The impact of known breast cancer polygenes on critical illness insurance

Adams, C.; Donnelly, Catherine; Macdonald, Angus S.

doi:10.1080/03461238.2013.794520

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Maxwell et al (18) suggests that the use of polygenic scores provides additional information for insurance underwriting beyond these conventional risk factors. The actuarial models of MacDonald and McIvor (77) and Adams et al (78) suggest adverse selection in the presence of polygenic risk for breast cancer (i.e. people with a higher polygenic risk for breast cancer will tend to choose more comprehensive policies).…”

Section: Predicting Costs and Related Phenotypes From Genotypementioning

confidence: 99%

Genomics and Insurance in the United Kingdom: Increasing Complexity and Emerging Challenges

Dixon¹,

Horton²,

McDermott³

et al. 2023

Preprint

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This article identifies issues relating to the use of genetics and genomics in insurance that may challenge existing regulatory models in the UK and elsewhere. We discuss three core issues: (1) As genomic testing advances, and results are increasingly relevant to guide healthcare across an individual's lifetime, the distinction between diagnostic and predictive testing that the current UK insurance code relies on becomes more blurred and this has consequences what constitutes a genetic “result” (2) The emerging category of pharmacogenomic tests which are predictive only the in the context of a specific prescribing moment (3) The increasing availability and affordability of polygenic scores that are neither clearly diagnostic nor highly predictive, but which nonetheless might have incremental value for insurance underwriting beyond conventional factors. We also outline the broad scope of possible regulatory responses to these developments. At present in the UK, consumers are not obliged to declare results of predictive genetic tests except in very specific scenarios, meaning that pricing is not actuarially fair (premiums paid in such cases are likely to be lower than the expected value of the compensation received). We suggest a deliberative approach is required to establish where these deviations from actuarially fair pricing should be upheld, and whether there might be situations in which they should be withdrawn.

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Section: Predicting Costs and Related Phenotypes From Genotypementioning

confidence: 99%

Genomics and Insurance in the United Kingdom: Increasing Complexity and Emerging Challenges

Dixon¹,

Horton²,

McDermott³

et al. 2023

Preprint

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“…9.4.2. For the purposes of this exercise, we consider high-risk variants of APOE to be ε 3 ε 4 and ε 4 ε 4, while low-risk variants are ε 2 ε 2 and ε 3 ε 3 with testing occurring at a rate of 0.08916 per annum , the high rate from Adams et al (2013). This is used to exaggerate the costs in order to illustrate how genetic testing interacts with other scenarios.…”

Section: Calculating Adverse Selectionmentioning

confidence: 99%

Adverse selection in a start-up long-term care insurance market

Adams¹,

Donnelly²,

Macdonald³

2014

Br. Actuar. J.

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Common to all previous studies assessing the cost of adverse selection associated with genetics has been the assumption of an established market, i.e., the adverse selectors have been buying insurance at that rate for such a period that premiums have already absorbed it. Their analyses involve calculating the percentage difference between premiums in a market with adverse selection and one without adverse selection. They can shed no light on how the premiums would get to this stage over time and what losses might be incurred in the process. We take the modelling further by outlining a multiple state Markov model for a start-up market of long-term care insurance. With this model, we explicitly show the progression of adverse selection costs using the development of information that an insurer would gain from analysing the claims history of its existing business, to reprice premiums for new business. To overcome the complication of insurance benefit amounts, which depend on the value of previous benefit payments, we develop a simulation approach of estimating the expected present values of insurance benefits and premium payments. In applying our modelling to a UK setting, we find genetic testing of the apolipoprotein E gene (whose variants can cause a high risk of developing dementia) to be of a relatively small impact compared with our hypothetical state of intermediate dementia progression. Furthermore, we find that the government’s cap on care costs has little effect on adverse selection costs as it benefits only a small proportion of people.

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“…In most cases, however, this is modeled by a simple stochastic model, a discrete-time, discrete-state Markov chain at best; see e.g. [5], [8], [18], and more recently [1], to name a few. By contrast, the present paper proposes a model using diffusion processes.…”

Section: Introductionmentioning

confidence: 99%

The Thermodynamic Approach to Whole-Life Insurance: A Method for Evaluation of Surrender Risk

Akahori,

Ida,

Nishida

et al. 2020

Preprint

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We introduce a collective model for life insurance where the heterogeneity of each insured, including the health state, is modeled by a diffusion process. This model is influenced by concepts in statistical mechanics. Using the proposed framework, one can describe the total pay-off as a functional of the diffusion process, which can be used to derive a level premium that evaluates the risk of lapses due to the so-called adverse selection. Two numerically tractable models are presented to exemplify the flexibility of the proposed framework.

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The impact of known breast cancer polygenes on critical illness insurance

Cited by 7 publications

References 29 publications

Genomics and Insurance in the United Kingdom: Increasing Complexity and Emerging Challenges

Genomics and Insurance in the United Kingdom: Increasing Complexity and Emerging Challenges

Adverse selection in a start-up long-term care insurance market

The Thermodynamic Approach to Whole-Life Insurance: A Method for Evaluation of Surrender Risk

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