Multifactorial Genetic Disorders and Adverse Selection: Epidemiology Meets Economics

Abstract

Rapid advances in genetic epidemiology and the setting up of large-scale cohort studies have shifted the focus from severe, but rare, single gene disorders to less severe, but common, multifactorial disorders. This will lead to the discovery of genetic risk factors for common diseases of major importance in insurance underwriting. If genetic information continues to be treated as private, adverse selection becomes possible, but it should occur only if the individuals at lowest risk obtain lower expected utility by purchasing insurance at the average price than by not insuring. We explore where this boundary may lie, using a simple 2 × 2 gene-environment interaction model of epidemiological risk, in a simplified 2-state insurance model and in a more realistic model of heart-attack risk and critical illness insurance. Adverse selection does not appear unless purchasers are not very risk-averse, and insure a small proportion of their wealth; or unless the elevated risks implied by genetic information are implausibly high. In many cases adverse selection is impossible if the low-risk stratum of the population is large enough. These observations are strongly accentuated in the critical illness model by the presence of risks other than heart attack, and the constraint that differential heart-attack risks must agree with the overall population risk. We find no convincing evidence that adverse selection is a serious insurance risk, even if information about multifactorial genetic disorders remains private.

Modelling Adverse Selection in The Presence of a Common Genetic Disorder: the Breast Cancer Polygene

“…In the CI model a unit sum assured is payable on transition from the Healthy state to any CI state (BC, OC or Other Critical Illness). For simplicity, and consistency with previous studies of insurance and utility (Hoy & Witt, 2007;Macdonald & Tapadar, 2009), let the force of interest be d = 0. This means that EPVs are equivalent to the probabilities of the CI event occurring.…”

“…It is hard to introduce these fully in the Markov models just described, but we can, nevertheless, use them to describe limits on the behaviour of market participants. The utility function U(w), with UЈ(w) > 0 and UЉ(w) < 0, can be interpreted as an increasing concave relation that describes the relative satisfaction gained from holding wealth w. Macdonald & Tapadar (2009) parameterised four utility functions, three from the Iso-Elastic family and one from the Negative Exponential family. We use the same utility functions and will refer to them as Models 1, 2, 3 and 4, as shown in Table 7.…”

“…In fact we could not compute them because of numerical overflow, which is why they are missing, but for all practical purposes these are cases where adverse selection will never appear. This THE BREAST CANCER POLYGENE 391 observation has an exact analogue in Macdonald & Tapadar (2009). Note that most missing values appear under utility Models 3 and 4, which were the two models parameterised from real economic data; Models 1 and 2, in contrast, were chosen simply to illustrate a lower level of risk-aversion.…”

Insurance Loss Coverage Under Restricted Risk Classification: The Case of Iso-Elastic Demand

“…For example Hoy (2006) assigns equal weight to the expected utilities of all individuals. In the actuarial literature, Macdonald & Tapadar (2010) also take a utility-based approach, while De Jong & Ferris (2006) instead model insurance demand directly as an elasticity-driven function of the pooled price, without explicitly considering utilities. The present paper follows this last approach.…”

Genetics and insurance in the United Kingdom 1995–2010: the rise and fall of “scientific” discrimination

“…Macdonald 1997, 2000, Viswanathan et al 2007, Macdonald and Tapadar 2010, Macdonald and Yu 2011. The results of these simulations can be succinctly stated: to a first approximation, preventing insurers' access to genetic test results will probably lead to only a tiny increase in average insurance prices, seldom rising above 1%.…”