Genetics and Underwriting in Health Insurance

Transcription

1 Genetics and Underwriting in Health Insurance Angus Macdonald Heriot-Watt University, Edinburgh

2 Outline Genetic epidemiology Example 1 Alzheimer s disease Example 2 Coronary heart disease

3 Single-Gene Disorders Extra morbidity and mortality - high Age at onset or death - often much younger than average, with high probability Comparatively rare - about 1% of births Many sufferers will not survive to insuring ages Probably a strong family history

4 Multifactorial Disorders Associated with common causes of death May occur in healthy lives May be a family history May interact with environmental factors Extra mortality - variable, often quite low Age at death/illness - not known with much greater probability

5 What Do Genetic Epidemiologists Study? Penetrance: p(x) = the probability that a given genotype will cause disease by age x Mutation frequency in the population Survival after onset of disease Progression or stages of disease Gene-environment interactions

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7 Ascertainment Bias A significant feature is ascertainment bias Search the world for unusual families with many affected members in several generations Estimate rates of onset based on affected members of these families Result: Penetrance estimates may be greatly overstated

8 Multifactorial Epidemiology Molecular epidemiology find out which genes are switched on during specific biomedical processes Population epidemiology find out which combinations of genetic variants and environment influence disease

9 UK Biobank Recruit 500,000 healthy people age Obtain DNA Obtain data on environmental exposures Follow up via doctors, hospitals and disease registers Use as source for large scale case-control studies from 5 years onwards

10 UK Biobank Value for Money? Cost of data collection alone ~ 40 million Medical Research Council, Department of Health and Wellcome Trust

11 UK Biobank Value for Money? Cost of data collection alone ~ 40 million Medical Research Council, Department of Health and Wellcome Trust Power to detect associations? 50% of population with interesting genotype and interesting exposure 4 controls per case 3,600 cases needed to detect 30% extra risk

12 UK Biobank Value for Money? Cost of data collection alone ~ 40 million Medical Research Council, Department of Health and Wellcome Trust Power to detect associations? 10% of population with interesting genotype and interesting exposure 4 controls per case 12,600 cases needed to detect 60% extra risk

13 Pharmacogenomics (1) Use molecular medicine to develop new drugs (2) Use genetic profiling to target specific drugs to persons who will respond well Unknown impact on: Drugs costs, in public and private medicine Rationing of expensive medicine Affordability of pooled medical costs

14 Alzheimer s Disease Alzheimer s Disease progressive dementia onset mostly at ages > 65 accounts for a significant part of LTC costs no known cure or effective treatment

15 Alzheimer s and the ApoE Gene The ApoE gene has 3 alleles: e2, e3, e4 e4 allele predisposes to earlier onset of AD e4/e4 about 2% of the population highest risk - up to x, males and females e3/e4 about 20% of the population females at up to 4-5 x normal risk

16 Learning About ApoE Early 1980s three variants of the ApoE protein identified 1980s e4 allele linked to heart disease 1991 e4 allele linked to Alzheimer s disease 1997 Age-dependent risk estimates published

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18 A Model of AD and Care Costs Healthy Onset of AD In Institution Dead

19 A Model of AD and Care Costs e2/e2, e2/e3 e3/e3 e2/e4 e3/e4 e4/e4

20 Features of the Model The normal level of insurance The extent of genetic testing The probability of a positive result The behaviour of adverse selectors The behaviour of insurers The amount and incidence of medical costs The amount and incidence of care costs

21 LTC Insurance Policies LTC policies pay out while suffering: typically, loss of 3 or more Activities of Daily Living (ADLs) ; or cognitive impairment, such as AD Payments are usually linked to an index Fewer than 30,000 policies sold in the UK AD accounts for 1/2-1/3 of costs (USA)

22 A Model of AD and Care Costs Healthy Onset of AD In Institution Dead

23 Extra Premiums? Female age 60 Proportion of Relative Risk e4/e4 e3/e4 e2/e4 % % %

24 Population Risk: Parameter m Rates of onset based on case-control studies Expect strong selection bias, lower genetic risk in a population sample Modelled by reducing excess e4 onset rate by 50% (m=0.5) by 75% (m=0.25) Delphic estimates may be even lower

25 Extra Premiums? Female age 60 Proportion of Relative Risk e4/e4 e3/e4 e2/e4 % % %

26 Long-Term Care Costs of AD Holmes et al. (1998) model: 41,794 p.a. PLUS p.a, for each year since onset MINUS p.a., for each year of age PLUS 17,840 p.a., if in an institution Includes costs of unpaid care

27 AD Costs at Age 60 (,000) e2/e3 e3/e3 e2/e4 e3/e4 e4/e4 Ave M F

28 Population Risk: m=0.25 e2/e3 e3/e3 e2/e4 e3/e4 e4/e4 Ave M F

29 Combined Pension and LTC? AD has opposite effects on pensions and LTC: Increases LTC costs Decreases pension costs Pension/LTC (AD) benefit in ratio 1:3 Average pension = 3,200 p.a. Average LTC benefit = 9,600 p.a.

30 Extra Premiums for Combined Pension + LTC? Female age 60 Prop n of RR e4/e4 e3/e4 e2/e4 % % % LTC only LTC+Pen

31 Conclusions e4/e4 as a separate risk group? Timescale of ~10 years between discovery and reliable assessment Great uncertainty about results: Recommend continuing research: Development of actuarial models More collaboration/interdisciplinary work

32 Coronary Heart Disease (CHD) Major common cause of death Caused by fatty deposits in coronary arteries Outcome myocardial infarction (MI, meaning heart attack) Genetic component is multifactorial

33 CHD Lifestyle risk factors: Sex, smoking, body mass index (BMI) Clinical risk factors: Hypertension Hypercholesterolaemia Diabetes These are also risk factors for stroke

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35 Examples of Extra Premiums Model from Macdonald, Waters & Wekwete (2004) based on Framingham study Compare a mutation that increases the intensity of: Progression through a clinical risk factor MI or stroke directly

36 Examples of Extra Premiums Male, 35, N-S, Normal BMI, 10-year term 5 x risk of Hypertension Hypercholesterolaimia Diabetes type 1 Diabetes type 2 CHD (directly) Stroke (directly) Extra Premium 14% 6% 2% 5% 192% 30%

37 Examples of Extra Premiums Male, 35, N-S, Normal BMI, Severe Hypertension, 10-year term 5 x risk of Hypercholesterolaemia Diabetes type 1 Diabetes type 2 CHD (directly) Stroke (directly) Extra Premium 97% (basic) 113% 99% 105% 597% 203%

38 Conclusion Genotypes that confer high additional risks of risk factors in complex disorders do not imply large additional risks of the disease endpoints The genetic contributions to complex disorders will mostly act in this way Important message for public debate

39 What is the Purpose of Research? NOT to allow more discrimination Main purpose is to obtain quantitative information to: inform policymakers; identify potential problems; allay unnecessary fears; and help to achieve fairness in provision

40 Genetics and Underwriting in Health Insurance Angus Macdonald Heriot-Watt University, Edinburgh