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

Chris Sampson’s journal round-up for 20th May 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.

A new method to determine the optimal willingness to pay in cost-effectiveness analysis. Value in Health Published 17th May 2019

Efforts to identify a robust estimate of the willingness to pay for a QALY have floundered. Mostly, these efforts have relied on asking people about their willingness to pay. In the UK, we have moved away from using such estimates as a basis for setting cost-effectiveness thresholds in the context of resource allocation decisions. Instead, we have attempted to identify the opportunity cost of a QALY, which is perhaps even more difficult, but more easy to justify in the context of a fixed budget. This paper seeks to inject new life into the willingness-to-pay approach by developing a method based on relative risk aversion.

The author outlines the relationship between relative risk aversion and the rate at which willingness-to-pay changes with income. Various candidate utility functions are described with respect to risk preferences, with a Weibull function being adopted for this framework. Estimates of relative risk aversion have been derived from numerous data sources, including labour supply, lottery experiments, and happiness surveys. These estimates from the literature are used to demonstrate the relationship between relative risk aversion and the ‘optimal’ willingness to pay (K), calibrated using the Weibull utility function. For an individual with ‘representative’ parameters plugged into their utility function, K is around twice the income level. K always increases with relative risk aversion.

Various normative questions are raised, including whether a uniform K should be adopted for everybody within the population, and whether individuals should be able to spend on health care on top of public provision. This approach certainly appears to be more straightforward than other approaches to estimating willingness-to-pay in health care, and may be well-suited to decentralised (US-style) resource allocation decision-making. It’s difficult to see how this framework could gain traction in the UK, but it’s good to see alternative approaches being proposed and I hope to see this work developed further.

Striving for a societal perspective: a framework for economic evaluations when costs and effects fall on multiple sectors and decision makers. Applied Health Economics and Health Policy [PubMed] Published 16th May 2019

I’ve always been sceptical of a ‘societal perspective’ in economic evaluation, and I have written in favour of a limited health care perspective. This is mostly for practical reasons. Being sufficiently exhaustive to identify a truly ‘societal’ perspective is so difficult that, in attempting to do so, there is a very high chance that you will produce estimates that are so inaccurate and imprecise that they are more dangerous than useful. But the fact is that there is no single decision-maker when it comes to public expenditure. Governments are made up of various departments, within which there are many levels and divisions. Not everybody will care about the health care perspective, so other objectives ought to be taken into account.

The purpose of this paper is to build on the idea of the ‘impact inventory’, described by the Second Panel on Cost-Effectiveness in Health and Medicine, which sought to address the challenge of multiple objectives. The extended framework described in this paper captures effects and opportunity costs associated with an intervention within various dimensions. These dimensions could (or should) align with decision-makers’ objectives. Trade-offs invariably require aggregation, and this aggregation could take place either within individuals or within dimensions – something not addressed by the Second Panel. The authors describe the implications of each approach to aggregation, providing visual representations of the impact inventory in each case. Aggregating within individuals requires a normative judgement about how each dimension is valued to the individual and then a judgement about how to aggregate for overall population net benefit. Aggregating across individuals within dimensions requires similar normative judgements. Where the chosen aggregation functions are linear and additive, both approaches will give the same results. But as soon as we start to consider equity concerns or more complex aggregation, we’ll see different decisions being indicated.

The authors adopt an example used by the Second Panel to demonstrate the decisions that would be made within a health-only perspective and then decisions that consider other dimensions. There could be a simple extension beyond health, such as including the impact on individuals’ consumption of other goods. Or it could be more complex, incorporating multiple dimensions, sectors, and decision-makers. For the more complex situation, the authors consider the inclusion of the criminal justice sector, introducing the number of crimes averted as an object of value.

It’s useful to think about the limitations of the Second Panel’s framing of the impact inventory and to make explicit the normative judgements involved. What this paper seems to be saying is that cross-sector decision-making is too complex to be adequately addressed by the Second Panel’s impact inventory. The framework described in this paper may be too abstract to be practically useful, and too vague to be foundational. But the complexities and challenges in multi-sector economic evaluation need to be spelt out – there is no simple solution.

Advanced data visualisation in health economics and outcomes research: opportunities and challenges. Applied Health Economics and Health Policy [PubMed] Published 4th May 2019

Computers can make your research findings look cool, which can help make people pay attention. But data visualisation can also be used as part of the research process and provide a means of more intuitively (and accurately) communicating research findings. The data sets used by health economists are getting bigger, which provides more opportunity and need for effective visualisation. The authors of this paper suggest that data visualisation techniques could be more widely adopted in our field, but that there are challenges and potential pitfalls to consider.

Decision modelling is an obvious context in which to use data visualisation, because models tend to involve large numbers of simulations. Dynamic visualisations can provide a means by which to better understand what is going on in these simulations, particularly with respect to uncertainty in estimates associated with alternative model structures or parameters. If paired with interactive models and customised dashboards, visualisation can make complex models accessible to non-expert users. Communicating patient outcomes data is also highlighted as a potential application, aiding the characterisation of differences between groups of individuals and alternative outcome measures.

Yet, there are barriers to wider use of visualisation. There is some scepticism about bias in underlying analyses, and end users don’t want to be bamboozled by snazzy graphics. The fact that journal articles are still the primary mode of communicating research findings is a problem, as you can’t have dynamic visualisations in a PDF. There’s also a learning curve for analysts wishing to develop complex visualisations. Hopefully, opportunities will be identified for two-way learning between the health economics world and data scientists more accustomed to data visualisation.

The authors provide several examples (static in the publication, but with links to live tools), to demonstrate the types of visualisations that can be created. Generally speaking, complex visualisations are proposed as complements to our traditional presentations of results, such as cost-effectiveness acceptability curves, rather than as alternatives. The key thing is to maintain credibility by ensuring that data visualisation is used to describe data in a more accurate and meaningful way, and to avoid exaggeration of research findings. It probably won’t be long until we see a set of good practice guidelines being developed for our field.

Credits

Sandpit: What is Distinctive about Student Mental Health

Recent epidemiological studies of student mental health in the UK have been small scale with a narrow focus. Much of the data around student mental health comes from non-academic surveys, however these have produced estimates of the prevalence of student mental health difficulties ranging from 12% to 87%.

The range highlights two challenges. Firstly, the framing of a mental health survey may influence results; surveys that target specific occupations get higher rates of disorders than those that look at the same occupations within population samples, suggesting that there might be systematic over estimation of prevalence in any survey of ‘student stress’. Secondly, the perennial issue of definition cannot be overlooked, especially in a broader cultural context where a widening spectrum of psychological and emotional states is seen as problematic. Recent sociological studies of the rise of public concern about mental health suggest that, at least in part, everyday emotional distress is being relabelled as a mental health difficulty.

There is much promising activity focused around addressing the paucity of prevalence data for UK student mental health and improving our understanding of mental health difficulties in students. Through this sandpit we aim to support collaboration between research groups and universities interested in measuring and tracking student mental health and wellbeing. From a network perspective, we are keen to help groups learn from each other and avoid duplication of effort. We are encouraging anyone interested in measuring student mental health at scale across their university to join this event.

This sandpit is linked to our first Plus-funding call. If assistance is necessary to get early stage projects established or to facilitate collaboration between existing projects, Plus-funding is available to pump prime research. To be eligible for the funding call we are running, projects need to be focused on understanding what is distinctive about student mental health. Please visit www.smarten.org.uk/funding for further details.

Key topics for discussion on the day will be:

  • Preliminary feedback from the SMaRteN student-led research team project on measuring student mental health and wellbeing
  • Preliminary early feedback from SMarRteN work on student ethnographic case studies, reflecting on how students are thinking about mental health and wellbeing
  • The challenge of developing a set of core outcome measures for student mental health and well-being, and creating a common language
  • Consideration of how to measure the cost implications of poor student functioning and mental health
  • Networking opportunity
  • Development of potential research projects for the Plus-funding call.

When & Where: The event will be held in the Franklin-Wilks Building at Waterloo (this is the KCL Waterloo Campus) from 10am – 4.30pm on Wednesday the 13th of March 2019.

Who: This event is open to anyone interested in measuring and tracking student mental health and wellbeing. Space at this event will be limited, to facilitate productive conversation between attendees, we thus ask:

  • Where you are working in a research group, only one member of that group attends;
  • Attendees are at a stage where they are planning or have started work in this area.

We particularly encourage representatives from across student services to join this event, to share their perspective on what is helpful and feasible in terms of measuring student mental health.

Register: Please apply here