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The Economics of Personalization in Prevention and Public Health

Don S. Kenkel and Hua Wang

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.


Corresponding author: Don S. Kenkel, Department of Policy Analysis and Management, MVR Hall, Cornell University, Ithaca, NY 14853, USA, e-mail:

  1. 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. 2
  3. 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. 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.

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Published Online: 2013-07-05
Published in Print: 2013-09-01

©2013 by Walter de Gruyter Berlin Boston