Chris Sampson’s journal round-up for 14th 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.

Transparency in health economic modeling: options, issues and potential solutions. PharmacoEconomics [PubMed] Published 8th October 2019

Reading this paper was a strange experience. The purpose of the paper, and its content, is much the same as a paper of my own, which was published in the same journal a few months ago.

The authors outline what they see as the options for transparency in the context of decision modelling, with a focus on open source models and a focus on for whom the details are transparent. Models might be transparent to a small number of researchers (e.g. in peer review), to HTA agencies, or to the public at large. The paper includes a figure showing the two aspects of transparency, termed ‘reach’ and ‘level’, which relate to the number of people who can access the information and the level of detail made available. We provided a similar figure in our paper, using the terms ‘breadth’ and ‘depth’, which is at least some validation of our idea. The authors then go on to discuss five ‘issues’ with transparency: copyright, model misuse, confidential data, software, and time/resources. These issues are framed as questions, to which the authors posit some answers as solutions.

Perhaps inevitably, I think our paper does a better job, and so I’m probably over-critical of this article. Ours is more comprehensive, if nothing else. But I also think the authors make a few missteps. There’s a focus on models created by academic researchers, which oversimplifies the discussion somewhat. Open source modelling is framed as a more complete solution than it really is. The ‘issues’ that are discussed are at points framed as drawbacks or negative features of transparency, which they aren’t. Certainly, they’re challenges, but they aren’t reasons not to pursue transparency. ‘Copyright’ seems to be used as a synonym for intellectual property, and transparency is considered to be a threat to this. The authors’ proposed solution here is to use licensing fees. I think that’s a bad idea. Levying a fee creates an incentive to disregard copyright, not respect it.

It’s a little ironic that both this paper and my own were published, when both describe the benefits of transparency in terms of reducing “duplication of efforts”. No doubt, I read this paper with a far more critical eye than I normally would. Had I not published a paper on precisely the same subject, I might’ve thought this paper was brilliant.

If we recognize heterogeneity of treatment effect can we lessen waste? Journal of Comparative Effectiveness Research [PubMed] Published 1st October 2019

This commentary starts from the premise that a pervasive overuse of resources creates a lot of waste in health care, which I guess might be true in the US. Apparently, this is because clinicians have an insufficient understanding of heterogeneity in treatment effects and therefore assume average treatment effects for their patients. The authors suggest that this situation is reinforced by clinical trial publications tending to only report average treatment effects. I’m not sure whether the authors are arguing that clinicians are too knowledgable and dependent on the research, or that they don’t know the research well enough. Either way, it isn’t a very satisfying explanation of the overuse of health care. Certainly, patients could benefit from more personalised care, and I would support the authors’ argument in favour of stratified studies and the reporting of subgroup treatment effects. The most insightful part of this paper is the argument that these stratifications should be on the basis of observable characteristics. It isn’t much use to your general practitioner if personalisation requires genome sequencing. In short, I agree with the authors’ argument that we should do more to recognise heterogeneity of treatment effects, but I’m not sure it has much to do with waste.

No evidence for a protective effect of education on mental health. Social Science & Medicine Published 3rd October 2019

When it comes to the determinants of health and well-being, I often think back to my MSc dissertation research. As part of that, I learned that a) stuff that you might imagine to be important often isn’t and b) methodological choices matter a lot. Though it wasn’t the purpose of my study, it seemed from this research that higher education has a negative effect on people’s subjective well-being. But there isn’t much research out there to help us understand the association between education and mental health in general.

This study add to a small body of literature on the impact of changes in compulsory schooling on mental health. In (West) Germany, education policy was determined at the state level, so when compulsory schooling was extended from eight to nine years, different states implemented the change at different times between 1949 and 1969. This study includes 5,321 people, with 20,290 person-year observations, from the German Socio-Economic Panel survey (SOEP). Inclusion was based on people being born seven years either side of the cutoff birth year for which the longer compulsory schooling was enacted, with a further restriction to people aged between 50 and 85. The SOEP includes the SF-12 questionnaire, which includes a mental health component score (MCS). There is also an 11-point life satisfaction scale. The authors use an instrumental variable approach, using the policy change as an instrument for years of schooling and estimating a standard two-stage least squares model. The MCS score, life satisfaction score, and a binary indicator for MCS score lower than or equal to 45.6, are all modelled as separate outcomes.

Estimates using an OLS model show a positive and highly significant effect of years of schooling on all three outcomes. But when the instrumental variable model is used, this effect disappears. An additional year of schooling in this model is associated with a statistically and clinically insignificant decrease in the MCS score. Also insignificant was the finding that more years of schooling increases the likelihood of developing symptoms of a mental health disorder (as indicated by the MCS threshold of 45.6) and that life satisfaction is slightly lower. The same model shows a positive effect on physical health, which corresponds with previous research and provides some reassurance that the model could detect an effect if one existed.

The specification of the model seems reasonable and a host of robustness checks are reported. The only potential issue I could spot is that a person’s state of residence at the time of schooling is not observed, and so their location at entry into the sample is used. Given that education is associated with mobility, this could be a problem, and I would have liked to see the authors subject it to more testing. The overall finding – that an additional year of school for people who might otherwise only stay at school for eight years does not improve mental health – is persuasive. But the extent to which we can say anything more general about the impact of education on well-being is limited. What if it had been three years of additional schooling, rather than one? There is still much work to be done in this area.

Scientific sinkhole: the pernicious price of formatting. PLoS One [PubMed] Published 26th September 2019

This study is based on a survey that asked 372 researchers from 41 countries about the time they spent formatting manuscripts for journal submission. Let’s see how I can frame this as health economics… Well, some of the participants are health researchers. The time they spend on formatting journal submissions is time not spent on health research. The opportunity cost of time spent formatting could be measured in terms of health.

The authors focused on the time and wage costs of formatting. The results showed that formatting took a median time of 52 hours per person per year, at a cost of $477 per manuscript or $1,908 per person per year. Researchers spend – on average – 14 hours on formatting a manuscript. That’s outrageous. I have never spent that long on formatting. If you do, you only have yourself to blame. Or maybe it’s just because of what I consider to constitute formatting. The survey asked respondents to consider formatting of figures, tables, and supplementary files. Improving the format of a figure or a table can add real value to a paper. A good figure or table can change a bad paper to a good paper. I’d love to know how the time cost differed for people using LaTeX.

Credits

Decision analytic modelling methods for economic evaluation – advanced course

A three-day course focusing on advanced modelling methods for economic evaluation.

The course is aimed at health economists and those health professionals with experience of health economics who wish to learn about recent methodological developments in cost-effectiveness analysis.

It is designed for participants who are familiar with basic decision modelling who wish to learn how to use more advanced modelling methods. It is particularly suitable for those who have attended our Introduction to Modelling Methods for Health Economic Evaluation.

It is envisaged that participants will currently be undertaking modelling for health economic evaluation within the pharmaceutical and medical device industries, consultancy, academia or the health service.

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