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FIGURE 3-1 The change in costs and change in effectiveness compared with current practice divides the results of cost-effectiveness analyses into four quadrants.
NOTE: ΔC, change in cost; ΔE, change in effectiveness.
SOURCE: David Veenstra, IOM workshop presentation, July 17-18, 2012.

terms of base pairs sequenced per dollar, the number of clinically meaningful genetic variants identified, diagnoses received, clinical actions taken, or patient outcomes. The other important factor is the comparator. Is genome sequencing being compared to nothing, to observing the patient in the clinic, or to a targeted sequencing approach?

Flowers and Veenstra (2004) developed a framework for factors that could influence cost-effectiveness in pharmacogenomic testing (see Table 3-2). Important factors include the prevalence and penetrance of the genetic variant, the cost and accuracy of the test, the prevalence of the disease and the outcomes if left untreated, and the effectiveness and cost of treatments. A similar framework could be constructed for whole genome sequencing to examine benefits and harms, according to Veenstra. That framework would consider the prevalence of the variant of interest, the penetrance of the condition, the cost of a test, the cost and outcomes of an intervention, and the severity of the disease. A major complication is that a typical economic evaluation of a single test or single genetic variant can take a year. “We don’t have time for that,” Veenstra said. “We need to have quicker approaches that use more of a qualitative assessment.” Yet, if enough examples of this type of analysis can be completed, he added, “we can get a sense of where good value may be provided.”



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