from The Academic Health Economists’ Blo… at https://bit.ly/2yhtVyt on March 30, 2020 at 12:05PM
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.
Willingness to pay for an early warning system for infectious diseases. The European Journal of Health Economics [PubMed] Published 16th March 2020
COVID-19 has caught much of the world with its pants down. It seems clear that more should have been done to prevent its spread. Epidemics start somewhere and don’t usually respect national borders. As such, there is value in collaborative research and information sharing between countries to speed up the detection of disease outbreaks and formulate rapid responses. This study was conducted Before Covid. The authors sought to find out how much – if anything – people of Europe are willing to pay for an early warning system for infectious diseases.
The study was conducted in Denmark, Germany, Hungary, Italy, the Netherlands, and the UK. The survey was tested with experts and underwent multiple stages of piloting with large samples of the UK public, with the full survey including 3,140 responses. The mechanism of payment was through an increase in taxes. Any potential health benefits of the early warning system were not described. Rather, the authors assert that they were trying to identify the value of the system in terms of the health safety that it would be perceived to provide.
A two-step approach was adopted to elicit willingness to pay. First, participants were asked for amounts (per month) that they would definitely pay and definitely would not pay. The possible payments ranged from 0 to 200 (€/£), with the option to select ‘more’. Second, participants had to select a specific amount from the interval identified in the first step. For the full sample, 14.8% of individuals said that they were not willing to pay anything. These people were asked to provide reasons, with more than half giving what might be considered protest answers, such as stating that it is the government’s responsibility.
The mean willingness to pay was €25.17 and the median was €10.07. Results were skewed by around 5% of people being willing to pay more than €100/month. People in Hungary had a low willingness to pay and lots of people unwilling to pay anything. Italy and Denmark were willing to pay the most, with 8.8% of Italians willing to pay more the €100/month. The survey included a reference point for the amount that people paid for home contents insurance, and the majority of responses were approximate to this. That could be seen as either an indicator of validity or that one was acting as an anchor for the other. Regression analyses were conducted to understand the individual characteristics that were associated with different willingness to pay values. As you might expect, income was a strong predictor. Better health was also associated with a lower willingness to pay. Adding it all together and extrapolating across the six countries, the authors estimate that there could be a willingness to pay of around €6.5 billion for an early warning system.
I don’t know much about the health safety literature, but surely health safety is simply health weighted by risk. If so, the analysis may still be eliciting people’s willingness to pay for health (either their own or that of their compatriots), weighted by the perceived risk. In that case, I’m not sure what this study tells us. The authors allude to a subsequent part of the survey – not discussed in this paper – that elicited willingness to pay associated with different levels of risk reduction and disease severity. It is surely that part of the survey that is most informative. There is one way to make this part of the survey very valuable, and that would be to dust off the protocols and re-run the survey right now. One of the findings was that past exposure to infectious disease does not affect willingness to pay. I wonder whether that holds true in a post-COVID world.
Mutually exclusive interventions in the cost-effectiveness bookshelf. Medical Decision Making [PubMed] Published 14th March 2020
The cost-effectiveness bookshelf is an excellent visualisation of what happens, in theory, when new technologies are adopted and old ones are displaced. But, like many teaching aids, it provides a simplified view that is almost entirely divorced from reality. In reality, we cannot line-up all current and possible health technologies according to their cost-effectiveness, because we don’t know the cost-effectiveness of these interventions. Another limitation is that decisions about technologies are not made independently of one another. For instance, within a single patient population, two interventions may be mutually exclusive. In this article, the authors extend the bookshelf analogy to this situation.
What it comes down to, the authors show, is the use of average cost-effectiveness or incremental cost-effectiveness as the basis for the height of the ‘books’. Where interventions are mutually exclusive, you need to use incremental cost-effectiveness for the ordering of the books to make any sense. In that case, the different books needn’t be distinct interventions. One book could be an ‘add-on’ to another book. The authors also suggest that this provides a more meaningful characterisation of the cost-effectiveness threshold, which might not be represented by any single intervention but by a collection of partial volumes.
I found this paper hard to follow, which I think demonstrates that it is stretching the bookshelf analogy too far. The point of it is not to represent reality, but to show an idea. By extending it in this way, the core idea seems to be lost.
Addressing challenges of economic evaluation in precision medicine using dynamic simulation modeling. Value in Health [PubMed] Published 26th March 2020
I’m always wary of a paper that claims to tackle challenges that are ‘unique’ to a particular context. Unique challenges are rare and it’s usually best to be honest and accept that you’re giving the challenges a narrow consideration. Nevertheless, modelling is tricky in the context of precision medicines, so some practical guidance could be useful.
The authors set out the challenges of economic evaluation in precision medicine. In particular, they assert the need for a ‘system view’ of health care. That’s because precision medicine necessarily includes multiple decision points within a complex (or, at least, extensive) set of possible pathways. For instance, it may be necessary to consider all possible screening, diagnostic, treatment, and follow-up pathways in order to capture the consequences of a precision medicine intervention. The authors reproduce a checklist from a previous study that sets out the challenges in modelling precision medicines and go on to explain why ‘traditional’ (read: Markov) modelling methods aren’t appropriate with respect to each item. The solution, we are told, is to use ‘simulation modelling’, by which the authors mean discrete event simulations or agent-based models. The authors provide a couple of case studies that demonstrate the value of these methods.
There isn’t a great deal of novelty in this paper. Nevertheless, it provides a useful reference for people conducting economic evaluations in precision medicine, especially in the context of genomics. In particular, it provides a useful description of the comparative merits of discrete event simulation and agent-based modelling in this context. I would have liked to see more on the barriers to the adoption of these methods and how we might actually address these. The challenges are by no means unique, so these lessons could be drawn from other contexts.