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