Transcript Genetics and Insurance: An Actuary's View

The Economic Stakes Involved in
Genetic Testing for Insurance
Companies
Angus Macdonald
Heriot-Watt University, Edinburgh
and the Maxwell Institute for Mathematical Sciences
Outline
 Fundamental
questions
 Problems posed by genetic testing
 Seeking evidence from data
 Examples
 Conclusions
Same Premiums or Not?
 Motor
Insurance
– 40-year old, no accidents, family car
– 17-year old, no experience, sports car
Same Premiums or Not?
 Life
Insurance
– Man, 40, smoker
– Man, 40, non-smoker
Same Premiums or Not?
 Pension
– Man, age 65
– Woman, age 65
Same Premiums or Not?
 Life
Insurance
– Man, 30, father had Huntington’s disease
– Man, 30, no family history of Huntington’s
Same Premiums or Not?
 Life
Insurance
– Woman, 30, tested and has BRCA1 mutation
– Woman, 30, never tested
Mathematical Basis of Insurance
 All
these examples rest on the same
principles
 Insurance has a mathematical basis
– Imperfect, fuzzy
– Judgement not excluded
 Arbitrary
pricing MAY, SOMETIMES,
damage the system
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
Two Kinds of Adverse Selection
 Insurers
gaming against each other
– Smoker/Non-Smoker differentials
– Male/female differentials (?)
 Applicants
not disclosing information
– AIDS (USA)
– Mortgage life insurance (UK)
– Genetic information (?)
Pooling of Risk
50%
50%
Combined
Group 1
Group 2
“Long Lived”
“Die Young”
£1,000
£2,000
£1,500
Two Basic Economic Questions
 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?
Single-Gene Disorders
Gene
Disease
Single Gene Disorders
 Can
present high risk of disease/death
 Can have late onset
 Treatment drastic or non-existent
 Rare
 Known about - epidemiology exists
 Can present clear pattern in family history
 Family history risk already underwritten
Very High Risk
Probability of serious illness by age 60:
Average:
15%
APKD1 mutation carrier:
75%
Huntington’s mutation carrier: 100%
Multifactorial Disorders
Smoking
Gene 2
Gene 1
Affluence
Disease
Diet
Gene 4
Gene 3
Gene 6
Gene 5
Multifactorial Disorders
 Common
diseases (cancer, heart disease)
 Complex interactions
– Many variants of many genes
– Environment
 Altered
susceptibility, not very high risk
 Pattern of inheritance unclear
 Not much epidemiology (yet)
Genetic Tests: How Predictive?
Single-gene disorders: STRONGLY
Multifactorial disorders: WEAKLY
An Example of Evidence: APKD
 Adult
Polycystic Kidney Disease (APKD)
 Leads to kidney failure and transplant
 APKD1
– Causes ~ 85% of APKD
 APKD2
– Causes ~ 15% of APKD
 Epidemiology
exists
CI Extra Premiums (Males)
Gene
Age 30 Age 30 Age 30 Age 40
Term 10 Term 20 Term 30 Term 10
APKD1
APKD2
492%
108%
639%
101%
521%
99%
775%
100%
(FH)
214%
267%
209%
305%
Adverse Selection Costs (CI)
 Premium
increases to cover cost
 Under extreme assumptions:
– Ban on all test results
– Ban on adverse test results
– Ban on family history
(1) Cost of broader risk pool
(2) Cost of adverse selection
(Males)
0.44%
0.32%
0.35%
1.25%
Life Ins Extra Premiums (Males)
No Transplants, Dialysis Only
Gene
Age 30 Age 30 Age 30 Age 40
Term 10 Term 20 Term 30 Term 10
APKD1
APKD2
73%
17%
132%
28%
146%
31%
93%
16%
(FH)
32%
57%
62%
37%
Life Ins Extra Premiums (Males)
Immediate Transplantation
Gene
Age 30 Age 30 Age 30 Age 40
Term 10 Term 20 Term 30 Term 10
APKD1
APKD2
12%
3%
44%
9%
53%
11%
19%
3%
(FH)
5%
19%
23%
8%
CI Extra Premiums (Males)
Gene
Age 30 Age 30 Age 30 Age 40
Term 10 Term 20 Term 30 Term 10
APKD1
APKD2
492%
108%
639%
101%
521%
99%
775%
100%
(FH)
214%
267%
209%
305%
Challenges to Family History
 Heterogeneity
means that an adverse test is
not always worse that family history
 If family history is uninsurable, is there an
implied requirement to be tested?
 If treatment normalizes risk, is there an
implied requirement to be treated?
Genetics of Tomorrow
 Genetics
of common diseases
 Gene-gene, gene-environment interactions
 Whole-genome scans, genetic arrays
 Large-scale population studies
 Novel mechanisms (epigenetics, RNA
interference)
 Genetic therapy
Insurance Implications
High-throughput genetic arrays will reveal much
about complex genetic influences on biological
processes – but this is not the same as disease.
 Understanding biological processes better will
help to understand disease – but this is not the
same as epidemiology.
 Epidemiology will emerge:

– But it will not be highly predictive, as for single-gene
disorders
– For insurance purposes it might fail criteria like
“reliability”.
Why Are Genes Special?
 Probability
of dying before age 60?
 Mr Smith and Mr Brown
– One is a mutation carrier:
20%
– One has had a serious illness: 20%
 If
you did not know which of Smith or
Brown had a mutation, who would get
special treatment?
The Economic Stakes Involved in
Genetic Testing for Insurance
Companies
Angus Macdonald
Heriot-Watt University, Edinburgh
and the Maxwell Institute for Mathematical Sciences