The Economic Stakes Involved in Genetic Testing for Insurance Companies Angus Macdonald Heriot-Watt...
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Transcript of The Economic Stakes Involved in Genetic Testing for Insurance Companies Angus Macdonald Heriot-Watt...
The Economic Stakes Involved in Genetic Testing for Insurance
Companies
Angus Macdonald
Heriot-Watt University, Edinburghand 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?
Group 1
“Long Lived”
£1,000
Group 2
“Die Young”
£2,000
Combined
£1,500
50% 50%
40%
60%
Who Actually Buys Insurance?
Group 1
“Long Lived”
£1,000
Group 2
“Die Young”
£2,000
Combined
£1,600
50% 50%
40%
60%
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
Group 1
“Long Lived”
£1,000
Group 2
“Die Young”
£2,000
Combined
£1,500
50% 50%
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:
APKD1 mutation carrier: 75%
Huntington’s mutation carrier: 100%
Average: 15%
Multifactorial Disorders
Disease
Gene 4
Gene 2Gene 1
Gene 3
Smoking
Gene 6
Diet
Affluence
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 Term 10
Age 30 Term 20
Age 30 Term 30
Age 40 Term 10
APKD1 492% 639% 521% 775% APKD2 108% 101% 99% 100% (FH) 214% 267% 209% 305%
Adverse Selection Costs (CI)
Premium increases to cover cost Under extreme assumptions:
– Ban on all test results 0.44%– Ban on adverse test results 0.32%– Ban on family history
(1) Cost of broader risk pool 0.35% (2) Cost of adverse selection 1.25%
(Males)
Life Ins Extra Premiums (Males)
Gene
Age 30 Term 10
Age 30 Term 20
Age 30 Term 30
Age 40 Term 10
APKD1 73% 132% 146% 93% APKD2 17% 28% 31% 16% (FH) 32% 57% 62% 37%
No Transplants, Dialysis Only
Life Ins Extra Premiums (Males)
Gene
Age 30 Term 10
Age 30 Term 20
Age 30 Term 30
Age 40 Term 10
APKD1 12% 44% 53% 19% APKD2 3% 9% 11% 3% (FH) 5% 19% 23% 8%
Immediate Transplantation
CI Extra Premiums (Males)
Gene
Age 30 Term 10
Age 30 Term 20
Age 30 Term 30
Age 40 Term 10
APKD1 492% 639% 521% 775% APKD2 108% 101% 99% 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, Edinburghand the Maxwell Institute for Mathematical Sciences