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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. 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

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  1. The Economic Stakes Involved in Genetic Testing for Insurance Companies Angus Macdonald Heriot-Watt University, Edinburgh and the Maxwell Institute for Mathematical Sciences

  2. Outline • Fundamental questions • Problems posed by genetic testing • Seeking evidence from data • Examples • Conclusions

  3. Same Premiums or Not? • Motor Insurance • 40-year old, no accidents, family car • 17-year old, no experience, sports car

  4. Same Premiums or Not? • Life Insurance • Man, 40, smoker • Man, 40, non-smoker

  5. Same Premiums or Not? • Pension • Man, age 65 • Woman, age 65

  6. Same Premiums or Not? • Life Insurance • Man, 30, father had Huntington’s disease • Man, 30, no family history of Huntington’s

  7. Same Premiums or Not? • Life Insurance • Woman, 30, tested and has BRCA1 mutation • Woman, 30, never tested

  8. 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

  9. Who Actually Buys Insurance? 50% 50% Combined 60% Group 2 Group 1 “Die Young” “Long Lived” £2,000 £1,000 40% £1,500

  10. Who Actually Buys Insurance? 50% 50% Combined 60% Group 2 Group 1 “Die Young” “Long Lived” £2,000 £1,000 40% £1,600

  11. 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 (?)

  12. Pooling of Risk 50% 50% Combined Group 2 Group 1 “Die Young” “Long Lived” £2,000 £1,000 £1,500

  13. Two Basic Economic Questions • 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?

  14. Single-Gene Disorders Gene Disease

  15. 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

  16. Very High Risk Probability of serious illness by age 60: Average: 15% APKD1 mutation carrier: 75% Huntington’s mutation carrier: 100%

  17. Multifactorial Disorders Smoking Gene 2 Gene 1 Disease Gene 6 Affluence Diet Gene 4 Gene 3 Gene 5

  18. 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)

  19. Genetic Tests: How Predictive? Single-gene disorders: STRONGLY Multifactorial disorders: WEAKLY

  20. 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 • Epidemiologyexists

  21. CI Extra Premiums (Males)

  22. 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)

  23. Life Ins Extra Premiums (Males) No Transplants, Dialysis Only

  24. Life Ins Extra Premiums (Males) Immediate Transplantation

  25. CI Extra Premiums (Males)

  26. 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?

  27. 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

  28. 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”.

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

  30. 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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