Evidence map›Paper›PMID 40110039›Full record

ArticleFrontiers in genetics2025

Genetic discrimination in insurance and employment based on personalized risk stratification for breast cancer screening.

Manuela Reveiz, Sarah Bouhouita-Guermech, Kristina M Blackmore, Jocelyne Chiquette, Éric Demers, Michel Dorval, Laurence Lambert-Côté, Hermann Nabi, Nora Pashayan, Penny Soucy and 6 more

Abstract read
In one paragraph

Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Manuela ReveizCentre of Genomics and Policy (CGP), McGill University, Montreal, QC, Canada.
Sarah Bouhouita-GuermechCentre of Genomics and Policy (CGP), McGill University, Montreal, QC, Canada.
Kristina M BlackmoreOntario Health (Cancer Care Ontario), Toronto, ON, Canada.
Jocelyne ChiquetteCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Éric DemersCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Michel DorvalCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Laurence Lambert-CôtéCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Hermann NabiCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Nora PashayanDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
Penny SoucyCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Annie TurgeonCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Meghan J WalkerOntario Health (Cancer Care Ontario), Toronto, ON, Canada.
Bartha M KnoppersCentre of Genomics and Policy (CGP), McGill University, Montreal, QC, Canada.
Anna M ChiarelliOntario Health (Cancer Care Ontario), Toronto, ON, Canada.
Jacques SimardCHU de Québec-Université Laval Research Center, Quebec City, QC, Canada.
Yann JolyCentre of Genomics and Policy (CGP), McGill University, Montreal, QC, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

breast cancercanadian employment legislationcanadian insurance legislationgenetic discriminationpolygenic risk scorerisk-stratified breast cancer screening

Identifiers

PMID40110039
PMCPMC11919849

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.