Abstract
Personalized prevention uses family history and predictive genetic testing to identify people at high risk of serious diseases. The availability of predictive genetic tests is a newer and still-developing phenomenon. Many observers see tremendous potential for personalized prevention to improve public health. At the same time, the emergence of these new markets raises familiar health policy concerns about costs, cost-effectiveness, and health disparities. This paper first discusses an economic framework for the analysis of personalized prevention. On the demand side, consumers use personalized prevention as a form of information that allows them to make better choices about prevention, including medical care and health behaviors like diet and exercise. On the supply side, an interplay of complex market forces and regulations will determine the prices, advertising, and insurance coverage of predictive genetic tests. Beyond the question of whether health insurance will cover the costs of predictive genetic tests, there is a great deal of concern about whether consumers’ use of genetic tests might place them at risk of genetic discrimination or might lead to adverse selection. The paper also reports descriptive analysis of data from the 2000, 2005, and 2010 National Health Interview Surveys on the use of predictive genetic tests. The empirical analysis documents large socioeconomic status-related disparities in consumers having heard of genetic tests: for example, consumers with less schooling, Blacks, and Hispanics were substantially less likely to have heard of genetic tests. Evidence from other empirical studies provides little evidence that genetic testing leads to genetic discrimination in insurance markets. There is more evidence suggesting adverse selection, where genetic testing leads consumers to purchase long-term care insurance. The paper concludes with some preliminary thoughts about important directions for future research. The goal of the paper is to review relevant research to help develop an economic approach and social science research agenda into the determinants and consequences of genetic tests for prevention.
- 1
Returning to the examples studied by Stout et al. (2006), while screening every other year beginning at age 50 years lowers total costs by $92 billion, it also yields 1.9 million fewer quality-adjusted life years (QALYs), compared to annual screening starting at age 40 years. These estimates suggest that the reduced screening frequency involves a difficult tradeoff between reducing costs but saving fewer QALYs.
- 2
- 3
The magnitude of the estimated associations reveals a well-known limitation of the linear probability model: for consumers who are disadvantaged in multiple dimensions, the model can predict a probability below zero. When we use a non-linear model such as probit that addresses this limitation, the implied average marginal effects are similar to the results reported in Table 2. Results available upon request.
- 4
Myriad Genetics’ response to the FAQ: “Federal and state legislation protects your privacy and prohibits health insurance discrimination based on genetic information. About 200,000 people have been tested in the last 10 years and there are no documented cases of discrimination.”See http://www.bracnow.com/considering-testing/#20.
References
Agency for Healthcare Research and Quality [AHRQ] (2006) 2006 National Healthcare Disparities Report. Rockville, MD: U.S. Department of Health and Human Services.Search in Google Scholar
AHRQ (2012) The Guide to Clinical Preventive Services 2012. U.S. Preventive Services Task Force. U.S. Department of Health and Human Services, AHRQ Pub. No. 12-05154.Search in Google Scholar
Armstrong, K., B. Weber, G. FitzGerald, J. C. Hershey, M. V. Pauly, J. Lemaire, K. Subramanian and D. A. Asch (2003) “Life Insurance and Breast Cancer Risk Assessment: Adverse Selection, Genetic Testing Decisions, and Discrimination,” American Journal of Medical Genetics Part A, 120A(3):359–364.10.1002/ajmg.a.20025Search in Google Scholar
Avery, R., D. Kenkel, D. R. Lillard and A. Mathios (2007) “Private Profits and Public Health: Does Advertising of Smoking Cessation Products Encourage Smokers to Quit?,” Journal of Political Economy, 115(3):447–481.10.1086/520065Search in Google Scholar
Baer, H. J., P. Brawarsky, M. F. Murray and J. S. Haas (2010) “Familial Risk of Cancer and Knowledge and Use of Genetic Testing,” Journal of General Internal Medicine, 25(7):717–724.10.1007/s11606-010-1334-9Search in Google Scholar
Barigozzi, F. and D. Henriet (2011) “Genetic Information: Comparing Alternative Regulatory Approaches When Prevention Matters,” Journal of Public Economic Theory, 13(1):23–46.10.1111/j.1467-9779.2009.01491.xSearch in Google Scholar
Bloss, C. S., N. J. Schork and E. J. Topol (2011) “Effect of Direct-to-Consumer Genomewide Profiling to Assess Disease Risk,” New England Journal of Medicine, 364(6):524–534.10.1056/NEJMoa1011893Search in Google Scholar
David, G., S. Markowitz and S. Richards-Shubik (2010) “The Effects of Pharmaceutical Marketing and Promotion on Adverse Drug Events and Regulation,” American Economic Journal-Economic Policy, 2(4):1–25.10.1257/pol.2.4.1Search in Google Scholar
David, G. and S. Markowitz (2011) “Side Effects of Competition: the Role of Advertising and Promotion in Pharmaceutical Markets,” w17162. National Bureau of Economic Research Working Paper # 17162, June 2011. pp. 1–40.10.3386/w17162Search in Google Scholar
Deaton, A. (2002) “Policy Implications of the Gradient of Health and Wealth,” Health Affairs, 21(2):13–30.10.1377/hlthaff.21.2.13Search in Google Scholar
Doherty, N. A. and L. L. Posey (1998) “On the Value of a Checkup: Adverse Selection, Moral Hazard and the Value of Information,” Journal of Risk and Insurance, 65(2):189–211.10.2307/253533Search in Google Scholar
Duggan, M. and F. Scott-Morton (2010) “The Effect of Medicare Part D on Pharmaceutical Prices and Utilization,” American Economic Review, 100(1):590–607.10.1257/aer.100.1.590Search in Google Scholar
Gollust, S. E., S. C. Hull and B. S. Wilfond (2002) “Limitations of Direct-to-Consumer Advertising for Clinical Genetic Testing,” Journal of the American Medical Association, 288(14):1762–1767.10.1001/jama.288.14.1762Search in Google Scholar
Grossman, M. (1972) “On the Concept of Health Capital and the Demand for Health,” Journal of Political Economy, 80(2):223–255.10.1086/259880Search in Google Scholar
Heshka, J. T., C. Palleschi, H. Howley, B. Wilson and P. S. Wells (2008) “A Systematic Review of Perceived Risks, Psychological and Behavioral Impacts of Genetic Testing,” Genetics in Medicine, 10(1):19–32.10.1097/GIM.0b013e31815f524fSearch in Google Scholar
Hoel, M. and T. Iversen (2002) “Genetic Testing When There Is a Mix of Compulsory and Voluntary Health Insurance,” Journal of Health Economics, 21(2):253–270.10.1016/S0167-6296(01)00112-6Search in Google Scholar
Jeffords, J. M. and T. Daschle (2001) “Policy Issues – Political Issues in the Genome Era,” Science, 291(5507):1249–1251.10.1126/science.1058370Search in Google Scholar
Kenkel, D. (2000) “Prevention.” In: (A. J. Cuyler and J. P. Newhouse, eds.) The Handbook of Health Economics. Amsterdam, North-Holland, The Netherlands: Elsevier Science, pp. 1675–1720.Search in Google Scholar
Kenkel, D. and A. Mathios (2012) “Promotion to Physicians and Consumers.” In: (P. Danzon and S. Nicholson, eds.) Handbook on the Economics of the Pharmaceutical Industry. Oxford and New York: Oxford University Press, pp. 493–523.10.1093/oxfordhb/9780199742998.013.0016Search in Google Scholar
Link, B. G. and J. Phelan (1995) “Social Conditions as Fundamental Causes of Disease,” Journal of Health and Social Behavior, 35(Extra Issue):80–94.10.2307/2626958Search in Google Scholar
Matloff, E. and A. Caplan (2008) “Direct to Confusion: Lessons Learned from Marketing BRCA Testing,” American Journal of Bioethics, 8(6):5–8.10.1080/15265160802248179Search in Google Scholar
Myriad Genetics Inc. (2013) “BRCA Testing Granted Preventive Care Designation Under the Affordable Care Act.” Press Release, Salt Lake City, March 6, 2013 (Globe Newswire).Search in Google Scholar
Oster, E., E. R. Dorsey, J. Bausch, A. Shinaman, E. Kayson, D. Oakes, I. Shoulson, K. Quaid and Pharos Invest Huntington Study Grp (2008) “Fear of Health Insurance Loss among Individuals at Risk for Huntington Disease,” American Journal of Medical Genetics Part A, 146A(16):2070–2077.10.1002/ajmg.a.32422Search in Google Scholar
Oster, E., I. Shoulson, K. Quaid and E. R. Dorsey (2009) “Genetic Adverse Selection: Evidence from Long-Term Care Insurance and Huntington Disease.” National Bureau of Economic Research Working Paper 15326, September 2009.10.3386/w15326Search in Google Scholar
Oster, E., I. Shoulson, K. Quaid and E. R. Dorsey (2010) “Genetic Adverse Selection: Evidence from Long-Term Care Insurance and Huntington Disease,” Journal of Public Economics, 94(11–12):1041–1050.10.1016/j.jpubeco.2010.06.009Search in Google Scholar
Rothschild, M. and J. Stiglitz (1976) “Equilibrium in Competitive Insurance Markets – Essay on Economics of Imperfect Information,” Quarterly Journal of Economics, 90(4):629–649.10.2307/1885326Search in Google Scholar
Stout, N. K., M. A. Rosenberg, A. Trentham-Dietz, M. A. Smith, S. M. Robinson and D. G. Fryback (2006) “Retrospective Cost-Effectiveness Analysis of Screening Mammography,” Journal of the National Cancer Institute, 98(11):774–782.10.1093/jnci/djj210Search in Google Scholar
Strohmenger, R. and A. Wambach (2000) “Adverse Selection and Categorical Discrimination in the Health Insurance Markets: The Effects of Genetic Tests,” Journal of Health Economics, 19(2):197–218.10.1016/S0167-6296(99)00021-1Search in Google Scholar
USDHHS (U.S. Department of Health and Human Services) (2011) Centers of Disease Control and Prevention. 2010 National Health Interview Survey Public Use Data Release NHIS Survey Description. CDC, June 2011.Search in Google Scholar
Viswanathan, K. S., J. Lemaire, K. Withers, K. Armstrong, A. Baumritter, J. C. Hershey, M. V. Pauly and D. A. Asch (2007) “Adverse Selection in Term Life Insurance Purchasing Due to the Brca1/2 Genetic Test and Elastic Demand,” Journal of Risk and Insurance, 74(1):65–86.10.1111/j.1539-6975.2007.00202.xSearch in Google Scholar
Wideroff, L., S. T. Vadaparampil, N. Breen, R. T. Croyle and A. N. Freedman (2003) “Awareness of Genetic Testing for Increased Cancer Risk in the Year 2000 National Health Interview Survey,” Community Genetics, 6(3):147–156.Search in Google Scholar
Zick, C. D., C. J. Mathews, J. S. Roberts, R. Cook-Deegan, R. J. Pokorski and R. C. Green (2005) “Genetic Testing for Alzheimer’s Disease and Its Impact on Insurance Purchasing Behavior—Widespread Genetic Testing for Alzheimer’s Susceptibility Could Present Dilemmas for Long-Term Care Insurance,” Health Affairs, 24(2):483–490.10.1377/hlthaff.24.2.483Search in Google Scholar
Zick, C. D., K. R. Smith, R. N. Mayer and J. R. Botkin (2000) “Genetic Testing, Adverse Selection, and the Demand for Life Insurance,” American Journal of Medical Genetics, 93(1):29–39.10.1002/1096-8628(20000703)93:1<29::AID-AJMG6>3.0.CO;2-KSearch in Google Scholar
©2013 by Walter de Gruyter Berlin Boston
