So how do we build a grand unified
theory of health risks?
Peter Banthorpe
Head of Global Research and Development
Background and Context
2
(Re)Insurers have mixed portfolios of different Mortality and
Morbidity risks
Product
Consumer
pays
Life
Assurance
Regular
Premium
Insurer pays
Length
of
contract
Example
Customer
need
Large Lump sum on death
10-40 years
or Whole of
Life
To pay off a
mortgage
Critical Illness
Regular
Premium
Large lump sum on
diagnosis of one of a set of
medical conditions
10-40 years
To pay off a
mortgage
Income
Protection
Regular
Premium
Regular income while
policyholder can’t work
10-40 years
To maintain quality
of life during illness
Annuity
A large lump
sum
Regular income for life
From
retirement till
death
To provide a
guaranteed income
for life
Private
Medical
Insurance
Regular
premium
Annual
To provide faster
access to medical
care
Cost of medical treatments
3
Longevity Risk Transfer from Company pension schemes
to (Re)Insurers is the major growth area
Source: Aon Hewitt. UK Risk Settlement Market Update – November 2014
4
Insurance underwriting creates groups of similar risks
Underwriting
Process
•
•
•
Health and Lifestyle
Questions
GP records
Medical Exams
•
Decisions supported
by extensive
research
•
Guards against antiselection
Non-disclosure
issues
•
Standard Price/Risk
Non-Standard Price/Risk
Decline Cover
5
How do Insured Standard Mortality Rates compare to
population?
160%
Age Standardised % of Population
140%
120%
100%
80%
60%
40%
20%
0%
Life Assurance
Occupational
Pensioners
Lower
Overall
Purchased Annuities
Upper
UK Population
6
What do we charge someone who has suffered a heart
attack for Life assurance?
Depends on the relative risk of the individual
MI Severity Assessment






Current symptoms (Angina)
Impact on activity
Severity of MI
Extent of vessel disease
Ejection Fraction
Degree of Cardiomegaly
+50% RR
Demographics
Co-Morbidities
 Age
 Time Since Last Heart
Attack
 Diabetes, heart failure
etc
 Valvular Heart disease
 Arrhythmia
 Stress ECG results
 Lifestyle risk factors
Decline Cover
7
8
Detailed research producing a granular level of
understanding
Our underwriting manual has 100 different curves for cancer,
including 13 for breast cancer and 11 for prostate.
9
A “simple” multistate model to illustrate different products
and underwriting combinations
Stroke
Stroke and
Heart Attack
Healthy
Heart Attack
Adverse
Lifestyle risk
factors
Other disease
Other
disease
states
Other
disease
states
Other
disease
states
states
Death
Life assurance
Non-Standard Lives
could enter anywhere
in here
Standard
Lives Enter
Here
Stroke
Stroke and
Heart Attack
Healthy
Heart Attack
Adverse
Lifestyle risk
factors
Other disease
Other
disease
states
Other
disease
states
Other
disease
states
states
We pay out
here
Death
Critical Illness
Non-Standard Lives
could enter in here
Standard
Lives Enter
Here
Stroke
Stroke and
Heart Attack
Healthy
Heart Attack
Adverse
Lifestyle risk
factors
Other disease
Other
disease
states
Other
disease
states
Other
disease
states
states
We pay on
the first of
one of these
Death
Better understanding contributes to Wider Access, Fairness
of Pricing and Financial stability
 Access
 For as many people as possible, whatever the medical history is
 To new products
 To premium guarantees
 Fairness
 Price
 This is not a zero-sum game
 Financial Stability
 Ability to withstand a 1 in 200 year event
 Diversification benefits
 Ability to invest in UK plc.
13
Current Approaches
Pricing for the next year
14
Like any research we need relevant, high-quality data
UK
Insurers
own
data
Representative of
the insured
population:
• Bias to higher SE
groups;
• Smoker
segregated;
• Grouped
according to risk
at entry
International
Data
Publically
available
Government
data
Adequate for
standard lives in
existing products
and markets
Detailed
Care
Data
Quality
Accurate, granular, detailed, long-time series,
voluminous, recent
15
Analysis Techniques For Insured data will be familiar
• Traditional Actuarial Approach – calculate exposures and actual deaths
• Sufficient where small number of risk factors, and…
Uni-variate
• … risk factors are un-correlated
A/E
GLM A/E
Analysis
Survival
Analysis
• Extension of uni-variate approach;
• Copes with multiple risk factors, including correlated risk factors;
• Approach limited to lives weighted analyses
• Models time to death rather than counts of death
• Continuous time model rather than discrete time model
16
Deriving absolute risk estimates from non-insurance data is
difficult
Rates
• Obtain population incidence rates
• Possible sources: Medical Papers, National Databases
Errors
• Understand the data and correct for any known errors
• Population sources often include either double counting or under reporting
• Insured Definition probably won’t match medical definition;
Severity • Adjust for differences.
• Adjust for differences between Population and insured experience
Insured • In particular, socio-economic bias, self-selection and impact of underwriting
• Reduce costs if elements of the definition already covered elsewhere
Overlaps
17
Pricing Co-Morbidities is a challenge but very necessary
Proportion of applications with disclosures
from shown disclosures only
(78% of all applications in this case)
13%
Smoking
(Empty cells mean <0.5%)
Other CV
Risk
Factors
12%
29%
1%
2%
16%
17%
Coronary
Artery
Disease
Diabetes
2%
3%
2%
2%
18
Current Approaches
Pricing for the next 40 years
19
A history of underestimating life expectancy
Source: Fifty Years of United Kingdom national population projections: How accurate have they been? Chris
Shaw, Office for National Statistics (2007), © Crown copyright 2007
20
For pricing purposes we need a single best estimate view
of the future
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
2011
Current
Improvement Rates
2021
2031
Convergence
Period
2041
Long-run
improvement rate
For reserving and some transactions we need Stochastic Methods
21
No one wants to drive looking only in their rear view mirrior
22
Building All-Cause mortality projections by cause
Other Circulatory
Heart Disease
Year
Age
21 - 25
81 - 85 86 - 90 91 - 95 96 - 00 01 - 05
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≥
-100.0%
-3.5%
-2.5%
-1.5%
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0.5%
1.5%
2.5%
3.5%
4.5%
6.5%
<
-3.5%
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4.5%
6.5%
100.0%
Colour
13
13
11
11
5
5
33
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4
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43
43
6
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44
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Source RGA analysis of ONS data
23
Models like this are getting towards what we would like….
24
Lewsey J D et al. Heart 2015;101:201-208
Factoring in global context: Global IHD Trends
Source: RGA calculations based on WHO Mortality Database
25
Factoring in global context: Global Cancer Trends
Source: RGA calculations based on WHO Mortality Database
26
Cancer deaths will be eliminated for all under 80 by
2050, new research predicts1
“It is realistic to expect that by 2050 nearly
all cancer related deaths in children and
adults aged up to (say) 80 years will have
become preventable through life style
changes and because of the availability of
protective technologies and better
pharmaceutical and other therapies.”(2)
1: The Independent. Accessed online: 16 January 2015
2: UCL School of Pharmacy: Overcoming Cancer in the 21st Century (14 January 2015)
27
Quantifying Socio-economic Differences in trend is a very
active area of research
28
So models like IMPACTSEC are very useful…
…if only we had something similar for Cancer
Bajekal M, Scholes S, Love H, Hawkins N, O'Flaherty M, et al. (2012) Analysing Recent Socioeconomic Trends in Coronary Heart
Disease Mortality in England, 2000–2007: A Population Modelling Study. PLoS Med 9(6): e1001237.
29
doi:10.1371/journal.pmed.1001237
In conclusion…
30
We are interested in most things, including lots of things
that haven’t been mentioned so far….







Constant Health Monitoring / Quantified Self / Wearables
Genetics
Wellbeing / Biopsychosocial
Maximum Lifespan Changes
Regenerative Medicine
Healthy Ageing
Antimicrobial resistance
31
So as a real conclusion….
 Understanding the past is essential
 But we rarely extrapolate the past without adjustment
 To adjust for the future requires detailed explanatory modelling
 Emerging data and modelling techniques hold promise for more
accurate pricing of non-standard risks
 Key areas of research interest are:





Models which clearly link morbidity and mortality
Socio-economic variations in trends
The future incidence and mortality from Cancer
Co-morbid mortality
Excess mortality as a function of age and duration
32
Thank you for your attention.
33