Chris Sampson’s journal round-up for 2nd 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.

The treatment decision under uncertainty: the effects of health, wealth and the probability of death. Journal of Health Economics Published 16th November 2019

It’s important to understand how people make decisions about treatment. At the end of life, the question can become a matter of whether to have treatment or to let things take their course such that you end up dead. In order to consider this scenario, the author of this paper introduces the probability of death to some existing theoretical models of decision-making under uncertainty.

The diagnostic risk model and the therapeutic risk model can be used to identify risk thresholds that determine decisions about treatment. The diagnostic model relates to the probability that disease is present and the therapeutic model relates to the probability that treatment is successful. The new model described in this paper builds on these models to consider the impact on the decision thresholds of i) initial health state, ii) probability of death, and iii) wealth. The model includes wealth after death, in the form of a bequest. Limited versions of the model are also considered, excluding the bequest and excluding wealth (described as a ‘QALY model’). Both an individual perspective and an aggregate perspective are considered by excluding and including the monetary cost of diagnosis and treatment, to allow for a social insurance type setting.

The comparative statics show a lot of ambiguity, but there are a few things that the model can tell us. The author identifies treatment as having an ‘insurance effect’, by reducing diagnostic risk, a ‘protective effect’, by lowering the probability of death, and a risk-increasing effect associated with therapeutic risk. A higher probability of death increases the propensity for treatment in both the no-bequest model and the QALY model, because of the protective effect of treatment. In the bequest model, the impact is ambiguous, because treatment costs reduce the bequest. In the full model, wealthier individuals will choose to undergo treatment at a lower probability of success because of a higher marginal utility for survival, but the effect becomes ambiguous if the marginal utility of wealth depends on health (which it obviously does).

I am no theoretician, so it can take me a long time to figure these things out in my head. For now, I’m not convinced that it is meaningful to consider death in this way using a one-period life model. In my view, the very definition of death is a loss of time, which plays little or no part in this model. But I think my main bugbear is the idea that anybody’s decision about life saving treatment is partly determined by the amount of money they will leave behind. I find this hard to believe. The author links the finding that a higher probability of death increases treatment propensity to NICE’s end of life premium. Though I’m not convinced that the model has anything to do with NICE’s reasoning on this matter.

Moving toward evidence-based policy: the value of randomization for program and policy implementation. JAMA [PubMed] Published 15th November 2019

Evidence-based policy is a nice idea. We should figure out whether something works before rolling it out. But decision-makers (especially politicians) tend not to think in this way, because doing something is usually seen to be better than doing nothing. The authors of this paper argue that randomisation is the key to understanding whether a particular policy creates value.

Without evidence based on random allocation, it’s difficult to know whether a policy works. This, the authors argue, can undermine the success of effective interventions and allow harmful policies to persist. A variety of positive examples are provided from US healthcare, including trials of Medicare bundled payments. Apparently, such trials increased confidence in the programmes’ effects in a way that post hoc evaluations cannot, though no evidence of this increased confidence is actually provided. Policy evaluation is not always easy, so the authors describe four preconditions for the success of such studies: i) early engagement with policymakers, ii) willingness from policy leaders to support randomisation, iii) timing the evaluation in line with policymakers’ objectives, and iv) designing the evaluation in line with the realities of policy implementation.

These are sensible suggestions, but it is not clear why the authors focus on randomisation. The paper doesn’t do what it says on the tin, i.e. describe the value of randomisation. Rather, it explains the value of pre-specified policy evaluations. Randomisation may or may not deserve special treatment compared with other analytical tools, but this paper provides no explanation for why it should. The authors also suggest that people are becoming more comfortable with randomisation, as large companies employ experimental methods, particularly on the Internet with A/B testing. I think this perception is way off and that most people feel creeped out knowing that the likes of Facebook are experimenting on them without any informed consent. In the authors’ view, it being possible to randomise is a sufficient basis on which to randomise. But, considering the ethics, as well as possible methodological contraindications, it isn’t clear that randomisation should become the default.

A new tool for creating personal and social EQ-5D-5L value sets, including valuing ‘dead’. Social Science & Medicine Published 30th November 2019

Nobody can agree on the best methods for health state valuation. Or, at least, some people have disagreed loud enough to make it seem that way. Novel approaches to health state valuation are therefore welcome. Even more welcome is the development and testing of methods that you can try at home.

This paper describes the PAPRIKA method (Potentially All Pairwise RanKings of all possible Alternatives) of discrete choice experiment, implemented using 1000Minds software. Participants are presented with two health states that are defined in terms of just two dimensions, each lasting for 10 years, and asked to choose between them. Using the magical power of computers, an adaptive process identifies further choices, automatically ranking states using transitivity so that people don’t need to complete unnecessary tasks. In order to identify where ‘dead’ sits on the scale, a binary search procedure asks participants to compare EQ-5D states with being dead. What’s especially cool about this process is that everybody who completes it is able to view their own personal value set. These personal value sets can then be averaged to identify a social value set.

The authors used their tool to develop an EQ-5D-5L value set for New Zealand (which is where the researchers are based). They recruited 5,112 people in an online panel, such that the sample was representative of the general public. Participants answered 20 DCE questions each, on average, and almost half of them said that they found the questions difficult to answer. The NZ value set showed that anxiety/depression was associated with the greatest disutility, though each dimension has a notably similar level of impact at each level. The value set correlates well with numerous existing value sets.

The main limitation of this research seems to be that only levels 1, 3, and 5 of each EQ-5D-5L domain were included. Including levels 2 and 4 would more than double the number of questions that would need to be answered. It is also concerning that more than half of the sample was excluded due to low data quality. But the authors do a pretty good job of convincing us that this is for the best. Adaptive designs of this kind could be the future of health state valuation, especially if they can be implemented online, at low cost. I expect we’ll be seeing plenty more from PAPRIKA.

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Chris Sampson’s journal round-up for 28th October 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.

Spatial competition and quality: evidence from the English family doctor market. Journal of Health Economics [RePEc] Published 17th October 2019

Researchers will never stop asking questions about the role of competition in health care. There’s a substantial body of literature now suggesting that greater competition in the context of regulated prices may bring some quality benefits. But with weak indicators of quality and limited generalisability, it isn’t a closed case. One context in which evidence has been lacking is in health care beyond the hospital. In the NHS, an individual’s choice of GP practice is perhaps the context in which quality can be observed and choice most readily (and meaningfully) exercised. That’s where this study comes in. Aside from the horrible format of a ‘proper economics’ paper (where we start with spoilers and climax with robustness tests), it’s a good read.

The study relies on a measure of competition based on the number of rival GPs within a 2km radius. Number of GPs, that is, rather than number of practices. This is important, as the number of GPs per practice has been increasing. About 75% of a practice’s revenues are linked to the number of patients registered, wherein lies the incentive to compete with other practices for patients. And, in this context, research has shown that patient choice is responsive to indicators of quality. The study uses data for 2005-2012 from all GP practices in England, making it an impressive data set.

The measures of quality come from the Quality and Outcomes Framework (QOF) and the General Practice Patient Survey (GPPS) – the former providing indicators of clinical quality and the latter providing indicators of patient experience. A series of OLS regressions are run on the different outcome measures, with practice fixed effects and various characteristics of the population. The models show that all of the quality indicators are improved by greater competition, but the effect is very small. For example, an extra competing GP within a 2km radius results in 0.035% increase in the percentage of the population for whom the QOF indicators have been achieved. The effects are a little stronger for the patient satisfaction indicators.

The paper reports a bunch of important robustness checks. For instance, the authors try to test whether practices select their locations based on the patient casemix, finding no evidence that they do. The authors even go so far as to test the impact of a policy change, which resulted in an exogenous increase in the number of GPs in some areas but not others. The main findings seem to have withstood all the tests. They also try out a lagged model, which gives similar results.

The findings from this study slot in comfortably with the existing body of research on the role of competition in the NHS. More competition might help to achieve quality improvement, but it hardly seems worthy of dedicating much effort or, importantly, much expense to the cause.

Worth living or worth dying? The views of the general public about allowing disabled children to die. Journal of Medical Ethics [PhilPapers] [PubMed] Published 15th October 2019

Recent years have seen a series of cases in the UK where (usually very young) children have been so unwell and with such a severe prognosis that someone (usually a physician) has judged that continued treatment is not warranted and that the child should be allowed to die. These cases have generated debate and outrage in the media. But what do people actually think?

This study recruited members of the public in the UK (n=130) to an online panel and asked about the decisions that participants would support. The survey had three parts. The first part set out six scenarios of hospitalised infants, which varied in terms of the infants’ physical and sensory abilities, cognitive capacity, level of suffering, and future prospects. Some of the cases approximated real cases that have received media coverage, and the participants were asked whether they thought that withdrawing treatment was justified in each case. In the second part of the survey, participants were asked about the factors that they believed were important in making such decisions. In the third part, participants answered a few questions about themselves and answered the Oxford Utilitarianism Scale.

The authors set up the concept of a ‘life not worth living’, based on the idea that net future well-being is ‘negative’, and supposing the individual’s own judgement were they able to provide it. In the first part of the survey, 88% indicated that life would be worse than death in at least one of the cases. In such cases, 65% thought that treatment withdrawal was ethically obligatory, while 33% thought that either decision was acceptable. Pain was considered the most important factor in making such decisions, followed by the presence of pleasure. Perhaps predictably for health economists familiar with the literature, about 42% of people thought that resources should be considered in the decision, while 40% thought they shouldn’t.

The paper includes an extensive discussion, with plenty of food for thought. In particular, it discusses the ways in which the findings might inform the debate between the ‘zero line view’, whereby treatment should be withdrawn at the point where life has no benefit, and the ‘threshold view’, which establishes a grey zone of ethical uncertainty, in which either decision is ethically acceptable. To some extent, the findings of this study support the need for a threshold approach. Ethical questions are rarely black and white.

How is the trade-off between adverse selection and discrimination risk affected by genetic testing? Theory and experiment. Journal of Health Economics [PubMed] [RePEc] Published 1st October 2019

A lot of people are worried about how knowledge of their genetic information could be used against them. The most obvious scenario is one in which insurers increase premiums – or deny coverage altogether – on the basis of genetic risk factors. There are two key regulatory options in this context – disclosure duty, whereby individuals are obliged to tell insurers about the outcome of genetic tests, or consent law, whereby people can keep the findings to themselves. This study explores how people behave under each of these regulations.

The authors set up a theoretical model in which individuals can choose whether to purchase a genetic test that can identify them as being either high-risk or low-risk of developing some generic illness. The authors outline utility functions under disclosure duty and consent law. Under disclosure duty, individuals face a choice between the certainty of not knowing their risk and receiving pooled insurance premiums, or a lottery in which they have to disclose their level of risk and receive a higher or lower premium accordingly. Under consent law, individuals will only reveal their test results if they are at low risk, thus securing lower premiums and contributing to adverse selection. As a result, individuals will be more willing to take a test under consent law than under disclosure duty, all else equal.

After setting out their model (at great length), the authors go on to describe an experiment that they conducted with 67 economics students, to elicit preferences within and between the different regulatory settings. The experiment was set up in a very generic way, not related to health at all. Participants were presented with a series of tasks across which the parameters representing the price of the test and the pooled premium were varied. All of the authors’ hypotheses were supported by the experiment. More people took tests under consent law. Higher test prices reduce the number of people taking tests. If prices are high enough, people will prefer disclosure duty. The likelihood that people take tests under consent law is increasing with the level of adverse selection. And people are very sensitive to the level of discrimination risk under disclosure duty.

It’s an interesting study, but I’m not sure how much it can tell us about genetic testing. Framing the experiment as entirely unrelated to health seems especially unwise. People’s risk preferences may be very different in the domain of real health than in the hypothetical monetary domain. In the real world, there’s a lot more at stake.

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Chris Sampson’s journal round-up for 25th March 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.

How prevalent are implausible EQ-5D-5L health states and how do they affect valuation? A study combining quantitative and qualitative evidence. Value in Health Published 15th March 2019

The EQ-5D-5L is able to describe a lot of different health states (3,125, to be precise), including some that don’t seem likely to ever be observed. For example, it’s difficult to conceive of somebody having extreme problems in pain/discomfort and anxiety/depression while also having no problems with usual activities. Valuation studies exclude these kinds of states because it’s thought that their inclusion could negatively affect the quality of the data. But there isn’t much evidence to help us understand how ‘implausibility’ might affect valuations, or which health states are seen as implausible.

This study is based on an EQ-5D-5L valuation exercise with 890 students in China. The valuation was conducted using the EQ VAS, rather than the standard EuroQol valuation protocol, with up to 197 states being valued by each student. Two weeks after conducting the valuation, participants were asked to indicate (yes or no) whether or not the states were implausible. After that, a small group were invited to participate in a focus group or interview.

No health state was unanimously identified as implausible. Only four states were unanimously rated as not being implausible. 910 of the 3,125 states defined by the EQ-5D-5L were rated implausible by at least half of the people who rated them. States more commonly rated as implausible were of moderate severity overall, but with divergent severities between states (i.e. 5s and 1s together). Overall, implausibility was associated with lower valuations.

Four broad themes arose from the qualitative work, namely i) reasons for implausibility, ii) difficulties in valuing implausible states, iii) strategies for valuing implausible states, and iv) values of implausible states. Some states were considered to have logical conflicts, with some dimensions being seen as mutually inclusive (e.g. walking around is a usual activity). The authors outline the themes and sub-themes, which are a valuable contribution to our understanding of what people think when they complete a valuation study.

This study makes plain the fact that there is a lot of heterogeneity in perceptions of implausibility. But the paper doesn’t fully address the issue of what plausibility actually means. The authors describe it as subjective. I’m not sure about that. For me, it’s an empirical question. If states are observed in practice, they are plausible. We need meaningful valuations of states that are observed, so perhaps the probability of a state being included in a valuation exercise should correspond to the probability of it being observed in reality. The difficulty of valuing a state may relate to plausibility – as this work shows – but that difficulty is a separate issue. Future research on implausible health states should be aligned with research on respondents’ experience of health states. Individuals’ judgments about the plausibility of health states (and the accuracy of those judgments) will depend on individuals’ experience.

An EU-wide approach to HTA: an irrelevant development or an opportunity not to be missed? The European Journal of Health Economics [PubMed] Published 14th March 2019

The use of health technology assessment is now widespread across the EU. The European Commission recently saw an opportunity to rationalise disparate processes and proposed new regulation for cooperation in HTA across EU countries. In particular, the proposal targets cooperation in the assessment of the relative effectiveness of pharmaceuticals and medical devices. A key purpose is to reduce duplication of efforts, but it should also make the basis for national decision-making more consistent.

The authors of this editorial argue that the regulation needs to provide more clarity, in the definition of clinical value, and of the quality of evidence that is acceptable, which vary across EU Member States. There is also a need for the EU to support early dialogue and scientific advice. There is also scope to support the generation and use of real-world evidence. The authors also argue that the challenges for medical device assessment are particularly difficult because many medical device companies cannot – or are not incentivised to – generate sufficient evidence for assessment.

As the final paragraph argues, EU cooperation in HTA isn’t likely to be associated with much in the way of savings. This is because appraisals will still need to be conducted in each country, as well as an assessment of country-specific epidemiology and other features of the population. The main value of cooperation could be in establishing a stronger position for the EU in negotiating in matters of drug design and evidence requirements. Not that we needed any more reasons to stop Brexit.

Patient-centered item selection for a new preference-based generic health status instrument: CS-Base. Value in Health Published 14th March 2019

I do not believe that we need a new generic measure of health. This paper was always going to have a hard time convincing me otherwise…

The premise for this work is that generic preference-based measures of health (such as the EQ-5D) were not developed with patients. True. So the authors set out to create one that is. A key feature of this study is the adoption of a framework that aligns with the multiattribute preference response model, whereby respondents rate their own health state relative to another. This is run through a mobile phone app.

The authors start by extracting candidate items from existing health frameworks and generic measures (which doesn’t seem to be a particularly patient-centred approach) and some domains were excluded for reasons that are not at all clear. 47 domains were included after overlapping candidates were removed. The 47 were classified as physical, mental, social, or ‘meta’. An online survey was conducted by a market research company. 2,256 ‘patients’ (people with diseases or serious complaints) were asked which 9 domains they thought were most important. Why 9? Because the authors figured it was the maximum that could fit on the screen of a mobile phone.

Of the candidate items, 5 were regularly selected in the survey: pain, personal relationships, fatigue, memory, and vision. Mobility and daily activities were also judged important enough to be included. Independence and self-esteem were added as paired domains and hearing was paired with the vision domain. The authors also added anxiety/depression as a pair of domains because they thought it was important. Thus, 12 items were included altogether, of which 6 were parts of pairs. Items were rephrased according to the researchers’ preferences. Each item was given 4 response levels.

It is true to say (as the authors do) that most generic preference-based measures (most notably the EQ-5D) were not developed with direct patient input. The argument goes that this somehow undermines the measure. But there are a) plenty of patient-centred measures for which preference-based values could be created and b) plenty of ways in which existing measures can be made patient-centred post hoc (n.b. our bolt-on study).

Setting aside my scepticism about the need for a new measure, I have a lot of problems with this study and with the resulting CS-Base instrument. The defining feature of its development seems to be arbitrariness. The underlying framework (as far as it is defined) does not seem well-grounded. The selection of items was largely driven by researchers. The wording was entirely driven by the researchers. The measure cannot justifiably be called ‘patient-centred’. It is researcher-centred, even if the researchers were able to refer to a survey of patients. And the whole thing has nothing whatsoever to do with preferences. The measure may prove fantastic at capturing health outcomes, but if it does it will be in spite of the methods used for its development, not because of them. Ironically, that would be a good advert for researcher-centred outcome development.

Proximity to death and health care expenditure increase revisited: a 15-year panel analysis of elderly persons. Health Economics Review [PubMed] [RePEc] Published 11th March 2019

It is widely acknowledged that – on average – people incur a large proportion of their lifetime health care costs in the last few years of their life. But there’s still a question mark over whether it is proximity to death that drives costs or age-related morbidity. The two have very different implications – we want people to be living for longer, but we probably don’t want them to be dying for longer. There’s growing evidence that proximity to death is very important, but it isn’t clear how important – if at all – ageing is. It’s important to understand this, particularly in predicting the impacts of demographic changes.

This study uses Swiss health insurance claims data for around 104,000 people over the age of 60 between 1996 and 2011. Two-part regression models were used to estimate health care expenditures conditional on them being greater than zero. The author analysed both birth cohorts and age classes to look at age-associated drivers of health care expenditure.

As expected, health care expenditures increased with age. The models imply that proximity-to-death has grown in importance over time. For the 1931-35 birth cohort, for example, the proportion of expenditures explained by proximity-to-death rose from 19% to 31%. Expenditures were partly explained by morbidity, and this effect appeared to be relatively constant over time. Thus, proximity to death is not the only determinant of rising expenditures (even if it is an important one). Looking at different age classes over time, there was no clear picture in the trajectory of health care expenditures. For the oldest age groups (76-85), health care expenditures were growing, but for some of the younger groups, costs appeared to be decreasing over time. This study paints a complex picture of health care expenditures, calling for complex policy responses. Part of this could be supporting people to commence palliative care earlier, but there is also a need for more efficient management of chronic illness over the long term.

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