Chris Sampson’s journal round-up for 13th March 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.

The effects of exercise and relaxation on health and wellbeing. Health Economics [PubMedPublished 9th Month 2017

Encouraging self-management of health sounds like a good idea, but the evidence is pretty weak. As economists, we know that something must be displaced in order to do it. This study considers the opportunity cost of time and how it might affect self-management activity and any associated benefits. Employment and education are likely to increase income and thus facilitate more expenditure on exercise. But the time cost of exercise is also likely to increase, meaning that the impact on demand is ambiguous. The study uses data from a trial of self-management support that included people with diabetes, COPD or IBS. EQ-5D, self-assessed health and the amount of time spent ‘being happy’ were all collected. Information was available for 12 different self-management activities, including ‘do exercises’ and ‘rest and relax’, and the extent to which individuals did these. Outcomes for 3,472 people at 12-month follow-up are estimated, controlling for outcomes at baseline and 6 months. The study assumes that employment and education affect health via their influence on exercise and relaxation. That seems a bit questionable and the other 10 self-management indicators could have been looked at to test this. People in full-time employment were 11 percentage points less likely to use relaxation to manage their condition, suggesting that the substitution effect on time dominates as the opportunity cost of self-management increases. Having a degree or professional qualification increased the probability of using exercise by 5 percentage points, suggesting that the income effect dominates. Those who are more likely to use either exercise or relaxation are also more likely to do the other. An interesting suggestion is that time preference might explain things here. Those with more education may prefer to exercise (as an investment) than to get the instant gratification of rest and relaxation. It’s important that policy recommendations take into consideration the fact that different groups will respond differently to incentives for self-management, at least partly due to their differing time constraints. The thing I find most interesting is the analysis of the different outcomes (something I’ve worked on). Exercise is found to improve self-assessed health, while relaxation increases happiness. Neither exercise or relaxation had a (statistically significant) effect on EQ-5D. Depending on your perspective, this either suggests that the EQ-5D is failing to identify important changes in broad health-related domains or it means that self-management does not achieve the goals (QALYs to the max) of the health service.

New findings from the time trade-off for income approach to elicit willingness to pay for a quality adjusted life year. The European Journal of Health Economics [PubMedPublished 8th March 2017

The question ‘what is a QALY worth’ could invoke any number of reactions in a health economist, from chin scratching to eye rolling. The perspective that we’re probably most familiar with in the UK is that the value of a QALY is the value of health foregone in order to achieve it (i.e. opportunity cost within the health care perspective). An alternative perspective is that the value of a QALY is the consumption value of health; how much consumption would individuals be willing to give up in order to obtain an additional QALY? This second perspective facilitates a broader societal perspective. It can tell us whether or not the budget is set at an appropriate level, while the health care perspective can only take the budget as given. This study relates mainly to decisions made with the ‘consumption value’ perspective. One approach that has been proposed is to assess willingness to pay for a QALY using a time trade-off exercise that incorporates trade-offs between length and quality of life and income. This study builds on the original work by using a multiplicative utility function to estimate willingness to pay and also bringing in prospect theory to allow for reference dependence and loss aversion. 550 participants were asked to choose between living 10 years in their current health state with their current salary or to live a reduced number of years in their current health state with a luxury income (pre-specified by the participant). Respondents were also asked to make a similar choice, but framed as a loss of income, between living 10 years at a subsistence income or fewer years with their current income. A quality of life trade-off exercise was also conducted, in which people traded reduced health and a lower income. The findings support the predictions of prospect theory. Loss aversion is found to be stronger for duration than for quality of life. Individuals were more willing to sacrifice life years to move from subsistence income to current income than to move from current income to luxury income. The results imply that quality of life and income are closer substitutes than longevity and income. That makes sense, given the all-or-nothing nature of being alive. Crucially, the findings highlight the need to better understand the shape of the underlying lifetime utility function. In all tasks, more than half of respondents were either non-traders or over-traded, indicating a negative willingness to pay. That should give pause for thought when it comes to any aggregation of the results. Willingness to pay studies often throw up more questions than answers. This one does so more than most, particularly about sources of bias in people’s responses. The authors identify plenty of opportunities for future research.

Beyond QALYs: multi-criteria based estimation of maximum willingness to pay for health technologies. The European Journal of Health Economics [PubMed] Published 3rd March 2017

Life is messy. Evaluating things in terms of a single outcome, whether that be QALYs, £££s or whatever, is necessarily simplifying and restrictive. That’s not necessarily a bad thing, but we’d do well to bear it in mind. In this paper, Erik Nord sets out a kind of cost value analysis that does away with QALYs (gasp!). The author starts by outlining some familiar criticisms of the QALY approach, such as its failure to consider the inherent value of life and people’s differing reference points. Generally, I see these as features rather than bugs, and it isn’t QALYs themselves in the crosshairs here so much as cost-per-QALY analysis. The proposed method flips current practice by putting societal preferences about fair and efficient resource allocation before attaching values to the outcomes. As such, it acknowledges the fact that society’s preferences for gains in quality of life differ from those for gains in length of life. For example, society may prefer treating the more severely ill (independent of age) but also exhibit a ‘fair innings’ preference that is related to age. Thus, quality and quantity of life are disaggregated and the QALY is no more. A set of tables is presented that can be read to assess ‘value’ in alternative scenarios, given the assumptions set out in the paper. There is merit in the approach and a lot that I like about the possibilities of its use. But for me, the whole thing was made less attractive by the way it is presented in the paper. The author touts willingness to pay – for quality of life gains and for longevity gains – as the basis for value. Anything that makes resource allocation more dependent on willingness to pay values for things without a price (health, life) is a big no-no for me. But the method doesn’t depend on that. Furthermore, as is so often the case, most of the criticisms within relate to ways of using QALYs, rather than the fundamental basis for their estimation. This only weakens the argument for an alternative. But I can think of plenty of problems with QALYs, some of which might be addressed by this alternative approach. It’s unfortunate that the paper doesn’t outline how these more fundamental problems might be addressed. There may come a day when we do away with QALYs, and we may end up doing something similar to what’s outlined here, but we need to think harder about how this alternative is really better.



Transformative treatments: a big methodological challenge for health economics

Social scientists, especially economists, are concerned with causal inference: understanding whether and how an event causes a certain effect. Typically, we subscribe to the view that causal relations are reducible to sets of counterfactuals, and we use ever more sophisticated methods, such as instrumental variables and propensity score matching, to estimate these counterfactuals. Under the right set of assumptions, like that unobserved differences between study subjects are time invariant or that a treatment causes its effect through a certain mechanism, we can derive estimators for average treatment effects. All uncontroversial stuff indeed.

A recent paper from L.A. Paul and Kieran Healy introduces an argument of potential importance to how we can interpret studies investigating causal relations. In particular, they make the argument that we don’t know if individual preferences persist in a study through treatment. It is in general not possible to distinguish between the case where a treatment has satisfied an underlying revealed preference, or transformed an individual’s preferences. If preferences are changed or transformed, rather than revealed, then they are, in effect, a different population and in a causal inference type study, no longer comparable to the control population.

To quote their thought experiment:

Vampires: In the 21st century, vampires begin to populate North America. Psychologists decide to study the implications this could have for the human population. They put out a call for undergraduates to participate in a randomized controlled experiment, and recruit a local vampire with scientific interests. After securing the necessary permissions, they randomize and divide their population of undergraduates into a control group and a treatment group. At t1, members of each group are given standard psychological assessments measuring their preferences about vampires in general and about becoming a vampire in particular. Then members of the experimental group are bitten by the lab vampire.

Members of both groups are left to go about their daily lives for a period of time. At t2, they are assessed. Members of the control population do not report any difference in their preferences at t2. All members of the treated population, on the other hand, report living richer lives, enjoying rewarding new sensory experiences, and having a new sense of meaning at t2. As a result, they now uniformly report very strong pro-vampire preferences. (Some members of the treatment group also expressed pro-vampire preferences before the experiment, but these were a distinct minority.) In exit interviews, all treated subjects also testify that they have no desire to return to their previous condition.

Should our psychologists conclude that being bitten by a vampire somehow satisfies people’s underlying, previously unrecognized, preferences to become vampires? No. They should conclude that being bitten by a vampire causes you to become a vampire (and thus, to prefer being one). Being bitten by a vampire and then being satisfied with the result does not satisfy or reveal your underlying preference to be a vampire. Being bitten by a vampire transforms you: it changes your preferences in a deep and fundamental way, by replacing your underlying human preferences with vampire preferences, no matter what your previous preferences were.

In our latest journal round-up, I featured a paper that used German reunification in 1989 as a natural experiment to explore the impact of novel food items in the market on consumption and weight gain. The transformative treatments argument comes into play here. Did reunification reveal the preferences of East Germans for the novel food stuffs, or did it change their preferences for foodstuffs overall due to the significant cultural change? If the latter case is true then West Germans do not constitute an appropriate control group. The causal mechanism at play is also important to the development of policy: for example, without reunification there may not have been any impact from novel food products.

This argument is also sometimes skirted around with regards to the valuing of health states. Should it be the preferences of healthy people, or the experienced utility of sick people, that determine health state values? Do physical trauma and disease reveal our underlying preferences for different health states, or do they transform us to have different preferences entirely? Any study looking at the effect of disease on health status or quality of life could not distinguish between the two. Yet the two cases are akin to using the same or different groups of people to do the valuation of health states.

Consider also something like estimating the impact of retirement on health and quality of life. If self-reported quality of life is observed to improve in one of these studies, we don’t know if that is because retirement has satisfied a pre-existing preference for the retired lifestyle, or retirement has transformed a person’s preferences. In the latter case, the appropriate control group to evaluate the causal effect of retirement is not non-retired persons.

Paul and Healy do not make their argument to try to prevent or undermine research in the social sciences, they interpret their conclusion as a “methodological challenge”. The full implications of the above arguments have not been explored but could be potentially great and new innovations in methodology to estimate average causal effects could be warranted. How this may be achieved, I’ll have to admit, I do not know.


Chris Sampson’s journal round-up for 14th November 2016

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.

Weighing clinical evidence using patient preferences: an application of probabilistic multi-criteria decision analysis. PharmacoEconomics [PubMedPublished 10th November 2016

There are at least two ways in which preferences determine the allocation of health care resources (in a country with a HTA agency, at least). One of them we think about a lot; the (societal) valuation of health states as defined by a multi-attribute measure (like the EQ-5D). The other relates to patient preferences that determine whether or not a specific individual (and their physician) will choose to use a particular technology, given its expected clinical outcomes for that individual. A drug may very well make sense at the aggregate level but be a very bad choice for a particular individual when compared with alternatives. It’s right that this process should be deliberative and not solely driven by an algorithm, but it’s also important to maintain transparent and consistent decision making. Multi-criteria decision analysis (MCDA) has been proposed as a means of achieving this, and it can be used to take into account the uncertainty associated with clinical outcomes. In this study the authors present an approach that also incorporates random preference variation along with parameter uncertainty in both preferences and clinical evidence. The model defines a value function and estimates the impact of uncertainty using probabilistic Monte Carlo simulation, which in turn estimates the mean value of each possible treatment in the population. Treatments can therefore be ranked according to patients’ preferences, along with an estimate of the uncertainty associated with this ranking. To demonstrate the utility of the model it is applied to an example for the relative value of HAARTs for HIV, with parameters derived from clinical evaluations and stated preferences studies. It’s nice to see that the authors also provide their R script. One headline finding seems to be that this approach is likely to demonstrate just how much uncertainty is involved that might not previously have been given much attention. It could therefore help steer us towards more valuable research in the future. And it could be used to demonstrate that optimal decisions might change when all sources of uncertainty are considered. Clearly a potential application of this method is in the realm of personalised medicine, which is slowly but inevitably reaching beyond the confines of pharmacogenomics.

Communal sharing and the provision of low-volume high-cost health services: results of a survey. PharmacoEconomics – Open Published 4th November 2016

One of the distributional concerns we might have about the QALY-maximisation approach is its implications for people with rare diseases. Drugs for rare diseases are often expensive (because the marginal cost is likely to be higher) and therefore less cost-effective. There is mixed evidence about whether or not people exhibit a preference for redistributive allocation of QALY-creating resources according to rarity. Of course, the result you get from such studies is dependent on the question you ask. In order to ask the right question it’s important to understand the mechanisms by which people might prefer allocation of additional resources to services for rare diseases. One suggestion in the literature is the preservation of hope. This study presents another, based on the number of people sharing the cost. So imagine a population of 1000 people, and all those people share the cost of health care. For a rare disease, more people will share the cost of the treatment per person treated. So if 10 people have the disease, that’s 100 payers per recipient. If 100 people have the disease then it’s just 10 payers per recipient. The idea is that people prefer a situation in which more people share the cost, and on that basis prefer to allocate resources to rare diseases. A web-based survey was conducted in Australia in which 702 people were asked to divide a budget between a small patient group with a high-cost illness and a large patient group with a low-cost illness. There were also a set of questions in which respondents indicated the importance of 6 possible influences on their decisions. The findings show that people did choose to allocate more funds to the rarer disease, despite the reduced overall health gain. This suggests that people do have a preference for wider cost sharing, which could explain extra weight being given to rare diseases. I think it’s a good idea that deserves more research, but for me there are a few problems with the study. Much of the effect could be explained by people’s non-linear valuations of risk, as the scenario highlighted that the respondents themselves would be at risk of the disease. We also can’t clearly differentiate between an effect due to the rarity of the disease (and associated cost sharing) and an effect due to the severity of the disease.

The challenge of conditional reimbursement: stopping reimbursement can be more difficult than not starting in the first place! Value in Health Published 3rd November 2016

If anything’s going to make me read a paper, it’s an exclamation mark! Conditional reimbursement of technologies that are probably effective but probably not cost-effective can be conducted in a rational way in order to generate research findings and benefit social welfare in the long run. But that can only hold true if those technologies subsequently found (through more research) to be ineffective or too costly are then made unavailable. Otherwise conditional reimbursement agreements will do more harm than good. This study uses discrete choice experiments to compare public (n=1169) and potential policymaker (n=90) values associated with the removal of an available treatment compared with non-reimbursement of a new treatment. The results showed (in addition to some other common findings) that both the public and policymakers preferred reimbursement of an existing treatment over the reimbursement of a new treatment, and were willing to accept an ICER of more than €7,000 higher for an existing treatment. Though the DCE found it to be a significant determinant, 60% of policymakers reported that they thought that reimbursement status was unimportant, so there may be some cognitive dissonance going on there. The most obvious (and probably most likely) explanation for the observed preference for currently reimbursed treatments is loss aversion. But it could also be that people recognise real costs associated with ending reimbursement that are not reflected in either the QALY estimates or the costs to the health system. Whatever the explanation, HTA agencies need to bear this in mind when using conditional reimbursement agreements.

Head-to-head comparison of health-state values derived by a probabilistic choice model and scores on a visual analogue scale. The European Journal of Health Economics [PubMed] Published 2nd November 2016

I’ve always had a fondness for a good old VAS as a direct measure of health state (dare we say utility) values, despite the limitations of the approach. This study compares discrete choices for EQ-5D-5L states with VAS valuations – thus comparing indirect and direct health state valuations – in Canada, the USA, England and The Netherlands (n=1775). Each respondent had to make a forced choice between two EQ-5D-5L health states and then assess both states on a single VAS. Ten different pairs were completed by each respondent. The two different approaches correlated strongly within and across countries, as we might expect. And pairs of EQ-5D-5L states that were valued relatively low or high in the discrete choice model were also valued accordingly in the VAS. But the relationship between the two approaches was non-linear in that values differed more at the ends of the scale, with poor health states valued more differently in the choice model and good health states valued more differently on the VAS. This probably just reflects some of the biases observed in the use of VAS that are already well-documented, particularly context bias and end-state aversion. This study clearly suggests (though does not by itself prove) that discrete choice models are a better choice for health state valuation… but the VAS ain’t dead yet.