Adult Polycystic Kidney Disease and Critical Illness Insurance

Gutiérrez, Cristina; F.F.A., Angus S. Macdonald

doi:10.1080/10920277.2003.10596092

Cited by 22 publications

(41 citation statements)

References 42 publications

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“…In the latter case, EPVs are weighted averages of EPVs given presence in states 15, 25, 35, 45 and 55, the weights being the occupancy probabilities in each of these states at the applicant's age. The rate of onset of 'other critical illnesses' is based on the model described in Gutiérrez & Macdonald (2003). The rate of mortality is based on the English Life Tables No.15, adjusted to exclude deaths which follow a CI insurance claim.…”

Section: A Dynamic Family History Modelmentioning

confidence: 99%

“…The rate of mortality is based on the English Life Tables No.15, adjusted to exclude deaths which follow a CI insurance claim. We refer the reader to Gutiérrez & Macdonald (2003) for details.…”

Section: A Dynamic Family History Modelmentioning

confidence: 99%

“…. We fix the other intensities at their estimated values from Gutiérrez & Macdonald (2003). We denote this set of intensities, some fixed and others to be estimated, m * , with estimate m * given some data, whose corresponding estimator m * has sampling distribution denoted by F * .…”

Section: More On Sampling Uncertainty In Actuarial Modelsmentioning

confidence: 99%

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Sampling Distributions of Critical Illness Insurance Premium Rates: Breast and Ovarian Cancer

Lu¹,

Macdonald²,

Waters³

2008

ASTIN Bull.

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Evaluating the risk of disorders in long-term insurance often relies on rates of onset estimated from quite small epidemiological studies. These estimates can carry considerable uncertainty, hence so may functions of them, such as a premium rate. In the case of genetic disorders, where it may be required to demonstrate the reliability of genetic information as a risk factor, such uncertainty may be material. Epidemiological studies publish their results in a variety of forms and it is rarely easy to estimate the sampling distribution of a premium rate without access to the original data. We found a large study of breast and ovarian cancer that cited relative risks of breast and ovarian cancer onset, with confidence intervals, in 10-year age groups. We obtained critical illness premium rates and their sampling distributions by parametric bootstrapping, and investigated the effect of possible patterns of sampling correlations. We found that this study provides ample statistical evidence that known BRCA1 or BRCA2 mutations, or a typical family history of breast or ovarian cancer, are reliable risk factors, but the sampling covariances of the relative risks could be important at some ages and terms. Studies that cite only standard errors of parameter estimates erect a small but awkward barrier between the models they describe, and some important actuarial questions.

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Section: A Dynamic Family History Modelmentioning

confidence: 99%

Section: A Dynamic Family History Modelmentioning

confidence: 99%

See 1 more Smart Citation

Sampling Distributions of Critical Illness Insurance Premium Rates: Breast and Ovarian Cancer

Lu¹,

Macdonald²,

Waters³

2008

ASTIN Bull.

View full text Add to dashboard Cite

show abstract

“…We therefore perform simulations and calculate male premiums ignoring BRCA mutation in males except for transmission from father to daughter. Transition rates for males are those given in Gutiérrez & Macdonald (2003). Table 14 gives the premium rates as a percentage of those for a female with log RR = log RR and no BRCA mutation.…”

Section: Effect Of the 'Test-achats' European Court Of Justice Rulingmentioning

confidence: 99%

“…, g 147 ) where g 0 ∈ {0, 1, 2} is the major genotype corresponding to BRCA0, BRCA1, BRCA2 respectively. For consistency with previous work, transition intensities from 'Healthy' to 'Dead' and 'Other CI' are taken from Gutiérrez & Macdonald (2003). These include onset of all cancers so onset rates of BC and OC have been removed from them, using transition intensities from Macdonald, Waters, & Wekwete (2003a) as these were calculated from the same data (Cancer Registrations 1990-1992.…”

Section: Insurance Modelmentioning

confidence: 99%

The impact of known breast cancer polygenes on critical illness insurance

Adams

Donnelly

Macdonald

2013

Scandinavian Actuarial Journal

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Genetic studies indicate that the inherited risk of breast cancer is mediated by the well-studied major genes BRCA1 and BRCA2, and a polygenic component, probably with many genes each making a small contribution. Recently, seven polygenes have been found (Pharoah et al., 2008) contributing an estimated 3.6% of all familial risk This suggests that the polygenic component may involve well over 100 genetic loci.We extrapolate these new results into a polygenic model with 147 genetic loci and simulate lifetimes of families to calculate the premium ratings appropriate for a family history of breast or ovarian cancer. We model the adverse selection costs arising from restricting the use of genetic test information in critical illness insurance underwriting in light of new European legislation banning the use of gender for insurance underwriting. In this setting we confirm the overall conclusion of Macdonald & McIvor (2009) that the polygene confers higher adverse selection risk than the BRCA genes. We establish that their three-gene polygenic model does not overly inflate the insurance costs attributable to a polygenic component of breast cancer risk under a model with 147 polygenes.

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Genetics and Insurance

Macdonald

2004

Encyclopedia of Actuarial Science

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Most actuarial modeling has concentrated on single‐gene disorders, because it necessarily relies on genetic epidemiology to parameterize any models, and that is where the epidemiology is most advanced. We can identify two broad approaches. A top‐down approach treats whole classes of genetic disorder as if they were homogeneous. This avoids the need to model individual disorders in detail, which in the case of multifactorial disorders may be impossible just now anyway. If, under extremely adverse assumptions, either extra premiums or the costs of adverse selection are small, this is a useful general conclusion; see for examples. In the long run, this approach is limited. A bottom‐up approach involves detailed modeling of individual disorders, and estimating the overall premium increases or costs of adverse selection by aggregation. Examples include models of breast cancer and ovarian cancer, Huntington's Disease, early‐onset Alzheimer's Disease, adult polycystic kidney disease and Alzheimer's Disease.

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Adult Polycystic Kidney Disease and Critical Illness Insurance

Cited by 22 publications

References 42 publications

Sampling Distributions of Critical Illness Insurance Premium Rates: Breast and Ovarian Cancer

Sampling Distributions of Critical Illness Insurance Premium Rates: Breast and Ovarian Cancer

The impact of known breast cancer polygenes on critical illness insurance

Genetics and Insurance

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