Genetic discrimination in insurance and employment based on personalized risk stratification for breast cancer screening
Bibliographic record
Abstract
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 3,055 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 could have 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.
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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".