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.
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
Genomics at the 2015 iHEA meeting in Milan
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
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
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.