Genetics Influence on Life Insurance Angus Macdonald 26 November 2015

PART 1 THE PROBLEM

“Indeed the sociology of risk.... is an academic subject akin to the black actuarial arts which set insurance premiums. Even now insurance companies are plotting to use genetic medicine to limit their own risks.” (Arnold Kemp, Observer, 29 October 2000)

“Indeed the sociology of risk.... is an academic subject akin to the black actuarial arts which set insurance premiums. Even now insurance companies are plotting to use genetic medicine to limit their own risks.” (Arnold Kemp, Observer, 29 October 2000)

Same Premiums or Not?  Motor Insurance –40-year old, no accidents, Vauxhall Astra –17-year old, no experience, Porche 911

Same Premiums or Not?  Life Insurance –Man, 40, smoker –Man, 40, non-smoker

Same Premiums or Not?  Disability Insurance –Dentist, 40, male –Dentist, 40, female

Same Premiums or Not?  Long-Term Care Insurance –Woman, 60, has “Alzheimer mutation” –Woman, 60, no “Alzheimer mutation”

Insurance and Discrimination  Underwriting = Discrimination  Do insurers have a “right to underwrite”? –Age, smoking status, medical history: YES –Gender: NO (in EU since 2012) –Family history: YES –Disability: YES given evidence –Race: NO –Genetic test results: ?

Pooling of Risk Group 1 “Long Lived” £1,000 Group 2 “Die Young” £2,000 Combined £1,500 50%

Who Actually Buys Insurance? Group 1 “Long Lived” £1,000 Group 2 “Die Young” £2,000 Combined £1,500 50% 40% 60%

Who Actually Buys Insurance? Group 1 “Long Lived” £1,000 Group 2 “Die Young” £2,000 Combined £1,600 50% 40% 60%

Adverse Selection If someone knows about a health risk and has an incentive to buy insurance and does not disclose it to the insurer then the insurer doesn’t know who buys insurance

“I am not opposed to people knowing their predisposition to an illness.... I do oppose insurance companies and others taking this into account when they are assessing premiums, the prospects of getting a mortgage and employment.” Dr Ian Gibson MP, Daily Mail, 12 October 2000

What Did Government Do?  Moratorium agreed with industry –Insurers will not ask anyone to be tested –Insurers will not use genetic test results except sometimes for very large amounts of insurance –Insurers will not seek out “good” genes –Family history may still be used  Industry asked for research evidence for future approach to genetic information

Two Basic Questions The Genetics Question: Just how predictive are genetic tests? The Insurance Question: What might happen if insurers do not have access to genetic information?

PART 2 GENETICS

Single-Gene Disorders Gene Disease

An Example: APKD  Adult Polycystic Kidney Disease (APKD)  Leads to kidney failure and transplant  APKD1 –Causes ~ 85% of APKD  APKD2 –Causes ~ 15% of APKD  Often a family history of APKD

Dominant Inheritance

Very High Risk Probability of serious illness by age 60: APKD1 mutation carrier: 75% APKD2 mutation carrier: 30% Average: 15%

Single-Gene Disorders are Rare Huntington’s Disease 1 in 5,000 HNPCC 1 in 400 FAP 1 in 8,000 APKD 1 in 1,000 (Sudbery, 1998)

Multifactorial Disorders Disease Gene 4 Gene 2 Gene 1 Gene 3 Smoking Gene 6 Diet Affluence Gene 5

Multifactorial Inheritance ? ? ? ? ?

What is Genetic Information?  Genetic information? –Result of a DNA-based test –Test for a gene product (e.g. kidney cysts) –Family history of a Mendelian disorder –Family history of a common disorder  How can we distinguish –genetic contributions to disease? –shared environment (including affluence)?

Genetic Tests: How Predictive? Single-gene disorders: STRONGLY Mutifactorial disorders: WEAKLY

PART 3 GENETICS AND INSURANCE

The Cost of Genetic Information  If insurers do have genetic information: –People at higher risk might pay more –Question: how much more?  If insurers do not have genetic information: –People at higher risk might over-insure (adverse selection) –Question: how much would that cost?

A Simple Life Insurance Model Dead TestedUntestedInsured

A Simple Population Model No Family History Family History No MutationMutation

Ban on Genetic Test Results No Family History Family History No MutationMutation

Ban on Family History As Well No Family History Family History No MutationMutation

Features of the Model  The size of the insurance market  The extent of genetic testing  Population mutation frequencies  The behaviour of “adverse selectors”  The underwriting practices of insurers

Example: Life Insurance We model a large life insurance market assuming 2% of the population is affected by very severe single-gene disorders (75% dead by age 60). We assume that adverse selection is very common - someone with an adverse genetic test result will very soon buy insurance.

Example: Life Insurance Insurers may not use genetic test results. By how much would everybody’s premiums increase to pay for the adverse selection? ~ 4%

Example: Life Insurance More realistic mortality for mutation carriers? (25% dead by age 60.) ~ 1% Insurers may not use genetic test results.

Example: Life Insurance Insurers may not use family history. By how much would everybody’s premiums increase? ~ 9%

Example: Life Insurance Insurers may not use family history. What if adverse selection was not so extreme? ~ 8%

Example: Life Insurance Insurers may not use family history. What if the life insurance market was much smaller? ~ 22%

Plunging Price of Life Cover “Good news: the price of life insurance is tumbling … In the past five years term insurance premiums have fallen by 40% …” (Guardian, 11 May 2002)

Conclusions  Life insurance is not affected much –very large market –multifactorial disorders not significant  Critical illness insurance is a problem –smaller market –ban on family history most serious  Disability, long-term care insurance …?

Why Are Genes Special?  Probability of dying before age 60?  Mr Smith and Mr Brown –One is a mutation carrier: 20% –One had a childhood illness: 20%  If you did not know which of Smith or Brown had a mutation, who would get special treatment?

Genetics Influence on Life Insurance Angus Macdonald 26 November 2015