Welfarism versus extra-welfarism: an important analytical decision in genomics?

In my first blog post, I posed a number of questions that were relevant in health economics and genomics. One of these was “Is there a greater role for cost-benefit analysis in genomics?”. I’m working towards contributing to this debate with my PhD (if I ever finish), and one of the byproducts of my PhD is this paper, published over the weekend in PharmacoEconomics, titled: “Welfarism Versus Extra-Welfarism: Can the Choice of Economic Evaluation Approach Impact on the Adoption Decisions Recommended by Economic Evaluation Studies?”. I hope it mght be relevant to other health economists working in genomics, so I thought I would share it here. There are (hopefully!) a few findings of note, but I guess the main take-home message is this: “We found that for every five studies applying both approaches, one shows limited or no concordance in economic evaluation results: the different approaches suggest conflicting adoption decisions, and there is no pattern to which approach provides the most convincing adoption evidence”. It certainly provides food for thought when designing economic evaluations in genomics.

What are the real costs of sequencing?

I normally steer well clear of the topic of sequencing in newborn babies because this area raises so many social, legal and ethical questions that go way beyond the clinical/economics perspective that we’re used to considering. However, I read an interesting commentary piece the other day by Jacques Beckmann titled ‘Can we afford to sequence every newborn baby’s genome?’ which I think deserves a wider audience for two reasons. One, it reminded me of a comment that Professor Sir John Burn (director, NHS England) made during the recent Astellas Innovation Debate in London. Jonathan Dimbleby asked if he could see whole genome sequencing (WGS) being rolled out to everyone across the UK, to which he replied: “the reality is that even when we get the 100,000 Genomes Project fully operational and get it absorbed, we’ll only be doing maybe 30,000-50,000 whole genomes a year – we’d have to do 600,000 a year to catch up with the new babies”. Second, I think there are some points raised in this article that go beyond newborn screening and are directly applicable to the economic evaluation of genomic testing in a variety of clinical contexts.

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Genomics, the data revolution and health economics – the 2015 Astellas Innovation Debate

Last week I attended the Astellas Innovation Debate (“i-Genes: What the DNA and Data revolutions mean for our health”) at the Royal Institution of Great Britain in London. This was an interesting event and I was pleased to get the opportunity to make a couple of points during the debate itself. I also wrote about the debate and the wider implications of this revolution from a health economics perspective for the BMJ. You can read this blog here.

Interested readers can watch the entire 2015 debate at http://www.innovationdebate.com/.

Making better decisions in genomics

Happy New Year everybody. One of my new year’s resolutions is to post more frequently in 2015, and I’m going to start by taking a look at a recently published paper by Susan Snyder and colleagues titled “Economic evaluation of pharmacogenomics: a value-based approach to pragmatic decision making in the face of complexity”. This is a review paper that takes a look at the need for, and current use of economic evaluations in pharmacogenomics, identifying both obstacles to progress and also areas where, actually, we’re doing ok at the moment. There have been a few papers covering similar ground in the past couple of years (interested readers should check out Faulkner et al., Annemans et al. and, in a shameless act of self-promotion, one of my publications) and I think all of them have made a significant contribution to the literature in one way or another. Snyder et al. do so as well. Rather than review their paper in full, I wanted to focus on their unique contribution by pulling out a couple of points of interest that might prompt further discussion.

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Incidental findings in genomic sequencing: a health economics perspective

When this blog began, I mentioned that I would be happy to publish posts by other researchers: different opinions and healthy debate are both welcome here. I’m therefore very pleased to introduce a colleague of mine at the University of Oxford, Jilles Fermont, who discusses some recently published articles on incidental findings in genomic sequencing from a health economics perspective.

Incidental findings (IFs) are a topic of considerable debate, not just in genomic medicine but also in other fields of medicine. To date, few health economists have undertaken any work in this area, but a recent burst of publications suggests that this is beginning to change. This post is primarily prompted by the publication of a paper in Genetics in Medicine earlier in November titled “The cost-effectiveness of returning incidental findings from next-generation genomic sequencing”, authored by Bennette and colleagues. The authors intended to evaluate the clinical and economic impact of IFs in genomic sequencing. The cost-effectiveness analysis (CEA) is restricted and has limitations (see below) but, as the authors already indicated, it is much more of an exploratory study providing policy recommendations on how to deal with IFs from genomic sequencing. Also, it is not a CEA of next-generation sequencing (NGS) but that of the return of IFs. Despite these caveats, it remains an interesting and relevant paper. Those involved or interested in this field are recommended to read it.

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Concepts of ‘personalization’ in personalized medicine: implications for economic evaluation

In my introductory blog post, I noted that genomics might present new challenges for health economics and called for more discussion about appropriate methods in this context. I didn’t anticipate a particularly rapid response, but just a few days after posting I became aware of a new article published in PharmacoEconomics that engaged with many of the issues raised in my introductory post. Titled “Concepts of ‘personalization’ in personalised medicine: Implications for economic evaluations”, this paper reports the results of a workshop which considered where extensions to standard methods might be required in genomics and is a welcome addition to the limited existing literature on this subject. We covered some similar ground in a related paper published in Pharmacogenomics last year, and it is heartening to see that this new paper has reached some similar conclusions and developed a number of these issues further.

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Introduction

Hello. Chances are you’re a health economist, although you might also be a researcher in a different field of healthcare. You might even be a scientist (apologies in advance for any bad science that might follow). Whoever you are, you’re very welcome. The aim of this introductory post is to tell you what you should expect from this blog. Hopefully you’ll be sufficiently intrigued to return, read some more articles and contribute to the growing debate surrounding the application of standard health economic methods in the field of genomics.

This blog is going to assume that you know a little bit about genomics. Specifically, this blog is going to assume that you have at least read the “What are genomic technologies?” section of this blog, a short introduction for a layperson who already has a little bit of science knowledge. Of course, many of you will have a greater knowledge of genetics and genomics than this simple introduction, but this blog is intended to be broadly accessible to stimulate wide debate, so the aim is to keep the genomics jargon to a minimum. This blog is also going to assume that the average reader has some basic knowledge about health economics. Those who don’t could do a lot worse than frequent the excellent “Academic Health Economists’ Blog”.

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