5L.

The authors conducted an online survey, identifying participants through 320 patient associations across 47 countries. Participants were asked to complete the EQ-5D-5L and then asked if any aspects of their illness, which had a “big impact” on their health, were not captured by the EQ-5D-5L. 1,031 people started the survey and 767 completed it. More than half were from the UK. 51% of respondents said that there was some aspect of health not captured by the EQ-5D-5L. Of them, 19% mentioned fatigue, 12% mentioned medication side effects, 9.5% mentioned co-morbid conditions, and then a bunch of others in smaller proportions.

It’s nice to know what people think, but I have a few concerns about the usefulness of this study. One of the main problems is that it doesn’t seem safe to assume that respondents interpret “big impact” as meaning “an impact that is independently important in determining your overall level of quality of life”. So, even if we accept that people judging something to be important makes it important (which I’m not sure it does), then we still can’t be sure whether what they are identifying is within the scope of what we’re trying to measure. For starters, I can see no justification for including a ‘medication side effects’ domain. There’s also some concern about selection and attrition. I’d guess that people with more complicated or less common health concerns would be more likely to start and finish a survey about more complicated or less common health concerns.

The main thing I took from this study is that half of respondents with chronic diseases thought that the EQ-5D-5L captured every single aspect of health that had a “big impact”, and that there wasn’t broad support for any other specific dimension.

Reducing drug wastage in pharmaceuticals dosed by weight or body surface areas by optimising vial sizes. Applied Health Economics and Health Policy [PubMed] Published 5th December 2018

It’s common for pharmaceuticals to be wasted. Not just those out-of-date painkillers you threw in the bin, but also the expensive stuff being used in hospitals. One of the main reasons that waste occurs is that vials are made to specific sizes and, often, dosage varies from patient to patient – according to weight, for example – and doesn’t match the vial size. Suppose that vials are available as 50mg and 80mg and that an individual requires a 60mg dose. One way to address this might be to allow for vial sharing, whereby the leftovers are given to the next patient. But that isn’t always possible. So, we might like to consider what the best combination of available vial sizes should be, given the characteristics of the population.

In this paper, the authors set out the problem mathematically. Essentially, the optimisation problem is to minimise cost across the population subject to the vial sizes. An example is presented for two drugs (pembrolizumab and cabazitaxel), simulating patients based on samples drawn from the Health Survey for England. Simplifications are applied to the examples, such as setting a constraint of 8 vials per patient and assuming that prices are linear (i.e. fixed per milligram).

Pembrolizumab is currently available in 50mg and 100mg vials, and the authors estimate current wastage to be 13.2%. The simulations show that switching the 50mg to a 70mg would cut wastage to 8.6%. Cabazitaxel is available in 60mg vials, resulting in 19.4% wastage. Introducing a 12.5mg vial would cut wastage by around two thirds. An important general finding, which should be self-evident, is that vial sizes should not be divisible by each other, as this limits the number of possible combinations.

Depending on when vial sizes are determined (e.g. pre- or post-authorisation), pharmaceutical companies might use it to increase profit margins, or health systems might use it to save costs. Regardless, wastage isn’t useful. Evidence-based manufacture is an example of one of those best ideas; the sort that is simple and seems obvious once it’s spelt out. It’s a rare opportunity to benefit patients, health care providers, and manufacturers, with no significant burden on policymakers.

Death or debt? National estimates of financial toxicity in persons with newly-diagnosed cancer. The American Journal of Medicine [PubMed] Published October 2018

If you’re British, what’s the scariest thing about an ‘Americanised’ (/Americanized) health care system? Expensive inhalers? A shortened life expectancy? My guess is that the prospect of having to add financial ruin to terminal illness looms pretty large. You should make sure your fear is evidence-based. Here’s a paper to shake in the face of anyone who doesn’t support universal health care.

The authors use data from the Health and Retirement Study from 1998-2014, which includes people over 50 years of age and includes new (self-reported) diagnoses of cancer. This was the basis for inclusion in the study, with over 9.5 million new diagnoses of cancer. Up to two years pre-diagnosis was taken as a baseline. The data set also includes information on participants’ assets and debts, allowing the authors to use change in net worth as the primary outcome. Generalised linear models were used to assess various indicators of financial toxicity, including change or incurrence of consumer debt, mortgage debt, and home equity debt at two- and four-year follow-up. In addition to cancer diagnosis, various chronic comorbidities and socio-demographic variables were included in the models.

Shockingly, after two years following diagnosis, 42.4% of people had depleted their entire life’s assets. Average net worth had dropped $92,000. After four years, 38.2% were still insolvent. Women, older people, people who weren’t White, people with Medicaid, and those with worsening cancer status were among those more likely to have completely depleted their assets within two years. Having private insurance and being married had protective effects, as we might expect. There were some interesting findings associated with the 2008 financial crisis, which also seemed to be protective. And a protective effect associated with psychiatric comorbidity deserves more thought.

It’s difficult to explain away any (let alone all) of the magnitude of these findings. The analysis seems robust. But, given all other evidence available about out-of-pocket costs for cancer patients in the US, it should be shocking but not unexpected. The authors describe financial toxicity as ‘unintended’. There’s nothing unintended about this. Policymakers in the US keep deciding that they’d prefer to destroy the lives of sick people than allow for the spreading of that financial risk.

Credits

Chris Sampson’s journal round-up for 27th August 2018

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

Ethically acceptable compensation for living donations of organs, tissues, and cells: an unexploited potential? Applied Health Economics and Health Policy [PubMed] Published 25th August 2018

Around the world, there are shortages of organs for transplantation. In economics, the debate around the need to increase organ donation can be frustratingly ignorant of ethical and distributional concerns. So it’s refreshing to see this article attempting to square concerns about efficiency and equity. The authors do so by using a ‘spheres of justice’ framework. This is the idea that different social goods should be distributed according to different principles. So, while we might be happy for brocolli and iPhones to be distributed on the basis of free exchange, we might want health to be distributed on the basis of need. The argument can be extended to state that – for a just situation to prevail – certain exchanges between these spheres of justice (e.g. health for iPhones) should never take place. This idea might explain why – as the authors demonstrate with a review of European countries – policy tends not to allow monetary compensation for organ donation.

The paper cleverly sets out to taxonomise monetary and non-monetary reimbursement and compensation with reference to individuals’ incentives and the spheres of justice principles. From this, the authors reach two key conclusions. Firstly, that (monetary) reimbursement of donors’ expenses (e.g. travel costs or lost earnings) is ethically sound as this does not constitute an incentive to donate but rather removes existing disincentives. Secondly, that non-monetary compensation could be deemed ethical.

Three possible forms of non-monetary compensation are discussed: i) prioritisation, ii) free access, and iii) non-health care-related benefits. The first could involve being given priority for receiving organs, or it could extend to the jumping of other health care waiting lists. I think this is more problematic than the authors let on because it asserts that health care should – at least in part – be distributed according to desert rather than need. The second option – free access – could mean access to health care that people would otherwise have to pay for. The third option could involve access to other social goods such as education or housing.

This is an interesting article and an enjoyable read, but I don’t think it provides a complete solution. Maybe I’m just too much of a Marxist, but I think that this – as all other proposals – fails to distribute from each according to ability. That is, we’d still expect non-monetary compensation to incentivise poorer (and on average less healthy) people to donate organs, thus exacerbating health inequality. This is because i) poorer people are more likely to need the non-monetary benefits and ii) we live in a capitalist society in which there is almost nothing that money can’t by and which is strictly non-monetary. Show me a proposal that increases donation rates from those who can most afford to donate them (i.e. the rich and healthy).

Selecting bolt-on dimensions for the EQ-5D: examining their contribution to health-related quality of life. Value in Health Published 18th August 2018

Measures such as the EQ-5D are used to describe health-related quality of life as completely and generically as possible. But there is a trade-off between completeness and the length of the questionnaire. Necessarily, there are parts of the evaluative space that measures will not capture because they are a simplification. If the bit they’re missing is important to your patient group, that’s a problem. You might fancy a bolt-on. But how do we decide which areas of the evaluative space should be more completely included in the measure? Which bolt-ons should be used? This paper seeks to provide means of answering these questions.

The article builds on an earlier piece of work that was included in an earlier journal round-up. In the previous paper, the authors used factor analysis to identify candidate bolt-ons. The goal of this paper is to outline an approach for specifying which of these candidates ought to be used. Using data from the Multi-Instrument Comparison study, the authors fit linear regressions to see how well 37 candidate bolt-on items explain differences in health-related quality of life. The 37 items correspond to six different domains: energy/vitality, satisfaction, relationships, hearing, vision, and speech. In a second test, the authors explored whether the bolt-on candidates could explain differences in health-related quality of life associated with six chronic conditions. Health-related quality of life is defined according to a visual analogue scale, which notably does not correspond to that used in the EQ-5D but rather uses a broader measure of physical, mental, and social health.

The results suggest that items related to energy/vitality, relationships, and satisfaction explained a significant part of health-related quality of life on top of the existing EQ-5D dimensions. The implication is that these could be good candidates for bolt-ons. The analysis of the different conditions was less clear.

For me, there’s a fundamental problem with this study. It moves the goals posts. Bolt-ons are about improving the extent to which a measure can more accurately represent the evaluative space that it is designed to characterise. In this study, the authors use a broader definition of health-related quality of life that – as far as I can tell – the EQ-5D is not designed to capture. We’re not dealing with bolt-ons, we’re dealing with extensions to facilitate expansions to the evaluative space. Nevertheless, the method could prove useful if combined with a more thorough consideration of the evaluative space.

Sources of health financing and health outcomes: a panel data analysis. Health Economics [PubMed] [RePEc] Published 15th August 2018

There is a growing body of research looking at the impact that health (care) spending has on health outcomes. Usually, these studies don’t explicitly look at who is doing the spending. In this study, the author distinguishes between public and private spending and attempts to identify which type of spending (if either) results in greater health improvements.

The author uses data from the World Bank’s World Development Indicators for 1995-2014. Life expectancy at birth is adopted as the primary health outcome and the key expenditure variables are health expenditure as a share of GDP and private health expenditure as a share of total health expenditure. Controlling for a variety of other variables, including some determinants of health such as income and access to an improved water source, a triple difference analysis is described. The triple difference estimator corresponds to the difference in health outcomes arising from i) differences in the private expenditure level, given ii) differences in total expenditure, over iii) time.

The key finding from the study is that, on average, private expenditure is more effective in increasing life expectancy at birth than public expenditure. The author also looks at government effectiveness, which proves crucial. The finding in favour of private expenditure entirely disappears when only countries with effective government are considered. There is some evidence that public expenditure is more effective in these countries, and this is something that future research should investigate further. For countries with ineffective governments, the implication is that policy should be directed towards increasing overall health care expenditure by increasing private expenditure.

Credits