It is now well documented that health economic evidence to inform commissioning decisions regarding genomic tests is in short supply. This lack of evidence relates to both costs and health outcomes – there is perhaps an understandable tendency to focus on the issues surrounding the measurement of health outcomes in genomics, but data on costs is equally sparse and the generation of such data is also beset by practical and methodological challenges. That said, in the past twelve months we have started to finally see some good quality data emerging on the costs of whole genome and whole exome sequencing, and a recent paper by Kate Tsiplova and colleagues has made a notable contribution to this literature.
So, apologies again for the radio silence. Good news though: the PhD has finally been submitted! That’s not quite the end of that chapter in my life though, as I still have a viva to complete and six more publications to prepare to add to the two that have been published in the last 18 months or so. Hopefully I’ll be able to share more of my PhD outputs from the start of 2016 onwards, depending (of course) on the vagaries of the peer-review process.
Anyway, I now have time to read and then write about all of the publications that I’ve been putting to one side over the last few months. I’m going to start with a paper by Carlos Gallego et al. which was published in JCO in May, and which considered the cost-effectiveness of next generation sequencing (NGS) panels for the diagnosis of colorectal cancer and polyposis (CRCP) syndromes.
Apologies for the recent lack of blog posts. It turns out it takes a lot of effort to get a PhD written up alongside other research commitments. Normal service will be resumed very soon. For now, a few quick notes on the International Health Economics Association meeting in Milan which has just concluded. Specifically, this is a quick review of the presentations that I attended which had a link (however tenuous!) to genomics. Continue reading
Terry Flynn recently blogged on how treatment tailored to genes will kill economic evaluation. It’s a catchy title that I hope will draw health economists working outside of genetics into a growing debate on the best way to do economic evaluation in genetics and genomics. However, I don’t entirely agree with everything that Terry said and wanted to respond on a few points:
Yesterday in Oxford we hosted a conference titled “Personalised Medicine and Resource Allocation”. The conference aimed to explore the challenges of implementing genomic medicine into widespread clinical practice, and there was a particular focus on the generation of economic evidence and the ethical issues that arise in the resource allocation decisions required to allow personalised medicine to be realised.
I was pleased to be asked to speak at the event, and I presented alongside Jilles Fermont on “Methodological issues surrounding the health economic evaluation of genomic technologies and a case study of these issues in the research setting”. It was an interesting day overall, and I suspect that others will blog more extensively on the various topics that were discussed. For now, I’ll leave a link to our slides, in case anybody is interested in this topic. For more information on the day itself, please visit the conference website or follow the proceedings on Twitter via the hashtag #PMRAoxford.
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.
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.
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/.
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.
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.