Genetic Discrimination in Insurance and Employment based on Personalized Risk Stratification for breast cancer screening

Manuela Reveiz ¹ Sarah Bouhouita-Guermech ^1* Kristina M. Blackmore ² Jocelyne Chiquette ^3,4,5 Éric Demers ³ Michel Dorval ^3,6,7 Laurence Lambert-Côté ³ Hermann NABI ^3,8,9 Nora Pashayan ¹⁰ Penny Soucy ³ Annie Turgeon ³ Meghan J. Walker ^11,2 Bartha Maria Knoppers ¹ Anna Chiarelli ^11,2 Jacques Simard ^12,3 Yann Joly ¹

• ¹ Centre of Genomics and Policy (CGP), McGill University, Montreal, Canada
• ² Ontario Health (Cancer Care Ontario), Toronto, Canada
• ³ CHU de Québec-Université Laval Research Center, Quebec City, Canada
• ⁴ CHU de Québec-Université Laval, Quebec City, Quebec, Canada
• ⁵ Département de Médecine Familiale et de Médecine D’urgence, Université Laval, Quebec City, Canada
• ⁶ Faculty of Pharmacy, Université Laval, Quebec City, Canada
• ⁷ CISSS de Chaudière-Appalaches Research Center, Lévis, Canada
• ⁸ Department of Social and Preventative Medicine, Faculty of Medicine, Université Laval, Quebec City, Canada
• ⁹ Université Laval Cancer Research Center, Quebec City, Canada
• ¹⁰ Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
• ¹¹ Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
• ¹² Department of Molecular Medicine, Faculty of Medicine, Université Laval, Quebec City, Canada

Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) incorporates the effects of common genetic variants, from polygenic risk scores, pathogenic variants in major breast cancer (BC) susceptibility genes, lifestyle/hormonal risk factors, mammographic density, and cancer family history to predict risk levels of developing breast and ovarian cancer. While offering multifactorial risk assessment to the population could be a promising avenue for early detection of BC, obstacles to its implementation including fear of genetic discrimination (GD), could prevent individuals from undergoing screening. Methods: The aim of our study was two-fold: determine the extent of legal protection in Canada available to protect information generated by risk prediction models such as the BOADICEA algorithm through a literature review, and then, assess individuals' knowledge of and concerns about GD in this context by collecting data through surveys. Results: Our legal analysis highlighted that while Canadian employment and privacy laws provide a good level of protection against GD, it remains uncertain whether the Genetic Non-Discrimination Act (GNDA) would provide protection for BC risk levels generated by a risk prediction model. The survey results of 3055 participants who consented to risk assessment in the PERSPECTIVE I&I project showed divergent perspectives of how the law would protect BC risk level in the context of employment and that a high number of participants did not feel that their risk level was protected from access and use by life insurers. Indeed, 49,1% of participants reckon that the level of breast cancer risk has an impact on a woman's ability to buy insurance and 58,9% of participants reckon that a woman's insurance might be cancelled if important health information (including level of breast cancer risk) is not given when buying or renewing life or health insurance. Conclusion: The results indicate that much work needs to be done to improve and clarify the extent of protection against GD in Canada and to inform the population of how the legal framework applies to risk levels generated by risk prediction models.