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

Chris Sampson’s journal round-up for 12th August 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.

Developing open-source models for the US health system: practical experiences and challenges to date with the Open-Source Value Project. PharmacoEconomics [PubMed] Published 7th August 2019

PharmacoEconomics will soon publish a themed issue on transparency in decision modelling (to which I’ve contributed), and this paper – I assume – is one that will feature. At least one output from the Open-Source Value Project has featured in these round-ups before. The purpose of this paper is to describe the experiences of the initiative in developing and releasing two open-source models, one in rheumatoid arthritis and one in lung cancer.

The authors outline the background to the project and its goal to develop credible models that are more tuned-in to stakeholders’ needs. By sharing the R and C++ source code, developing interactive web applications, and providing extensive documentation, the models are intended to be wholly transparent and flexible. The model development process also involves feedback from experts and the public, followed by revision and re-release. It’s a huge undertaking. The paper sets out the key challenges associated with this process, such as enabling stakeholders with different backgrounds to understand technical models and each other. The authors explain how they have addressed such difficulties along the way. The resource implications of this process are also challenging, because the time and expertise required are much greater than for run-of-the-mill decision models. The advantages of the tools used by the project, such as R and GitHub, are explained, and the paper provides some ammunition for the open-source movement. One of the best parts of the paper is the authors’ challenge to those who question open-source modelling on the basis of intellectual property concerns. For example, they state that, “Claiming intellectually property on the implementation of a relatively common modeling approach in Excel or other programming software, such as a partitioned survival model in oncology, seems a bit pointless.” Agreed.

The response to date from the community has been broadly positive, though there has been a lack of engagement from US decision-makers. Despite this, the initiative has managed to secure adequate funding. This paper is a valuable read for anyone involved in open-source modelling or in establishing a collaborative platform for the creation and dissemination of research tools.

Incorporating affordability concerns within cost-effectiveness analysis for health technology assessment. Value in Health Published 30th July 2019

The issue of affordability is proving to be a hard nut to crack for health economists. That’s probably because we’ve spent a very long time conducting incremental cost-effectiveness analyses that pay little or no attention to the budget constraint. This paper sets out to define a framework that finally brings affordability into the fold.

The author sets up an example with a decision-maker that seeks to maximise population health with a fixed budget – read, HTA agency – and the motivating example is new medicines for hepatitis C. The core of the proposal is an alternative decision rule. Rather than simply comparing the incremental cost-effectiveness ratio (ICER) to a fixed threshold, it incorporates a threshold that is a function of the budget impact. At it’s most basic, a bigger budget impact (all else equal) means a greater opportunity cost and thus a lower threshold. The author suggests doing away with the ICER (which is almost impossible to work with) and instead using net health benefits. In this framework, whether or not net health benefit is greater than zero depends on the size of the budget impact at any given ICER. If we accept the core principle that budget impact should be incorporated into the decision rule, it raises two other issues – time and uncertainty – which are also addressed in the paper. The framework moves us beyond the current focus on net present value, which ignores the distribution of costs over time beyond simply discounting future expenditure. Instead, the opportunity cost ‘threshold’ depends on the budget impact in each time period. The description of the framework also addresses uncertainty in budget impact, which requires the estimation of opportunity costs in each iteration of a probabilistic analysis.

The paper is thorough in setting out the calculations needed to implement this framework. If you’re conducting an economic evaluation of a technology that could have a non-marginal (big) budget impact, you should tag this on to your analysis plan. Once researchers start producing these estimates, we’ll be able to understand how important these differences could be for resource allocation decision-making and determine whether the likes of NICE ought to incorporate it into their methods guide.

Did UberX reduce ambulance volume? Health Economics [PubMed] [RePEc] Published 24th June 2019

In London, you can probably – at most times of day – get an Uber quicker than you can get an ambulance. That isn’t necessarily a bad thing, as ambulances aren’t there to provide convenience. But it does raise an interesting question. Could the availability of super-fast, low-cost, low-effort taxi hailing reduce pressure on ambulance services? If so, we might anticipate the effect to be greatest where people have to actually pay for ambulances.

This study combines data on Uber market entry in the US, by state and city, with ambulance rates. Between Q1 2012 and Q4 2015, the proportion of the US population with access to Uber rose from 0% to almost 25%. The authors are also able to distinguish ‘lights and sirens’ ambulance rides from ‘no lights and sirens’ rides. A difference-in-differences model estimates the ambulance rate for a given city by quarter-year. The analysis suggests that there was a significant decline in ambulance rates in the years following Uber’s entry to the market, implying an average of 1.2 fewer ambulance trips per 1,000 population per quarter.

There are some questionable results in here, including the fact that a larger effect was found for the ‘lights and sirens’ ambulance rate, so it’s not entirely clear what’s going on. The authors describe a variety of robustness checks for our consideration. Unfortunately, the discussion of the results is lacking in detail and insight, so readers need to figure it out themselves. I’d be very interested to see a similar analysis in the UK. I suspect that I would be inclined to opt for an Uber over an ambulance in many cases. And I wouldn’t have the usual concern about Uber exploiting its drivers, as I dare say ambulance drivers aren’t treated much better.

Credits

James Lomas’s journal round-up for 21st May 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.

Decision making for healthcare resource allocation: joint v. separate decisions on interacting interventions. Medical Decision Making [PubMed] Published 23rd April 2018

While it may be uncontroversial that including all of the relevant comparators in an economic evaluation is crucial, a careful examination of this statement raises some interesting questions. Which comparators are relevant? For those that are relevant, how crucial is it that they are not excluded? The answer to the first of these questions may seem obvious, that all feasible mutually exclusive interventions should be compared, but this is in fact deceptive. Dakin and Gray highlight inconsistency between guidelines as to what constitutes interventions that are ‘mutually exclusive’ and so try to re-frame the distinction according to whether interventions are ‘incompatible’ – when it is physically impossible to implement both interventions simultaneously – and, if not, whether interventions are ‘interacting’ – where the costs and effects of the simultaneous implementation of A and B do not equal the sum of these parts. What I really like about this paper is that it has a very pragmatic focus. Inspired by policy arrangements, for example single technology appraisals, and the difficulty in capturing all interactions, Dakin and Gray provide a reader-friendly flow diagram to illustrate cases where excluding interacting interventions from a joint evaluation is likely to have a big impact, and furthermore propose a sequencing approach that avoids the major problems in evaluating separately what should be considered jointly. Essentially when we have interacting interventions at different points of the disease pathway, evaluating separately may not be problematic if we start at the end of the pathway and move backwards, similar to the method of backward induction used in sequence problems in game theory. There are additional related questions that I’d like to see these authors turn to next, such as how to include interaction effects between interventions and, in particular, how to evaluate system-wide policies that may interact with a very large number of interventions. This paper makes a great contribution to answering all of these questions by establishing a framework that clearly distinguishes concepts that had previously been subject to muddied thinking.

When cost-effective interventions are unaffordable: integrating cost-effectiveness and budget impact in priority setting for global health programs. PLoS Medicine [PubMed] Published 2nd October 2017

In my opinion, there are many things that health economists shouldn’t try to include when they conduct cost-effectiveness analysis. Affordability is not one of these. This paper is great, because Bilinski et al shine a light on the worldwide phenomenon of interventions being found to be ‘cost-effective’ but not affordable. A particular quote – that it would be financially impossible to implement all interventions that are found to be ‘very cost-effective’ in many low- and middle-income countries – is quite shocking. Bilinski et al compare and contrast cost-effectiveness analysis and budget impact analysis, and argue that there are four key reasons why something could be ‘cost-effective’ but not affordable: 1) judging cost-effectiveness with reference to an inappropriate cost-effectiveness ‘threshold’, 2) adoption of a societal perspective that includes costs not falling upon the payer’s budget, 3) failing to make explicit consideration of the distribution of costs over time and 4) the use of an inappropriate discount rate that may not accurately reflect the borrowing and investment opportunities facing the payer. They then argue that, because of this, cost-effectiveness analysis should be presented along with budget impact analysis so that the decision-maker can base a decision on both analyses. I don’t disagree with this as a pragmatic interim solution, but – by highlighting these four reasons for divergence of results with such important economic consequences – I think that there will be further reaching implications of this paper. To my mind, Bilinski et al essentially serves as a call to arms for researchers to try to come up with frameworks and estimates so that the conduct of cost-effectiveness analysis can be improved in order that paradoxical results are no longer produced, decisions are more usefully informed by cost-effectiveness analysis, and the opportunity costs of large budget impacts are properly evaluated – especially in the context of low- and middle-income countries where the foregone health from poor decisions can be so significant.

Patient cost-sharing, socioeconomic status, and children’s health care utilization. Journal of Health Economics [PubMed] Published 16th April 2018

This paper evaluates a policy using a combination of regression discontinuity design and difference-in-difference methods. Not only does it do that, but it tackles an important policy question using a detailed population-wide dataset (a set of linked datasets, more accurately). As if that weren’t enough, one of the policy reforms was actually implemented as a result of a vote where two politicians ‘accidentally pressed the wrong button’, reducing concerns that the policy may have in some way not been exogenous. Needless to say I found the method employed in this paper to be a pretty convincing identification strategy. The policy question at hand is about whether demand for GP visits for children in the Swedish county of Scania (Skåne) is affected by cost-sharing. Cost-sharing for GP visits has occurred for different age groups over different periods of time, providing the basis for regression discontinuities around the age threshold and treated and control groups over time. Nilsson and Paul find results suggesting that when health care is free of charge doctor visits by children increase by 5-10%. In this context, doctor visits happened subject to telephone triage by a nurse and so in this sense it can be argued that all of these visits would be ‘needed’. Further, Nilsson and Paul find that the sensitivity to price is concentrated in low-income households, and is greater among sickly children. The authors contextualise their results very well and, in addition to that context, I can’t deny that it also particularly resonated with me to read this approaching the 70th birthday of the NHS – a system where cost-sharing has never been implemented for GP visits by children. This paper is clearly also highly relevant to that debate that has surfaced again and again in the UK.

Credits