Breast Cancer Risk Estimation and Personal Insurance: A Qualitative Study Presenting Perspectives from Canadian Patients and Decision Makers
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".