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.

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Chris Sampson’s journal round-up for 17th June 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.

Mental health: a particular challenge confronting policy makers and economists. Applied Health Economics and Health Policy [PubMed] Published 7th June 2019

This paper has a bad title. You’d never guess that its focus is on the ‘inconsistency of preferences’ expressed by users of mental health services. The idea is that people experiencing certain mental health problems (e.g. depression, conduct disorders, ADHD) may express different preferences during acute episodes. Preference inconsistency, the author explains, can result in failures in prediction (because behaviour may contradict expectations) and failures in evaluation (because… well, this is a bit less clear). Because of preference inconsistency, a standard principal-agent model cannot apply to treatment decisions. Conventional microeconomic theory cannot apply. If this leaves you wondering “so what has this got to do with economists?” then you’re not alone. The author of this article believes that our role is to identify suitable agents who can interpret patients’ inconsistent preferences and make appropriate decisions on their behalf.

But, after introducing this challenge, the framing of the issue seems to change and the discussion becomes about finding an agent who can determine a patient’s “true preferences” from “conflicting statements”. That seems to me to be a bit different from the issue of ‘inconsistent preferences’, and the phrase “true preferences” should raise an eyebrow of any sceptical economist. From here, the author describes some utility models of perfect agency and imperfect agency – the latter taking account of the agent’s opportunity cost of effort. The models include error in judging whether the patient is exhibiting ‘true preferences’ and the strength of the patient’s expression of preference. Five dimensions of preference with respect to treatment are specified: when, what, who, how, and where. Eight candidate agents are specified: family member, lay helper, worker in social psychiatry, family physician, psychiatrist/psychologist, health insurer, government, and police/judge. The knowledge level of each agent in each domain is surmised and related to the precision of estimates for the utility models described. The author argues that certain agents are better at representing a patient’s ‘true preferences’ within certain domains, and that no candidate agent will serve an optimal role in every domain. For instance, family members are likely to be well-placed to make judgements with little error, but they will probably have a higher opportunity cost than care professionals.

The overall conclusion that different agents will be effective in different contexts seems logical, and I support the view of the author that economists should dedicate themselves to better understanding the incentives and behaviours of different agents. But I’m not convinced by the route to that conclusion.

Exploring the impact of adding a respiratory dimension to the EQ-5D-5L. Medical Decision Making [PubMed] Published 16th May 2019

I’m currently working on a project to develop and test EQ-5D bolt-ons for cognition and vision, so I was keen to see the methods reported in this study. The EQ-5D-5L has been shown to have only a weak correlation with clinically-relevant changes in the context of respiratory disease, so it might be worth developing a bolt-on (or multiple bolt-ons) that describe relevant functional changes not captured by the core dimensions of the EQ-5D. In this study, the authors looked at how the inclusion of respiratory dimensions influenced utility values.

Relevant disease-specific outcome measures were reviewed. The researchers also analysed EQ-5D-3L data and disease-specific outcome measure data from three clinical studies in asthma and COPD, to see how much variance in visual analogue scores was explained by disease-specific items. The selection of potential bolt-ons was also informed by principal-component analysis to try to identify which items form constructs distinct from the EQ-5D dimensions. The conclusion of this process was that two other dimensions represented separate constructs and could be good candidates for bolt-ons: ‘limitations in physical activities due to shortness of breath’ and ‘breathing problems’. Some think-aloud interviews were conducted to ensure that the bolt-ons made sense to patients and the general public.

A valuation study using time trade-off and discrete choice experiments was conducted in the Netherlands with a representative sample of 430 people from the general public. The sample was split in two, with each half completing the EQ-5D-5L with one or the other bolt-on. The Dutch EQ-5D-5L valuation study was used as a comparator data set. The inclusion of the bolt-ons seemed to extend the scale of utility values; the best-functioning states were associated with higher utility values when the bolt-ons were added and the worst-functioning states were associated with lower values. This was more pronounced for the ‘breathing problems’ bolt-on. The size of the coefficients on the two bolt-ons (i.e. the effect on utility values) was quite different. The ‘physical activities’ bolt-on had coefficients similar in size to self-care and usual activities. The coefficients on the ‘breathing problems’ bolt-on were a bit larger, comparable in size with those of the mobility dimension.

The authors raise an interesting question in light of their findings from the development process, in which the quantitative analysis supported a ‘symptoms’ dimension and patients indicated the importance of a dimension relating to ‘physical activities’. They ask whether it is more important for an item to be relevant or for it to be quantitatively important for valuation. Conceptually, it seems to me that the apparent added value of a ‘physical activity’ bolt-on is problematic for the EQ-5D. The ‘physical activity’ bolt-on specifies “climbing stairs, going for a walk, carrying things, gardening” as the types of activities it is referring to. Surely, these should be reflected in ‘mobility’ and ‘usual activities’. If they aren’t then I think the ‘usual activities’ descriptor, in particular, is not doing its job. What we might be seeing here, more than anything, is the flaws in the development process for the original EQ-5D descriptors. Namely, that they didn’t give adequate consideration to the people who would be filling them in. Nevertheless, it looks like a ‘breathing problems’ bolt-on could be a useful part of the EuroQol armoury.

Technology and college student mental health: challenges and opportunities. Frontiers in Psychiatry [PubMed] Published 15th April 2019

Universities in the UK and elsewhere are facing growing demand for counselling services from students. That’s probably part of the reason that our Student Mental Health Research Network was funded. Some researchers have attributed this rising demand to the use of personal computing technologies – smartphones, social media, and the like. No doubt, their use is correlated with mental health problems, certainly through time and probably between individuals. But causality is uncertain, and there are plenty of ways in which – as set out in this article – these technologies might be used in a positive way.

Most obviously, smartphones can be a platform for mental health programmes, delivered via apps. This is particularly important because there are perceived and actual barriers for students to accessing face-to-face support. This is an issue for all people with mental health problems. But the opportunity to address this issue using technology is far greater for students, who are hyper-connected. Part of the problem, the authors argue, is that there has not been a focus on implementation, and so the evidence that does exist is from studies with self-selecting samples. Yet the opportunity is great here, too, because students are often co-located with service providers and already engaged with course-related software.

Challenges remain with respect to ethics, privacy, accountability, and duty of care. In the UK, we have the benefit of being able to turn to GDPR for guidance, and universities are well-equipped to assess the suitability of off-the-shelf and bespoke services in terms of their ethical implications. The authors outline some possible ways in which universities can approach implementation and the challenges therein. Adopting these approaches will be crucial if universities are to address the current gap between the supply and demand for services.

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Sam Watson’s journal round-up for 25th June 2018

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 efficiency of slacking off: evidence from the emergency department. Econometrica [RePEc] Published May 2018

Scheduling workers is a complex task, especially in large organisations such as hospitals. Not only should one consider when different shifts start throughout the day, but also how work is divided up over the course of each shift. Physicians, like anyone else, value their leisure time and want to go home at the end of a shift. Given how they value this leisure time, as the end of a shift approaches physicians may behave differently. This paper explores how doctors in an emergency department behave at ‘end of shift’, in particular looking at whether doctors ‘slack off’ by accepting fewer patients or tasks and also whether they rush to finish those tasks they have. Both cases can introduce inefficiency by either under-using their labour time or using resources too intensively to complete something. Immediately, from the plots of the raw data, it is possible to see a drop in patients ‘accepted’ both close to end of shift and close to the next shift beginning (if there is shift overlap). Most interestingly, after controlling for patient characteristics, time of day, and day of week, there is a decrease in the length of stay of patients accepted closer to the end of shift, which is ‘dose-dependent’ on time to end of shift. There are also marked increases in patient costs, orders, and inpatient admissions in the final hour of the shift. Assuming that only the number of patients assigned and not the type of patient changes over the course of a shift (a somewhat strong assumption despite the additional tests), then this would suggest that doctors are rushing care and potentially providing sub-optimal or inefficient care closer to the end of their shift. The paper goes on to explore optimal scheduling on the basis of the results, among other things, but ultimately shows an interesting, if not unexpected, pattern of physician behaviour. The results relate mainly to efficiency, but it’d be interesting to see how they relate to quality in the form of preventable errors.

Semiparametric estimation of longitudinal medical cost trajectory. Journal of the American Statistical Association Published 19th June 2018

Modern computational and statistical methods have opened up a range of statistical models to estimation hitherto inestimable. This includes complex latent variable structures, non-linear models, and non- and semi-parametric models. Recently we covered the use of splines for semi-parametric modelling in our Method of the Month series. Not that complexity is everything of course, but given this rich toolbox to more faithfully replicate the data generating process, one does wonder why the humble linear model estimated with OLS remains so common. Nevertheless, I digress. This paper addresses the problem of estimating the medical cost trajectory for a given disease from diagnosis to death. There are two key issues: (i) the trajectory is likely to be non-linear with costs probably increasing near death and possibly also be higher immediately after diagnosis (a U-shape), and (ii) we don’t observe the costs of those who die, i.e. there is right-censoring. Such a set-up is also applicable in other cases, for example looking at health outcomes in panel data with informative dropout. The authors model medical costs for each month post-diagnosis and time of censoring (death) by factorising their joint distribution into a marginal model for censoring and a conditional model for medical costs given the censoring time. The likelihood then has contributions from the observed medical costs and their times, and the times of the censored outcomes. We then just need to specify the individual models. For medical costs, they use a multivariate normal with mean function consisting of a bivariate spline of time and time of censoring. The time of censoring is modelled non-parametrically. This setup of the missing data problem is sometimes referred to as a pattern mixing model, in that the outcome is modelled as a mixture density over different populations dying at different times. The authors note another possibility for informative missing data, which was considered not to be estimable for complex non-linear structures, was the shared parameter model (to soon appear in another Method of the Month) that assumes outcomes and dropout are independent conditional on an underlying latent variable. This approach can be more flexible, especially in cases with varying treatment effects. One wonders if the mixed model representation of penalised splines wouldn’t fit nicely in a shared parameter framework and provide at least as good inferences. An idea for a future paper perhaps… Nevertheless, the authors illustrate their method by replicating the well-documented U-shaped costs from the time of diagnosis in patients with stage IV breast cancer.

Do environmental factors drive obesity? Evidence from international graduate students. Health Economics [PubMedPublished 21st June 2018

‘The environment’ can encompass any number of things including social interactions and networks, politics, green space, and pollution. Sometimes referred to as ‘neighbourhood effects’, the impact of the shared environment above and beyond the effect of individual risk factors is of great interest to researchers and policymakers alike. But there are a number of substantive issues that hinder estimation of neighbourhood effects. For example, social stratification into neighbourhoods likely means people living together are similar so it is difficult to compare like with like across neighbourhoods; trying to model neighbourhood choice will also, therefore, remove most of the variation in the data. Similarly, this lack of common support, i.e. overlap, between people from different neighbourhoods means estimated effects are not generalisable across the population. One way of getting around these problems is simply to randomise people to neighbourhoods. As odd as that sounds, that is what occurred in the Moving to Opportunity experiments and others. This paper takes a similar approach in trying to look at neighbourhood effects on the risk of obesity by looking at the effects of international students moving to different locales with different local obesity rates. The key identifying assumption is that the choice to move to different places is conditionally independent of the local obesity rate. This doesn’t seem a strong assumption – I’ve never heard a prospective student ask about the weight of our student body. Some analysis supports this claim. The raw data and some further modelling show a pretty strong and robust relationship between local obesity rates and weight gain of the international students. Given the complexity of the causes and correlates of obesity (see the crazy diagram in this post) it is hard to discern why certain environments contribute to obesity. The paper presents some weak evidence of differences in unhealthy behaviours between high and low obesity places – but this doesn’t quite get at the environmental link, such as whether these behaviours are shared through social networks or perhaps the structure and layout of the urban area, for example. Nevertheless, here is some strong evidence that living in an area where there are obese people means you’re more likely to become obese yourself.

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