53 GENETIC DISCRIMINATION BY INSURERS AS A RESULT OF GENETIC RESEARCH AND TESTING: A COMPARISON OF NATIONAL POLICIES
Bibliographic record
Abstract
Purpose Fear of discrimination by health and life insurers has been shown to deter individuals from participating in genetic research and testing. We aimed to construct a comparison of genetic discrimination policies in Canada, Australia, the United Kingdom, and the United States providing useful lessons regarding the management of genetic discrimination public policy. Methods MEDLINE and the HumGen database were searched for items related to genetic discrimination and health and/or life insurance. Results The Canadian, Australian, and UK health systems cover all citizens regardless of disease risk. However, life insurance applicants are not protected. In 2004, the Canadian Genetics and Life Insurance Task Force released a “Points to Consider Document” highlighting the importance of having a Canadian debate on the necessity of adopting a moratorium on the use of genetic test results for life insurance. In 2003, the Australian Law Reform Commission and the Australian Health Ethics Committee of the National Health and Medical Research Council recommended the adoption of a system consisting of a regulatory review aimed at ensuring that genetic information would be used in a scientifically reliable and actuarially sound manner. The Association of British Insurers9 Code of Practice, in 1999, mandated a “5-year moratorium on the use of genetic test results by insurers” up to a certain policy amount on life, long-term care and disability insurance. In 2001, the moratorium was strengthened and extended for an additional 5 years. As of 2004, 48 states and the District of Columbia had passed some form of legislation prohibiting genetic discrimination in health insurance decisions. At the federal level, The Genetic Information Non-discrimination Act (S. 1053) prohibits discrimination on the basis of genetic information regarding health insurance. It is now in the House of Representatives, where its outcome is uncertain. Conclusions The public9s concern about their own insurability necessitates action to ensure the successful conduct of future genetic epidemiological studies and testing. We argue that the adoption of a voluntary moratorium on genetic test results by the insurance industry appears to be the best option to answer both public anxiety and to provide a minimal amount of protection to insurance applicants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".