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.

Firstly, one of the aspects of the paper that I particularly appreciated was that the authors provided several examples of economic analyses in genomics to back up the points that they were making. I often find that discussions about appropriate methods in health economics and genomics focus primarily on the challenges that we face when conducting economic evaluations in this context; we rarely take the time to acknowledge that there are plenty of instances where current methods are, for the most part, pretty good. I think this is a point worth making more frequently, and this is one of the useful contributions made by this paper.

Secondly, in genomics, possibly more so than most other clinical contexts, timing is crucial. The basic science evolves so quickly that it can often be difficult to generate the clinical and economic evidence that is required to inform decision-making and ensure the appropriate allocation of healthcare resources. There is rarely a point at which you can say with confidence: ‘this is the final specification of the genomic test that we are evaluating – now, we shall undertake our economic evaluation’. Test filters change, new SNPs are discovered, thresholds for treatment stratification are altered, polygenic risk scores are recalculated etc. etc.

Snyder et al. highlight this challenge by focusing on the information needs of decision makers, noting that input from health economists is required at multiple stages of the translation process, including evaluations of the potential impact of hypothetical new technologies, pre-implementation economic evaluations, and also post-implementation assessments. Again, I think this is a point worth making more frequently. We like to provide definitive answers as health economists, but I think we need to acknowledge that this is going to be very difficult in genomics. We need to work in a much more iterative manner, for example, adapting models as new evidence comes to light. I think the health economic publishing model in genomics also needs to change: rather than focusing on a final definitive economic evaluation paper, publication strategies should be more geared towards multiple publications at different stages of the translational cycle. As Snyder et al. note:

“It is important to recognise that clinical decision making in the presence of insufficient evidence for genotype-guided approaches that may improve outcomes will occur despite controversy about the level of evidence required to justify routine use”

My final point relates to the last section of the paper, where the focus turns to developing countries. To the best of my knowledge, very little has been written about the health economics of introducing genomic testing in developing countries, so it was heartening to see it discussed here. The authors highlight the need to localise the generation of health economic evidence, on the basis that frequencies of genetic biomarkers vary considerably from one country to the next. Several neat examples are provided, including one which describes the cost-effectiveness of pharmacogenomics-guided warfarin treatment in Croatia. The authors primarily focus on differences in the underlying genetics of different countries, but it is also worth noting that in the absence of nationally (or internationally) agreed test prices, costs are also likely to vary significantly between countries. This is another reason to replicate health economic analyses in different settings. One caveat worth noting is that the ‘developing’ countries considered include Croatia, Serbia and Malta – the UN may classify these countries slightly differently! However, the general point being made is solid, and is transferable to other, less developed, country settings.

Ok, I’ll end there. Snyder et al. make some interesting points about how we make decisions in genomics, both now and going forward. Their paper is worth a read: I’d encourage you to take a look.

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