Estimating Adverse Selection Costs from Genetic Testing for Breast and Ovarian Cancer: The Case of Life Insurance

Genetic testing is a concern for insurers if they cannot use test results in underwriting. We model adverse selection in an insurance market with genetic testing for breast and ovarian cancer. Increased forces of mortality resulting from a family history of cancer or a positive test for a BRCA mutation are calculated. Using a Markov model, we estimate costs of adverse selection, assuming various testing and insurance purchase behaviors. Adverse selection should be controllable if companies apply strict underwriting rules, requesting cancer history and onset age for all first-degree relatives. If insurers fail to correctly identify the family history of the application and use it in pricing, adverse selection costs could become unbearable. GENETIC TESTING AND THE FEAR OF ADVERSE SELECTION Adverse selection can be defined as the process by which prospective policyholders may gain financial advantage through insurance purchase decisions based on risk characteristics known to them, but unknown and not revealed to the insurer. It is a source of concern for insurance companies because it could result in underpricing. Recent developments in the Human Genome Project, while offering medical promKrupa Subramanian is Assistant Professor at the Department of Risk, Insurance, and Healthcare Management at Temple University. Jean Lemaire is Professor and Chairperson of the Insurance and Risk Management Department; John C. Hershey is Professor at the Operations and Information Management Department; Mark V. Pauly is Professor at the Health Care Systems Department: all at the Wharton School, University of Pennsylvania. Katrina Armstrong is Assistant Professor; David Asch is Associate Professor and Executive Director of the Leonard Davis Institute: both at the School of Medicine, University of Pennsylvania. The authors wish to thank the Editor Patrick Brockett and two anonymous referees for excellent comments and suggestions.