title: Cost-effectiveness of testing for breast cancer susceptibility genes.
abstract: OBJECTIVE
  Genetic mutations in breast cancer susceptibility genes BRCA1/2 are associated with an increased risk of breast/ovarian cancers. Cost-effective preventive measures are available for women who test positive. The objective of this study was to determine at what risk of mutation it is cost-effective to test women for BRCA1/2 mutations.
  
  
  METHODS
  A semi-Markov model accrued costs and quality-adjusted life years (QALYs) annually from the societal perspective. The estimates of health-care costs, life expectancy, likelihood of obtaining a mastectomy or oophorectomy, and patient preferences for treatment and certainty about their BRCA1/2 status were based on the literature.
  
  
  RESULTS
  At a 10% probability of mutation (the current guideline), the test strategy generated 22.9 QALYs over the lifetime and cost $118k, while the no-test strategy generated 22.7 QALYs and cost $117k. The incremental cost-effectiveness ratio of the test strategy was $9k and the differences between costs and effects were not substantial. The test strategy remained cost-effective to a probability of mutation of 0%, as long as utility gained from a negative test result was 0.006 or greater. These results were sensitive to the frequency of inconclusive test results and utility gain from a negative test result.
  
  
  CONCLUSIONS
  The costs and effectiveness of both the test and no-test strategies are very similar even when there is a small probability of mutation. Current guidelines, which can be used by insurance companies to refuse coverage, could deny some women a cost-effective approach. Further research to decrease the frequency of inconclusive results could improve the cost-effectiveness of this test.
authors.affiliation: Department of Community and Preventive Medicine, University of Rochester, Rochester, NY 14620, USA. margaret_holland@urmc.rochester.edu
authors.forename: Margaret L
authors.initials: ML
authors.lastname: Holland
authors.forename: Alissa
authors.initials: A
authors.lastname: Huston
authors.forename: Katia
authors.initials: K
authors.lastname: Noyes
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000191:economics / Q000453:epidemiology / Q000235:genetics / Q000517:prevention & control*; D003362:Cost-Benefit Analysis; D005260:Female; D019398:Genes, BRCA1; D024522:Genes, BRCA2; D020022:Genetic Predisposition to Disease; D005820:Genetic Testing / Q000191:economics* / Q000706:statistics & numerical data; D017048:Health Care Costs; D006801:Humans; D015994:Incidence; D008017:Life Expectancy; D008390:Markov Chains; D008408:Mastectomy / Q000191:economics; D008875:Middle Aged; D018803:Models, Economic; D009010:Monte Carlo Method; D009154:Mutation; D010051:Ovarian Neoplasms / Q000191:economics / Q000453:epidemiology / Q000235:genetics / Q000517:prevention & control*; D010052:Ovariectomy / Q000191:economics; D017060:Patient Satisfaction; D019057:Quality-Adjusted Life Years; D012307:Risk Factors; D014481:United States / Q000453:epidemiology