An actuarial model of arrhythmogenic right ventricular cardiomyopathy and life insurance
Bibliographic record
Abstract
Many countries ban insurers from using genetic test results in underwriting. One study [Howard, R. C. W. (2014). Report to CIA research committee: Genetic testing model: If the underwriters had no access to known results. Canadian Institute of Actuaries (CIA).] stated that such a ban in Canada would expose life insurers to adverse selection, causing premiums to increase by 12%. More than a quarter of this cost was attributable to a single disorder, Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC). We model ARVC in a life insurance market, following the methodology of [Haçarız, O., Kleinow, T. & Macdonald, A. S. (2021). Genetics, insurance and hypertrophic cardiomyopathy. Scandinavian Actuarial Journal 2021, 54–81.], including ‘cascade’ genetic testing (CGT), so the rôle of family history in underwriting is modelled explicitly. We review (in the Appendix) the published epidemiology of ARVC, in particular the existence of an effective treatment, which we also include in our model. Our results are consistent with those of [Macdonald, A. S. & Yu, F. (2011). The impact of genetic information on the insurance industry: Conclusions from the ‘bottom-up’ modelling programme. Astin Bulletin 41(02), 343–376.] and [Haçarız, O., Kleinow, T. & Macdonald, A. S. (2021). Genetics, insurance and hypertrophic cardiomyopathy. Scandinavian Actuarial Journal 2021, 54–81.], namely, that in realistic scenarios premium increases would be negligible. We also consider the possibility of life settlement companies ‘gaming’ insurers by learning of adverse genetic test results, and conclude that to profit from purchasing policies from affected individuals, they would have to predict the future trajectory of the epidemiology of ARVC better than the epidemiologists themselves.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".