Medicine’s future? The health economics of population-wide genomic screening

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.

Encouragingly, the article author engaged with a number of individuals with expertise in health economics and genomics whilst writing this piece, including David Veenstra at the University of Washington, Glenn Palomaki at Brown University, and me. Unfortunately my contribution was considerably shortened in the final version of the article. I’ve attached my contribution as it appeared in an earlier draft of the article below, and would be very keen to hear the thoughts of others on this topic.

There is still not enough data to determine whether sequencing the genomes or exomes of a low risk population to identify disease-linked variations, as Geisinger is doing, is worth the expense, says health economist James Buchanan of the University of Oxford.

“To determine whether population-wide sequencing is cost-effective, we need to consider two things: how much sequencing increases costs in the healthcare system and whether population health improves sufficiently to offset these cost increases”, says Buchanan. Health economists commonly measure changes in population health using “quality-adjusted life-years” (QALYs), economist lingo for a year of perfect health. The current consensus in the US is that healthcare interventions that add a QALY to a person’s lifespan for less than $100,000 are cost-effective. However, Buchanan identifies three key reasons why it is difficult to say that population-wide sequencing falls under this cost-effectiveness threshold.

“First, the cost-effectiveness of sequencing will vary considerably depending on which disease-linked variations scientists are trying to identify. The cost-effectiveness of sequencing a population to identify individuals with a higher risk of developing cardiovascular disease is very different to the cost-effectiveness of identifying variations linked to cancer or rare diseases. The lack of consensus regarding which disease-linked variations to prioritise makes it difficult to determine whether sequencing is cost-effective overall”.

“Second, we do not know with any certainty what population-wide sequencing costs. There is little actual data on the cost of sequencing a genome or exome in the literature, but the consensus is that this is now in the low thousands of dollars per test. However, the cost of sequencing a genome or exome is a small part of the overall cost of sequencing. The costs associated with analysing the sequencing data – bioinformatics – remain high, and the costs associated with medical follow-up (preventative screening, surgery, high cost drugs tailored to the specific genetic mutation, and in some cases, a lifetime of behavioral and lifestyle changes) can far outweigh the costs of conducting the initial test.”

“Finally, we know very little about the health consequences of population-wide sequencing. We still need to do a lot of work before we can be confident that identifying disease-linked variations has a significant impact on the life expectancy and quality of life of a low-risk population. The collection of long-term data linking sequencing results with patient health records will help to address this issue”.

3 thoughts on “Medicine’s future? The health economics of population-wide genomic screening

  1. British health economist James Buchanan D.Phil. is skeptical that Geisinger Health System’s (Pa./USA) genomics project will prove to be cost-effective. But what about the cost-effectiveness of the whole U.S. health system?

    As a physician and resident of the southern reaches of Geisinger Health System’s catchment area in central Pennsylvania, I was intrigued by Dr. Buchanan’s blog on Geisinger’s population-based MyCode genomics project. This project was featured in October 27, 2017, issue of Science.

    Dr. Buchanan’s skepticism is undoubtedly warranted about the cost-effectiveness of costly screening of thousands of patients, unselected for any traits or conditions, across generally low-risk populations.

    However, I would comment that Geisinger’s intent – reflected in its motto, “Heal, Teach, Discover” – is not in the first instance cost-effective screening. Rather, Geisinger is motivated by scientific discovery and community service. In the process, its MyCode project has had the side benefit of enhancing Geisinger’s academic and clinical reputation to attract clinicians, resident trainees, academics and ultimately patients to this otherwise rural, non-cosmopolitan region. Geisinger has sustained its support of this project since 2006 based on its vision and ideals, free from any profit motive or mere search for efficiency.

    I would further comment that the profit-motive-driven quest for efficiency will not always converge with genomic research applications. A case in point was contemplated at a recent NEJM Catalyst webinar [ ] by Harvard Business School’s Professor of Social Policy Amitabh Chandra’s thought experiment about the “value” (to society) of life-saving curative treatments when their economic price is exorbitant. This scenario will likely emerge from new gene-editing technologies currently under development. Professor Chandra’s solution is to create a public research entity that would bear the cost of development but also own the intellectual property rights. Thereby life-saving gene-based treatments would be provided without regard to shareholder “return on investment.”

    I have also been interested in use of cost-effectiveness analysis to guide public health policy. Cost-effectiveness analysis is being done in the United Kingdom by the National Institute of Clinical Excellence (NICE) to guide budgeting for new technologies. Similar research is being done in the U.S. by ICER [ ]. Its application to pharmacoeconomics [ ] has been promoted by a for-profit data analytics company based in Massachusetts, BHE Analytics [ ], and was the subject of a pharmacoeconomics conference in Glasgow in November 2017 [ ].

    My particular interest has been in the Oregon Health Plan [], which in 1994 innovated the use of cost-benefit analysis to prioritize services provided by its Medicaid program. I would be interested in Dr. Buchanan’s perspective on how cost-benefit analysis could be applied at a national level to help the U.S. rein in its health spending, which has now grown to 18% of GDP and shows no sign of stopping.

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