The economic value of personalized medicine tests: what we know and what we need to know

PURPOSE: There is uncertainty about when personalized medicine tests provide economic value. We assessed evidence on the economic value of personalized medicine tests and gaps in the evidence base. METHODS: We created a unique evidence base by linking data on published cost–utility analyses from the Tufts Cost-Effectiveness Analysis Registry with data measuring test characteristics and reflecting where value analyses may be most needed: (i) tests currently available or in advanced development, (ii) tests for drugs with Food and Drug Administration labels with genetic information, (iii) tests with demonstrated or likely clinical utility, (iv) tests for conditions with high mortality, and (v) tests for conditions with high expenditures. RESULTS: We identified 59 cost–utility analyses studies that examined personalized medicine tests (1998–2011). A majority (72%) of the cost/quality-adjusted life year ratios indicate that testing provides better health although at higher cost, with almost half of the ratios falling below $50,000 per quality-adjusted life year gained. One-fifth of the results indicate that tests may save money. CONCLUSION: Many personalized medicine tests have been found to be relatively cost-effective, although fewer have been found to be cost saving, and many available or emerging medicine tests have not been evaluated. More evidence on value will be needed to inform decision making and assessment of genomic priorities

Payer decision making for next-generation sequencing-based genetic tests: insights from cell-free DNA prenatal screening.

PurposeCell-free DNA (cfDNA) prenatal screening tests have been rapidly adopted into clinical practice, due in part to positive insurance coverage. We evaluated the framework payers used in making coverage decisions to describe a process that should be informative for other sequencing tests.MethodsWe analyzed coverage policies from the 19 largest US private payers with publicly available policies through February 2016, building from the University of California San Francisco TRANSPERS Payer Coverage Policy Registry.ResultsAll payers studied cover cfDNA screening for detection of trisomies 21, 18, and 13 in high-risk, singleton pregnancies, based on robust clinical validity (CV) studies and modeled evidence of clinical utility (CU). Payers typically evaluated the evidence for each chromosomal abnormality separately, although results are offered as part of a panel. Starting in August 2015, 8 of the 19 payers also began covering cfDNA screening in average-risk pregnancies, citing recent CV studies and updated professional guidelines. Most payers attempted, but were unable, to independently assess analytic validity (AV).ConclusionPayers utilized the standard evidentiary framework (AV/CV/CU) when evaluating cfDNA screening but varied in their interpretation of the sufficiency of the evidence. Professional guidelines, large CV studies, and decision analytic models regarding health outcomes appeared highly influential in coverage decisions.Genet Med advance online publication 22 September 2016