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
50%
50%
Combined
Group 1
Group 2
“Long Lived”
“Die Young”
£1,000
£2,000
£1,500
Who Actually Buys Insurance?
50%
50%
Combined
60%
Group 1
Group 2
“Long Lived”
“Die Young”
£1,000
£2,000
40%
£1,500
Who Actually Buys Insurance?
50%
50%
Combined
60%
Group 1
Group 2
“Long Lived”
“Die Young”
£1,000
£2,000
40%
£1,600
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:
Average:
15%
APKD1 mutation carrier:
75%
APKD2 mutation carrier:
30%
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
Smoking
Gene 2
Gene 1
Affluence
Disease
Diet
Gene 4
Gene 3
Gene 6
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
Insured
Untested
Tested
Dead
Insured
A Simple Population Model
No Family History
Family History
No Mutation
Family History
Mutation
Ban on Genetic Test Results
No Family History
Family History
No Mutation
Family History
Mutation
Ban on Family History As Well
No Family History
Family History
No Mutation
Family History
Mutation
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
Insurers may not use genetic test results.
More realistic mortality for mutation
carriers? (25% dead by age 60.)
~ 1%
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