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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.

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The economic stakes involved in genetic testing for insurance companies

The Economic Stakes Involved in Genetic Testing for Insurance Companies

Angus Macdonald

Heriot-Watt University, Edinburgh

and the Maxwell Institute for Mathematical Sciences


Outline
Outline Insurance Companies

  • Fundamental questions

  • Problems posed by genetic testing

  • Seeking evidence from data

  • Examples

  • Conclusions


Same premiums or not
Same Premiums or Not? Insurance Companies

  • Motor Insurance

    • 40-year old, no accidents, family car

    • 17-year old, no experience, sports car


Same premiums or not1
Same Premiums or Not? Insurance Companies

  • Life Insurance

    • Man, 40, smoker

    • Man, 40, non-smoker


Same premiums or not2
Same Premiums or Not? Insurance Companies

  • Pension

    • Man, age 65

    • Woman, age 65


Same premiums or not3
Same Premiums or Not? Insurance Companies

  • Life Insurance

    • Man, 30, father had Huntington’s disease

    • Man, 30, no family history of Huntington’s


Same premiums or not4
Same Premiums or Not? Insurance Companies

  • Life Insurance

    • Woman, 30, tested and has BRCA1 mutation

    • Woman, 30, never tested


Mathematical basis of insurance
Mathematical Basis of Insurance Insurance Companies

  • 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
Who Actually Buys Insurance? Insurance Companies

50%

50%

Combined

60%

Group 2

Group 1

“Die Young”

“Long Lived”

£2,000

£1,000

40%

£1,500


Who actually buys insurance1
Who Actually Buys Insurance? Insurance Companies

50%

50%

Combined

60%

Group 2

Group 1

“Die Young”

“Long Lived”

£2,000

£1,000

40%

£1,600


Two kinds of adverse selection
Two Kinds of Adverse Selection Insurance Companies

  • 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
Pooling of Risk Insurance Companies

50%

50%

Combined

Group 2

Group 1

“Die Young”

“Long Lived”

£2,000

£1,000

£1,500


Two basic economic questions
Two Basic Economic Questions Insurance Companies

  • If insurers do have genetic information:

    • People at higher risk might pay more

    • Question: howmuch more?

  • If insurers donot have genetic information:

    • People at higher risk might over-insure (adverse selection)

    • Question: howmuch would that cost?


Single gene disorders
Single-Gene Disorders Insurance Companies

Gene

Disease


Single gene disorders1
Single Gene Disorders Insurance Companies

  • 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
Very High Risk Insurance Companies

Probability of serious illness by age 60:

Average: 15%

APKD1 mutation carrier: 75%

Huntington’s mutation carrier: 100%


Multifactorial disorders
Multifactorial Disorders Insurance Companies

Smoking

Gene 2

Gene 1

Disease

Gene 6

Affluence

Diet

Gene 4

Gene 3

Gene 5


Multifactorial disorders1
Multifactorial Disorders Insurance Companies

  • 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
Genetic Tests: How Predictive? Insurance Companies

Single-gene disorders: STRONGLY

Multifactorial disorders: WEAKLY


An example of evidence apkd
An Example of Evidence: APKD Insurance Companies

  • Adult Polycystic Kidney Disease (APKD)

  • Leads to kidney failure and transplant

  • APKD1

    • Causes ~ 85% of APKD

  • APKD2

    • Causes ~ 15% of APKD

  • Epidemiologyexists


Ci extra premiums males
CI Extra Premiums (Males) Insurance Companies


Adverse selection costs ci
Adverse Selection Costs (CI) Insurance Companies

  • 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
Life Ins Extra Premiums (Males) Insurance Companies

No Transplants, Dialysis Only


Life ins extra premiums males1
Life Ins Extra Premiums (Males) Insurance Companies

Immediate Transplantation


Ci extra premiums males1
CI Extra Premiums (Males) Insurance Companies


Challenges to family history
Challenges to Family History Insurance Companies

  • 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 Tomorrow Insurance Companies

  • 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
Insurance Implications Insurance Companies

  • 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
Why Are Genes Special? Insurance Companies

  • 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 didnotknow which of Smith or Brown had a mutation, who would get special treatment?


The economic stakes involved in genetic testing for insurance companies1

The Economic Stakes Involved in Genetic Testing for Insurance Companies

Angus Macdonald

Heriot-Watt University, Edinburgh

and the Maxwell Institute for Mathematical Sciences


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