Chris Sampson’s journal round-up for 23rd September 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.

Can you repeat that? Exploring the definition of a successful model replication in health economics. PharmacoEconomics [PubMed] Published 18th September 2019

People talk a lot about replication and its role in demonstrating the validity and reliability of analyses. But what does a successful replication in the context of cost-effectiveness modelling actually mean? Does it mean coming up with precisely the same estimates of incremental costs and effects? Does it mean coming up with a model that recommends the same decision? The authors of this study sought to bring us closer to an operational definition of replication success.

There is potentially much to learn from other disciplines that have a more established history of replication. The authors reviewed literature on the definition of ‘successful replication’ across all disciplines, and used their findings to construct a variety of candidate definitions for use in the context of cost-effectiveness modelling in health. Ten definitions of a successful replication were pulled out of the cross-disciplinary review, which could be grouped into ‘data driven’ replications and ‘experimental’ replications – the former relating to the replication of analyses and the latter relating to the replication of specific observed effects. The ten definitions were from economics, biostatistics, cognitive science, psychology, and experimental philosophy. The definitions varied greatly, with many involving subjective judgments about the proximity of findings. A few studies were found that reported on replications of cost-effectiveness models and which provided some judgment on the level of success. Again, these were inconsistent and subjective.

Quite reasonably, the authors judge that the lack of a fixed definition of successful replication in any scientific field is not just an oversight. The threshold for ‘success’ depends on the context of the replication and on how the evidence will be used. This paper provides six possible definitions of replication success for use in cost-effectiveness modelling, ranging from an identical replication of the results, through partial success in replicating specific pathways within a given margin of error, to simply replicating the same implied decision.

Ultimately, ‘data driven’ replications are a solution to a problem that shouldn’t exist, namely, poor reporting. This paper mostly convinced me that overall ‘success’ isn’t a useful thing to judge in the context of replicating decision models. Replication of certain aspects of a model is useful to evaluate. Whether the replication implied the same decision is a key thing to consider. Beyond this, it is probably worth considering partial success in replicating specific parts of a model.

Differential associations between interpersonal variables and quality-of-life in a sample of college students. Quality of Life Research [PubMed] Published 18th September 2019

There is growing interest in the well-being of students and the distinct challenges involved in achieving good mental health and addressing high levels of demand for services in this group. Students go through many changes that might influence their mental health, prominent among these is the change to their social situation.

This study set out to identify the role of key interpersonal variables on students’ quality of life. The study recruited 1,456 undergraduate students from four universities in the US. The WHOQOL measure was used for quality of life and a barrage of measures were used to collect information on loneliness, social connectedness, social support, emotional intelligence, intimacy, empathic concern, and more. Three sets of analyses of increasing sophistication were conducted, from zero-order correlations between each measure and the WHOQOL, to a network analysis using a Gaussian Graphical Model to identify both direct and indirect relationships while accounting for shared variance.

In all analyses, loneliness stuck out as the strongest driver of quality of life. Social support, social connectedness, emotional intelligence, intimacy with one’s romantic partner, and empathic concern were also significantly associated with quality of life. But the impact of loneliness was greatest, with other interpersonal variables influencing quality of life through their impact on loneliness.

This is a well-researched and reported study. The findings are informative to student support and other services that seek to improve the well-being of students. There is reason to believe that such services should recognise the importance of interpersonal determinants of well-being and in particular address loneliness. But it’s important to remember that this study is only as good as the measures it uses. If you don’t think WHOQOL is adequately measuring student well-being, or you don’t think the UCLA Loneliness Scale tells us what we need to know, you might not want these findings to influence practice. And, of course, the findings may not be generalisable, as the extent to which different interpersonal variables affect quality of life is very likely dependent on the level of service provision, which varies greatly between different universities, let alone countries.

Affordability and non-perfectionism in moral action. Ethical Theory and Moral Practice [PhilPapers] Published 14th September 2019

The ‘cost-effective but unaffordable’ challenge has been bubbling for a while now, at least since sofosbuvir came on the scene. This study explores whether “we can’t afford it” is a justifiable position to take. The punchline is that, no, affordability is not a sound ethical basis on which to support or reject the provision of a health technology. I was extremely sceptical when I first read the claim. If we can’t afford it, it’s impossible, and how can there by a moral imperative in an impossibility? But the authors proceeded to convince me otherwise.

The authors don’t go into great detail on this point, but it all hinges on divisibility. The reason that a drug like sofosbuvir might be considered unaffordable is that loads of people would be eligible to receive it. If sofosbuvir was only provided to a subset of this population, it could be affordable. On this basis, the authors propose the ‘principle of non-perfectionism’. This states that not being able to do all the good we can do (e.g. provide everyone who needs it with sofosbuvir) is not a reason for not doing some of the good we can do. Thus, if we cannot support provision of a technology to everyone who could benefit from it, it does not follow (ethically) to provide it to nobody, but rather to provide it to some people. The basis for selecting people is not of consequence to this argument but could be based on a lottery, for example.

Building on this, the authors explain to us why this is wrong, with the notion of ‘numerical discrimination’. They argue that it is not OK to prioritise one group over another simply because we can meet the needs of everyone within that group as opposed to only some members of the other group. This is exactly what’s happening when we are presented with notions of (un)affordability. If the population of people who could benefit from sofosbuvir was much smaller, there wouldn’t be an issue. But the simple fact that the group is large does not make it morally permissible to deny cost-effective treatment to any individual member within that group. You can’t discriminate against somebody because they are from a large population.

I think there are some tenuous definitions in the paper and some questionable analogies. Nevertheless, the authors succeeded in convincing me that total cost has no moral weight. It is irrelevant to moral reasoning. We should not refuse any health technology to an entire population on the grounds that it is ‘unaffordable’. The authors frame it as a ‘mistake in moral mathematics’. For this argument to apply in the HTA context, it relies wholly on the divisibility of health technologies. To some extent, NICE and their counterparts are in the business of defining models of provision, which might result in limited use criteria to get around the affordability issue. Though these issues are often handled by payers such as NHS England.

The authors of this paper don’t consider the implications for cost-effectiveness thresholds, but this is where my thoughts turned. Does the principle of non-perfectionism undermine the morality of differentiating cost-effectiveness thresholds according to budget impact? I think it probably does. Reducing the threshold because the budget impact is great will result in discrimination (‘numerical discrimination’) against individuals simply because they are part of a large population that could benefit from treatment. This seems to be the direction in which we’re moving. Maybe the efficiency cart is before the ethical horse.

Credits

Equal care for the elderly: A swing and a miss?

This month the Equality Act 2010 was accepted by the Department of Health here in the UK, with no amendments. The NHS can no longer discriminate based on age (aside from a few reasonable exceptions). But can this new legislation actually make any difference? An interesting article from Age UK suggests possibly not.

Why are the elderly treated so badly?

I agree with Age UK. Legislation is not what was lacking; the NHS as an organisation does not promote age discrimination. As an institution it does not provide incentives to discriminate by age (not that I know of anyway). I believe the problem exists on a personal level, in the attitude of health care professionals. The elderly are perceived as being less deserving of care than their more recently born counterparts. In fairness to said health care professionals, I don’t think they are entirely to blame for this. Society values the elderly less. From a human capital perspective the elderly are certainly less valuable to society. Caring for the elderly is also far less efficient; they require greater care, at a greater cost, with a reduced capacity to benefit. They might also have fewer family and friends around them; we might expect a young mother to attract a greater standard of care than an old lady with no dependants. So why should we treat the elderly equally? Well, because it’s right, it’s fair, it’s equitable. Efficiency is not paramount when it is at the expense of equitable outcomes.

The effect of legislation

But the legislation will not work. Legislation CAN change attitudes, but not nearly quick enough. I’m sure many of you have read Freakonomics and its sequel, Superfreakonomics. The latter draws attention to the effect of seat belt legislation in the USA. Most US states introduced seat belt legislation around 1987, when usage was about 42%. 5 years later, in 1992, seatbelt usage was only 62%. 20 years later seat belt use in the USA was still only 82%. And putting on a seat belt is a behaviour that is far more easily changed than your attitude towards care for the elderly.

So, while this legislation is a nice thought, what we really need is a program of education and training for health care professionals, that shows them the importance of good care for the elderly. Even more effective might be a set of incentives, possibly financial, to encourage a high standard of care for the elderly in order to level the playing field. EVEN MORE effective might be a set of disincentives to prevent negligence.

Implications for health economics

I feel that this is a difficult area for health economics; a study so ardently striving for efficiency in health care. Clearly an analysis of the impact of this legislation would be of interest in the future. More generally I think there is a need to develop methods that are able to incorporate the importance of a good standard of care, regardless of treatment outcomes. It would also be good to see health economists evaluating the impact of possible interventions to really improve standard of care of the elderly; such as incentives and training for health care professionals.

So, is this the right course of action? Or is it another useless government PR move? What do you think health economists should be doing to help combat this problem?