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.
Four years ago I blogged on how “The $1000 genome is a myth”. I think the first paragraph from that blog post is as relevant today as it was in 2015:
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.
Since 2015, the health economic evidence base for genome sequencing has gradually expanded, and several cost estimates are now available, but overall I think we still lack the sort of rigorously conducted microcosting studies that can usefully inform resource allocation decisions regarding genomic testing.
Hopefully this paper that we published in Genetics in Medicine a couple of weeks’ ago can make such a contribution.
Around 12 months ago I joined an exciting new venture: the Population Genomics Health Economists Working Group. This group is made up of health economists and policy researchers from major institutions across the globe who have been at the forefront of the incorporation of genomics into clinical care. The group is chaired by Kathryn A. Phillips, PhD, Director of the Center for Translational and Policy Research on Personalized Medicine (TRANSPERS) at the University of California. You can find out more about the group members here.
The first key output from this working group has been published today: a themed section in the September issue of Value in Health which addresses the challenges and solutions for assessment of the value of clinical genomic testing.
Yesterday, we published an article in Genomics in Medicine titled: “Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature”. The lead author for this work was Katharina Schwarze, who spent several months at HERC working on a project related to the costs of whole genome sequencing.
The aim of this particular piece of work was to summarise the current health economic evidence for whole-exome sequencing (WES) and whole-genome sequencing (WGS). The key finding was that the current health economic evidence base to support the more widespread use of WES and WGS in clinical practice is very limited. Other important findings include the following:
- Cost estimates for a single test ranged from $555 to $5,169 for WES and from $1,906 to $24,810 for WGS.
- There was no evidence that the cost of WES was falling over time, and only limited evidence that the cost of WGS was decreasing.
- Few studies used outcome measures recommended for use in economic evaluations, such as survival or quality of life.
- Only eight publications were full economic evaluations, of which only five produced evidence that WES or WGS may represent a cost-effective use of limited health-care resources.
We conclude by making four practical recommendations:
- Future studies should report costs by stage of testing for WES and WGS and highlight particularly notable costs, as it is currently difficult to identify key cost drivers.
- Future studies should report resource use and unit costs in a disaggregated manner to aid interpretation.
- Future studies evaluating the cost-effectiveness of WES or WGS should use calculated costs instead of prices, to better capture the economic value associated with WES and WGS, and to avoid incorrect and inefficient adoption decisions.
- Future studies of the cost-effectiveness of WES and WGS should include trained health economists as coinvestigators to improve study quality.
This paper challenges a number of assumptions in the literature and in the wider conversation regarding the cost and potential value of next generation sequencing technologies. I hope you’ll read, share, and debate these findings!
The latest issue of Science contains an interesting and lengthy article on how Geisinger are trying to integrate genomic screening into routine care in Pennsylvania, USA. Although this is an exciting area of research, and the business model surrounding these innovative approaches to genomic sequencing is quite interesting, I have a number of reservations about the cost-effectiveness of population-wide genomic screening.
A quick update for you on my PhD publications. Last year, I completed my PhD which considered the issues surrounding the economic analysis of genomic diagnostic technologies in the UK NHS. So far, I have published three papers reporting the results of this work:
- Paper 1 (2013): “Issues surrounding the health economic evaluation of genomic technologies”
- Paper 2 (2015): “Welfarism versus extra-welfarism: can the choice of economic evaluation approach impact on the adoption decisions recommended by economic evaluation studies?”
- Paper 3 (2016): “Patients’ Preferences for Genomic Diagnostic Testing in Chronic Lymphocytic Leukaemia: A Discrete Choice Experiment”
I am pleased to be able to report that the fourth paper arising from my PhD work was published today in PharmacoEconomics, titled: “Using genomic information to guide ibrutinib treatment decisions in chronic lymphocytic leukaemia: A cost-effectiveness analysis“.
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.
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
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:
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.