Abstract

Genetic stratification approaches in personalized medicine may considerably improve our ability to predict breast cancer risk for women at higher risk of developing breast cancer. Notwithstanding these advantages, concerns have been raised about the use of the genetic information derived in these processes, outside of the research and medical health care settings, by third parties such as insurers. Indeed, insurance applicants are asked to consent to insurers accessing their medical information (implicitly including genetic) to verify or determine their insurability level, or eligibility to certain insurance products. This use of genetic information may result in the differential treatment of individuals based on their genetic information, which could lead to higher premium, exclusionary clauses or even the denial of coverage. This phenomenon has been commonly referred to as “Genetic Discrimination” (GD). In the Canadian context, where federal Bill S-201, An Act to prohibit and prevent genetic discrimination, has recently been enacted but may be subject to constitutional challenges, information about potential risks to insurability may raise issues in the clinical context. We conducted a survey with women in Quebec who have never been diagnosed with breast cancer to document their perspectives. We complemented the research with data from 14 semi-structured interviews with decision-makers in Quebec to discuss institutional issues raised by the use of genetic information by insurers. Our results provide findings on five main issues: (1) the reluctance to undergo genetic screening test due to insurability concerns, (2) insurers' interest in genetic information, (3) the duty to disclose genetic information to insurers, (4) the disclosure of potential impacts on insurability before genetic testing, and (5) the status of genetic information compared to other health data. Overall, both groups of participants (the women surveyed and the decision-makers interviewed) acknowledged having concerns about GD and reported a need for better communication tools discussing insurability risk. Our conclusions regarding concerns about GD and the need for better communication tools in the clinical setting may be transferable to the broader Canadian context.

Highlights

  • Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012 (World Cancer Research Fund

  • This study is part of a larger research program, PERSPECTIVE, aimed at developing and implementing a personalized riskstratification approach in order to prevent and detect breast cancer at its earliest stages (Lévesque et al, 2015, p. 283). Considering that this new approach is not yet offered in medical practice and it includes a genetic test component, we focused our study on issues that could be raised by the implementation of a “genetic test” or a “genetic screening test”7 to estimate individual risk for screening purpose

  • In your opinion, is genetic information (G.I.) different from other types of medical information? SUB-THEMES G.I. is personal information G.I. relates to a possible future risk G.I. is confidential G.I. is complex There is a distinction between G.I. and health-related data G.I. is just one of many types of information

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Summary

Introduction

Breast cancer is the most common cancer in women worldwide, with nearly 1.7 million new cases diagnosed in 2012 (World Cancer Research Fund). A high proportion of breast cancer occurs in women at a relatively high risk, which highlights the importance of identifying predisposed women in order to facilitate screening and prevention (Peto and Mack, 2000). In this context, common polygenic variants in the general population that are associated with a low cancer-predisposing risk may be more clinically useful for population-screening programs than low-penetrant high-risk predisposition alleles (Pharoah et al, 2008). Population screening programs using age as a criterion for eligibility to routine mammography screening might instead exploit individual risk levels based on polygenic variants (Pharoah et al, 2008; Pashayan et al, 2011; Burton et al, 2013; Gagnon et al, 2016)

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