Personalized medicine is a hot topic in the health care industry. Many in the medical community believe that this is the future direction of medicine. Who doesn’t like having products and services tailored to individuals, especially health services that are fundamental to people’s lives? Indeed, there are no two identical people in the world, just as there are no two identical leaves on a tree. Therefore, since different people can respond to the same medical treatment differently, it makes perfect sense to tailor the treatment based on their individual characteristics.
What individual characteristics should we take into consideration in order to personalize health care? In the old days, physicians observed their patients and asked them about their medical history and family history before prescribing the appropriate treatment. Then diagnostic testing was invented for bacterial infection. Nowadays, a promising new direction is seen in genomic testing, which seems to be able to explain some individual differences that were totally mysterious before.
Genomic testing is a rapidly growing field, especially in cancer medicine, where it can be used to match patients to the most effective targeted treatment. In the past decade, we have seen rapid growth in the number of new genomic tests that are available. However, genomic testing is usually very expensive, raising the question of whether the testing is worth the cost.
Cost-effectiveness studies aim to address this question. They compare the relative costs and effects of two (or more) different treatments. Quality-adjusted life-years (QALYs) is the most commonly used measure of treatment effects, which takes into account not only survival (life-years) but also the quality of life. The most commonly used cost-effectiveness measure is an incremental cost-effectiveness ratio (ICER), which is the ratio of increase/decrease in costs to the increase/decrease in effects.
One recent study1 compared the cost-effectiveness of population-based BRCA testing with the standard family history-based approach in Ashkenazi Jewish women. BRCA mutations greatly increase risk of breast cancer and ovarian cancer. The researchers developed a decision-analytic model to compare the lifetime costs and gain in QALYs among Ashkenazi Jewish women in the U.K. aged 30 years and older who have a family history indicating a mutation risk of 10 percent or above. This study showed that for the specific population, population-based screening lowered ovarian and breast cancer incidence by 0.34 percent and 0.62 percent, respectively. It saved 0.090 more life-years and 0.101 more QALYs, resulting in 33 days’ gain in life expectancy. Further, it is cost-saving, with baseline-discounted ICER of -£2079/QALY. Therefore, the study concludes that a population-based screening test for BRCA is highly cost-effective compared with family-based testing among the group studied. It is very important to note that these conclusions are limited to the specific population group studied and do not apply to other population groups. Further, cost-effectiveness analysis depends on the benchmark, so it is very important to be clear about which alternative treatment we are comparing with the new treatment.
Panel testing is a newly emerging type of genomic test. Instead of testing for one or two specific mutations, panel genomic tests check hundreds of mutations and copy number alterations. For example, the FoundationOne test offered by Foundation Medicine2 interrogates the entire coding sequence of 315 cancer-related genes plus select introns, and the FoundationOne Heme tests even more genes.
The literature on the cost-effectiveness of panel genomic tests is scarce, partly because the cost of these tests is rather opaque. To the best of our knowledge, currently only FoundationOne gives a clear list price of $5,800 for its FoundationOne test and a list price of $7,200 for the FoundationOne Heme test. Most other providers do not state a dollar amount for their panel tests. Even when there is a list price, the estimation of cost is not totally straightforward. For example, these tests are often not covered by insurance. Even when they are covered, providers can have different agreements on the amount of reimbursement with different insurance companies. If patients do not have insurance coverage, the providers usually negotiate with patients on the out-of-pocket cost. In addition, although panel testing can test for a large number of mutations, the effectiveness largely depends on whether the detected mutations are actionable. When a targeted therapy is available for a mutation, that mutation is called “actionable.” Therefore, if a mutation is not actionable, the test for it is rendered useless. Further, prevalence also matters when we consider the economics. If the chance of detecting something actionable is very low, then it probably does not make sense to test everyone — it would be much more sensible to test for the mutation among a subgroup that has a high prevalence.
With more data on the prevalence of mutations becoming available and the price of genomic testing dropping, this is a very promising field that calls for economics research. We believe genomic tests will become more affordable and better targeted to vulnerable populations in the future.
Chan Shen, Ph.D., is a health policy scholar at the Baker Institute and an assistant professor in the Department of Health Services Research and the Department of Biostatistics at The University of Texas MD Anderson Cancer Center.
References:
- Manchanda, R., R. Legood, M. Burnell, et al. “Cost-effectiveness of population screening for BRCA mutations in Ashkenazi Jewish women compared with family history-based testing.” Journal of the National Cancer Institute 107 (1): 2014.
- FoundationOne. Accessed March 12, 2015. http://www.foundationone.com/.