One of the new publications that appeared in my searches this week was this paper on “Next-Generation Sequencing vs Culture-Based Methods for Diagnosing Periprosthetic Joint Infection After Total Knee Arthroplasty: A Cost-Effectiveness Analysis”, published in The Journal of Arthroplasty. I wrote about it on this Twitter thread, but I just wanted to expand on a couple of points here.
Whole genome sequencing costs – a step in the right direction
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.
Health economics, genomics and the value of knowing
In July 2016, the Office for Health Economics and the European Personalised Medicine Association published a white paper titled: “The Value of Knowing and Knowing the Value: Improving the Health Technology Assessment of Complementary Diagnostics”. This publication did not receive a great deal of attention at the time, but it raises some interesting points related to genomic testing that are worthy of consideration by a wider audience. In particular, it highlights several things that we currently do reasonably well in health economics and genomics, as well as some areas in which we need to improve evidence generation, suggesting a future research agenda in this field.
What are people willing to pay for whole genome sequencing information?
Given the wide variety of health and non-health outcomes associated with genomic tests, it is perhaps particularly important that the preferences of key stakeholders are considered within the health technology assessment process for these interventions. Indeed, in a paper published last year, Rogowski et al. highlight the importance of ‘preference-based personalization’ in this context. To date, few studies have generated data on preferences for genomic tests. However, a recent publication in Genetics in Medicine by Deborah Marshall and colleagues has attempted to address this gap in the literature.
Priority setting and genomic testing
I’m currently working on a project which is identifying the key barriers which are slowing down the translation of whole genome sequencing into clinical practice, and as a result I’ve been digging into the literature on priority setting and genomics (on the basis that one barrier might be resource constraints). To be honest, this hasn’t taken a lot of time, as it’s not a particularly well-researched area. That said, there were two specific papers that have informed the development of our work in this area, and I thought it might be interesting to bring these to the attention of a wider audience. Continue reading
The cost-effectiveness of next generation sequencing in colorectal cancer
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.
The $1000 genome is a myth
Barely a day goes by without a news story or social media post proclaiming that the $1000 genome now exists, and is ushering in a healthcare revolution. Every day, somebody, somewhere in the world, posts these graphs on Twitter. There’s even a Wikipedia page devoted to this topic. It’s a persistent news headline and, frustratingly, it’s currently wrong. Continue reading
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.
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.
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.