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Modelling Adverse Selection in The Presence of a Common Genetic Disorder: the Breast Cancer Polygene

Published online by Cambridge University Press:  09 August 2013

Angus S. Macdonald  and
Kenneth R. McIvor
Angus S. Macdonald
Affiliation:
Department of Actuarial Mathematics and Statistics, and the Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh EH14 4AS, U.K.Tel: +44(0)131-451-3209, Fax: +44(0)131-451-3249, E-mail: A.S.Macdonald@ma.hw.ac.uk
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Abstract

The cost of adverse selection in the life and critical illness (CI) insurance markets, brought about by restrictions on insurers' use of genetic test information, has been studied for a variety of rare single-gene disorders. Only now do we have a study of a common disorder (breast cancer) that accounts for the risk associated with multiple genes. Such a collection of genes is called a polygene. We take two approaches to modelling the severity of adverse selection which may result from insurers being unable to take account of tests for polygenes as well as major genes. First, we look at several genetic testing scenarios, with a corresponding range of possible insurance-buying behaviours, in a market model for CI insurance. Because a relatively large proportion of the population is exposed to adverse polygenic risk, the costs of adverse selection are potentially much greater than have been associated with rare single genes. Second, we use utility models to map out when adverse selection will appear, and which risk groups will cause it. Levels of risk aversion consistent with some empirical studies do not lead to significant adverse selection in our model, but lower levels of risk aversion could effectively eliminate the market.

Keywords

Adverse SelectionBRCA1BRCA2Breast CancerCritical Illness InsurancePolygene
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 39 , Issue 2 , November 2009 , pp. 373 - 402
Copyright
Copyright © International Actuarial Association 2009

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References

Antoniou, A.C., Pharoah, P.D.P., McMullan, G., Day, N.E., Stratton, M.R., Peto, J., Ponder, B.J. and Easton, D.F. (2002) A comprehensive model for familial breast cancer incorporating BRCA1, BRCA2 and other genes. British Journal of Cancer, 86, 76–83.CrossRefGoogle ScholarPubMed
Eisenhauer, J.G. and Ventura, L. (2003) Survey measures of risk aversion and prudence. Applied Economics, 35(13), 1477–1484.CrossRefGoogle Scholar
Gui, E.H., Lu, B.,Macdonald, A.S., Waters, H.R. and Wekwete, C.T. (2006) The genetics of breast and ovarian cancer III: A new model of family history with insurance applications. Scandinavian Actuarial Journal, 2006, 338–367.Google Scholar
Gutierrez, M.C. and Macdonald, A.S. (2007) Adult polycystic kidney disease and insurance: A case study in genetic heterogeneity. North American Actuarial Journal, 11(1), 90–118.CrossRefGoogle Scholar
Hoy, M. and Witt, J. (2007) Welfare effects of banning genetic information in the life insurance market: The case of BRCA1/2 genes. Journal of Risk and Insurance, 74(3), 523–546.CrossRefGoogle Scholar
Lange, K. (1997) An approximate model of polygenic inheritance. Genetics, 147, 1423–1430.CrossRefGoogle ScholarPubMed
Lemaire, J.,Subramanian, K., Armstrong, K. and Asch, D.A. (2000) Pricing term insurance in the presence of a family history of breast cancer. North American Actuarial Journal, 4, 75–87.CrossRefGoogle Scholar
Macdonald, A.S. (1999) Modeling the impact of genetics on insurance. North American Actuarial Journal, 3(1), 83–101.CrossRefGoogle Scholar
Macdonald, A.S. and McIvor, K.R. (2006) Application of a polygenic model of breast and ovarian cancer to critical illness insurance. Annals of Actuarial Science, 1, 319–343.CrossRefGoogle Scholar
Macdonald, A.S., Pritchard, D.J. and Tapadar, P. (2006) The impact of multifactorial genetic disorders on critical illness insurance: A simulation study based on UK Biobank. ASTIN Bulletin, 36, 311–346.CrossRefGoogle Scholar
Macdonald, A.S. and Tapadar, P. (2009) Multifactorial genetic disorders and adverse selection: Epidemiology meets economics. To appear in Journal of Risk and Insurance.Google Scholar
Macdonald, A.S., Waters, H.R. and Wekwete, C.T. (2003a) The genetics of breast and ovarian cancer I: A model of family history. Scandinavian Actuarial Journal, 2003, 1–27.CrossRefGoogle Scholar
Macdonald, A.S., Waters, H.R. and Wekwete, C.T. (2003b) The genetics of breast and ovarian cancer II: A model of critical illness insurance. Scandinavian Actuarial Journal, 2003, 28–50.CrossRefGoogle Scholar
Ropka, M.E., Wenzel, J., Phillips, E.K., Siadaty, M. and Philbrick, J.T. (2006) Uptake rates for breast cancer genetic testing: A systematic review. Cancer Epidemiology Biomarkers and Prevention, 15(5), 840–855.CrossRefGoogle ScholarPubMed
Strachan, T. and Read, A.P. (2004) Human Molecular Genetics 3. Garland Publishing.Google Scholar
Subramanian, K., Lemaire, J., Hershey, J.C., Pauly, M.V., Armstrong, K. and Asch, D.A. (1999) Estimating adverse selection costs from genetic testing for breast and ovarian cancer: The case of life insurance. Journal of Risk and Insurance, 66, 531–550.CrossRefGoogle Scholar