|Title||A framework to evaluate the economic impact of pharmacogenomics.|
|Publication Type||Journal Article|
|Year of Publication||2006|
|Authors||Stallings, SC, Huse, D, Finkelstein, SN, Crown, WH, Witt, WP, Maguire, J, Hiller, AJ, Sinskey, AJ, Ginsburg, GS|
|Date Published||2006 Sep|
|Keywords||Anti-Asthmatic Agents, Asthma, Cost Savings, Databases, Factual, Humans, Models, Economic, Pharmacogenetics, Retrospective Studies|
INTRODUCTION: Pharmacogenomics and personalized medicine promise to improve healthcare by increasing drug efficacy and minimizing side effects. There may also be substantial savings realized by eliminating costs associated with failed treatment. This paper describes a framework using health claims data for analyzing the potential value of pharmacogenomic testing in clinical practice.METHODS: We evaluated a model of alternate clinical strategies using asthma patients' data from a retrospective health claims database to determine a potential cost offset. We estimated the likely cost impact of using a hypothetical pharmacogenomic test to determine a preferred initial therapy. We compared the annualized per patient costs distributions under two clinical strategies: testing all patients for a nonresponse genotype prior to treating and testing none.RESULTS: In the Test All strategy, more patients fall into lower cost ranges of the distribution. In our base case (15% phenotype prevalence, 200 US dollars test, 74% overall first-line treatment efficacy and 60% second-line therapy efficacy) the cost savings per patient for a typical run of the testing strategy simulation ranged from 200 US dollars to 767 US dollars (5th and 95th percentile). Genetic variant prevalence, test cost and the cost of choosing the wrong treatment are key parameters in the economic viability of pharmacogenomics in clinical practice.CONCLUSIONS: A general tool for predicting the impact of pharmacogenomic-based diagnostic tests on healthcare costs in asthma patients suggests that upfront testing costs are likely offset by avoided nonresponse costs. We suggest that similar analyses for decision making could be undertaken using claims data in which a population can be stratified by response to a drug.