Chris Sampson’s journal round-up for 16th December 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.

MCDA-based deliberation to value health states: lessons learned from a pilot study. Health and Quality of Life Outcomes [PubMed] Published 1st July 2019

The rejection of the EQ-5D-5L value set for England indicates something of a crisis in health state valuation. Evidently, there is a lack of trust in the quantitative data and methods used. This is despite decades of methodological development. Perhaps we need a completely different approach. Could we instead develop a value set using qualitative methods?

A value set based on qualitative research aligns with an idea forwarded by Daniel Hausman, who has argued for the use of deliberative approaches. This could circumvent the problems associated with asking people to give instant (and possibly ill-thought-out) responses to preference elicitation surveys. The authors of this study report on the first ever (pilot) attempt to develop a consensus value set using methods of multi-criteria decision analysis (MCDA) and deliberation. The study attempts to identify a German value set for the SF-6D.

The study included 34 students in a one-day conference setting. A two-step process was followed for the MCDA using MACBETH (the Measuring Attractiveness by a Categorical Based Evaluation Technique), which uses pairwise comparisons to derive numerical scales without quantitative assessments. First, a scoring procedure was conducted for each of the six dimensions. Second, a weighting was identified for each dimension. After an introductory session, participants were allocated into groups of five or six and each group was tasked with scoring one SF-6D dimension. Within each group, consensus was achieved. After these group sessions, all participants were brought together to present and validate the results. In this deliberation process, consensus was achieved for all domains except pain. Then the weighting session took place, but resulted in no consensus. Subsequent to the one-day conference, a series of semi-structured interviews were conducted with moderators. All the sessions and interviews were recorded, transcribed, and analysed qualitatively.

In short, the study failed. A consensus value set could not be identified. Part of the problem was probably in the SF-6D descriptive system, particularly in relation to pain, which was interpreted differently by different people. But the main issue was that people had different opinions and didn’t seem willing to move towards consensus with a societal perspective in mind. Participants broadly fell into three groups – one in favour of prioritising pain and mental health, one opposed to trading-off SF-6D dimensions and favouring equal weights, and another group that was not willing to accept any trade-offs.

Despite its apparent failure, this seems like an extremely useful and important study. The authors provide a huge amount of detail regarding what they did, what went well, and what might be done differently next time. I’m not sure it will ever be possible to get a group of people to reach a consensus on a value set. The whole point of preference-based measures is surely that different people have different priorities, and they should be expected to disagree. But I think we should expect that the future of health state valuation lies in mixed methods. There might be more success in a qualitative and deliberative approach to scoring combined with a quantitative approach to weighting, or perhaps a qualitative approach informed by quantitative data that demands trade-offs. Whatever the future holds, this study will be a valuable guide.

Preference-based health-related quality of life outcomes associated with preterm birth: a systematic review and meta-analysis. PharmacoEconomics [PubMed] Published 9th December 2019

Premature and low birth weight babies can experience a whole host of negative health outcomes. Most studies in this context look at short-term biomedical assessments or behavioural and neurodevelopmental indicators. But some studies have sought to identify the long-term consequences on health-related quality of life by identifying health state utility values. This study provides us with a review and meta-analysis of such values.

The authors screened 2,139 articles from their search and included 20 in the review. Lots of data were extracted from the articles, which is helpfully tabulated in the paper. The majority of the studies included adolescents and focussed on children born very preterm or at very low birth weight.

For the meta-analysis, the authors employed a linear mixed-effects meta-regression, which is an increasingly routine approach in this context. The models were used to estimate the decrement in utility values associated with preterm birth or low birth weight, compared with matched controls. Conveniently, all but one of the studies used a measure other than the HUI2 or HUI3, so the analysis was restricted to these two measures. Preterm birth was associated with an average decrement of 0.066 and extremely low birth weight with a decrement of 0.068. The mean estimated utility scores for the study groups was 0.838, compared with 0.919 for the control groups.

Reviews of utility values are valuable as they provide modellers with a catalogue of potential parameters that can be selected in a meaningful and transparent way. Even though this is a thorough and well-reported study, it’s a bit harder to see how its findings will be used. Most reviews of utility values relate to a particular disease, which might be prevented or ameliorated by treatment, and the value of this treatment depends on the utility values selected. But how will these utility values be used? The avoidance of preterm or low-weight birth is not the subject of most evaluations in the neonatal setting. Even if it was, how valuable are estimates from a single point in adolescence? The authors suggest that future research should seek to identify a trajectory of utility values over the life course. But, even if we could achieve this, it’s not clear to me how this should complement utility values identified in relation to the specific health problems experienced by these people.

The new and non-transparent Cancer Drugs Fund. PharmacoEconomics [PubMed] Published 12th December 2019

Not many (any?) health economists liked the Cancer Drugs Fund (CDF). It was set-up to give special treatment to cancer drugs, which weren’t assessed on the same basis as other drugs being assessed by NICE. In 2016, the CDF was brought within NICE’s remit, with medicines available through the CDF requiring a managed access agreement. This includes agreements on data collection and on payments by the NHS during the period. In this article, the authors contend that the new CDF process is not sufficiently transparent.

Three main issued are raised: i) lack of transparency relating to the value of CDF drugs, ii) lack of transparency relating to the cost of CDF drugs, and iii) the amount of time that medicines remain on the CDF. The authors tabulate the reporting of ICERs according to the decisions made, showing that the majority of treatment comparisons do not report ICERs. Similarly, the time in the CDF is tabulated, with many indications being in the CDF for an unknown amount of time. In short, we don’t know much about medicines going through the CDF, except that they’re probably costing a lot.

I’m a fan of transparency, in almost all contexts. I think it is inherently valuable to share information widely. It seems that the authors of this paper do too. A lack of transparency in NICE decision-making is a broader problem that arises from the need to protect commercially sensitive pricing agreements. But what this paper doesn’t manage to do is to articulate why anybody who doesn’t support transparency in principle should care about the CDF in particular. Part of the authors’ argument is that the lack of transparency prevents independent scrutiny. But surely NICE is the independent scrutiny? The authors argue that it is a problem that commissioners and the public cannot assess the value of the medicines, but it isn’t clear why that should be a problem if they are not the arbiters of value. The CDF has quite rightly faced criticism over the years, but I’m not convinced that its lack of transparency is its main problem.

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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.

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