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 u Fundamental questions u Problems posed by genetic testing u Seeking evidence from data u Examples u Conclusions

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

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

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

6. Same Premiums or Not? u Life Insurance –Man, 30, father had Huntingtons disease –Man, 30, no family history of Huntingtons

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

8. Mathematical Basis of Insurance u All these examples rest on the same principles u Insurance has a mathematical basis –Imperfect, fuzzy –Judgement not excluded u Arbitrary pricing MAY, SOMETIMES, damage the system

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

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

11. Two Kinds of Adverse Selection u Insurers gaming against each other –Smoker/Non-Smoker differentials –Male/female differentials (?) u Applicants not disclosing information –AIDS (USA) –Mortgage life insurance (UK) –Genetic information (?)

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

13. Two Basic Economic Questions u If insurers do have genetic information: –People at higher risk might pay more –Question: how much more? u If insurers do not have genetic information: –People at higher risk might over-insure (adverse selection) –Question: how much would that cost?

14. Single-Gene Disorders Gene Disease

15. Single Gene Disorders u Can present high risk of disease/death u Can have late onset u Treatment drastic or non-existent u Rare u Known about - epidemiology exists u Can present clear pattern in family history u Family history risk already underwritten

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

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

18. Multifactorial Disorders u Common diseases (cancer, heart disease) u Complex interactions –Many variants of many genes –Environment u Altered susceptibility, not very high risk u Pattern of inheritance unclear u Not much epidemiology (yet)

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

20. An Example of Evidence: APKD u Adult Polycystic Kidney Disease (APKD) u Leads to kidney failure and transplant u APKD1 –Causes ~ 85% of APKD u APKD2 –Causes ~ 15% of APKD u Epidemiology exists

21. CI Extra Premiums (Males)

22. Adverse Selection Costs (CI) u Premium increases to cover cost u Under extreme assumptions: –Ban on all test results0.44% –Ban on adverse test results0.32% –Ban on family history (1) Cost of broader risk pool0.35% (2) Cost of adverse selection1.25% (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 u Heterogeneity means that an adverse test is not always worse that family history u If family history is uninsurable, is there an implied requirement to be tested? u If treatment normalizes risk, is there an implied requirement to be treated?

27. Genetics of Tomorrow u Genetics of common diseases u Gene-gene, gene-environment interactions u Whole-genome scans, genetic arrays u Large-scale population studies u Novel mechanisms (epigenetics, RNA interference) u Genetic therapy

28. Insurance Implications u High-throughput genetic arrays will reveal much about complex genetic influences on biological processes – but this is not the same as disease. u Understanding biological processes better will help to understand disease – but this is not the same as epidemiology. u 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? u Probability of dying before age 60? u Mr Smith and Mr Brown –One is a mutation carrier: 20% –One has had a serious illness: 20% u If you did not know 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