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3/19/2009
1
Understanding the Interactions Between Causes of Death – Mortality yProjections by Cause Research GroupAdrian Pinington, TSAP ConsultingScott Reid, Aon Benfield
23rd March 2009
Outline
IntroductionScenarios – flat and steep improvement trendsHeat ChartsHeat ChartsScenario funnel against Lee-Carter PercentilesCaveats and ConclusionsWhere Next
Mortality Projection by Cause Research GroupMembers
Harry Palmes Garth LaneAdrian Pinington Niel Daniels
Scott ReidRichard Morris Neil RobjohnsInput from others:
Dr R Croxson
Dr R Wyse
Dr A Luczak
Dr J Wilden
3/19/2009
2
Mortality Projections “By Cause” Research GroupTerms of Reference – Our Aims
To examine underlying trends in the factors influencing UK population mortality rates, and from these, to assess:
Historical overview of mortality by cause in the UK Review of literature on the key drivers of recent changes in mortality by cause in the UK, including research done by other professionsInvestigate and review data sources availableInvestigate and model past and future mortality improvements by
Introduction
cause. Investigate and model past and future mortality improvements by cause to sub-populations – smoker status, socio-economic categories and immigration patterns (with associated genetic and socio-cultural mortality differencesInvestigate and model the potential for mortality “shocks”, either short-term or via the advent of new diseases, by way of example, reduction in the incidence of heart attack due to the introduction of a smoking ban or obesity.
Formed in December 2007
Overview I
All Causes
Introduction
Overview II
All Causes
Cardiovascular
Cancer
Introduction
Other Causes
Respiratory
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3
Overview III
All Causes
Cardiovascular
Cancer
Other Causes
Forms of Chronic Ischaemic Heart Disease
Acute Myocardial Infarction
trachea, bronchus and lung
Other Cardiovascular
Introduction
Heart Failure
Aneurysm
Breast
Oesophagus
Uterus
Respiratory
Chronic obstructive pulmonary Disease
Pneumonia
prostate
Other Cancer
Other Respiratory Condition
Colon
Ovary
rectal
Stomach
Bladder
Pancreas
All causes, Male, Age 75-79Introduction
Age 75-79 - All Causes
800 00
1,000.00
1,200.00
1,400.00
1,600.00
Projection Observed
70-74 80-84
0.00
200.00
400.00
600.00
800.00
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
Source: ONS data using EASui Software (TSAP)
All causes, Male, Age 75-79Introduction
Age 75-79 All Cause
800.00
1,000.00
1,200.00
1,400.00
1,600.00
Other AllMentalNervousStrokeRespiratoryConditionCancer
-
200.00
400.00
600.00
800.00
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
CardiovascularObservedProjection
Source: ONS data using EASui Software (TSAP)
3/19/2009
4
All causes, Male, Age 75-79Introduction
Age 75-79 All Cause
50%
60%
70%
80%
90%
100%
0%
10%
20%
30%
40%
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055Cardiovascular Cancer RespiratoryConditionStroke MentalNervous Other All
Source: ONS data using EASui Software (TSAP)
Outline
IntroductionScenarios – flat and steep improvement trendsHeat ChartsHeat ChartsScenario funnel against Lee-Carter PercentilesCaveats and ConclusionsWhere Next
Scenarios – flat and steep
Explain terminology and techniqueComparison International mortality by causeIllustration approach for a few specific causesIllustration approach for a few specific causes
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5
Approach
By eye fit using the historical data as a guideFlat mortality improvement trendSteep mortality improvement trend
Scenarios – flat and steep
Carried out at group cause and sub-cause level for chosen conditions
Further medical input for scenarios still requiredVariation in Mortality rate by cause internationally
Variation in Mortality rate by cause internationally –Mortality Rates for Cardiovascular condition across 24 EU counties
Scenarios – flat and steep
France is 65% of UK
Bulgaria is 305% of UK
Source: WHO
Cardiovascular – Medical History
Myocardial Infarction has been the key driver of mortality improvement over the last 30 yearsThese improvements may well continue for some time to come – scenarios attempt to capture this uncertainty
Scenarios – flat and steep
come – scenarios attempt to capture this uncertaintyBut, Cardiovascular is a much smaller cause of death now, andIt will stop (reincarnations aside)
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Cardiovascular – Medical History
Medical input (Dr Croxson)Pollypill is not on market but clinical trials show this drug may reduce illnesses by more than 80%Expected to impact Heart Attack and “Forms of Ischaemic
Scenarios – flat and steep
Expected to impact Heart Attack and Forms of Ischaemic Heart Disease” equally
Smoking ban in UK is also expected to have a beneficial effect
Cardiovascular – Flat ScenarioScenarios – flat and steep
Age 75-79 Group Cause - Cardiovascular
400
500
600
700 Projection Observed
70-74 80-84
0
100
200
300
400
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
Source: ONS data using EASui Software (TSAP)
Cardiovascular – Steep ScenarioScenarios – flat and steep
Age 75-79 Group Cause - Cardiovascular
400
500
600
700Projection Observed
70-74 80-84
0
100
200
300
400
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
Source: ONS data using EASui Software (TSAP)
3/19/2009
7
Cardiovascular – Funnel of DoubtScenarios – flat and steep
Age 75-79 Group Cause - Cardiovascular
400
500
600
700Steep
Observed
Flat
0
100
200
300
400
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
Source: ONS data using EASui Software (TSAP)
Cardiovascular – All Ages (Flat)Scenarios – flat and steep
Cardiovascular Group Cause - All SubCause (M)
800
1,000
1,200
85plus
Observed
80-84
Observed
75-79
Observed
70-74
Observed
65-69
0
200
400
600
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
Observed
60-64
Observed
55-59
Observed
50-54
Observed
45-49
Observed
Source: ONS data using EASui Software (TSAP)
Cardiovascular – All Ages (Steep)Scenarios – flat and steep
Cardiovascular Group Cause - All SubCause (M)
800
1,000
1,200
85plus
Observed
80-84
Observed
75-79
Observed
70-74
Observed
65-69
0
200
400
600
1965 1975 1985 1995 2005 2015 2025 2035 2045 2055
65 69
Observed
60-64
Observed
55-59
Observed
50-54
Observed
45-49
Observed
Source: ONS data using EASui Software (TSAP)
3/19/2009
8
Outline
IntroductionScenarios – flat and steep improvement trendsHeat ChartsHeat ChartsScenario funnel against Lee-Carter PercentilesCaveats and ConclusionsWhere Next
Heat Charts
ApproachHeat Charts specific Group causes:
Cardiovascular Disease Breast and Lung CancerCardiovascular Disease, Breast and Lung CancerAggregate Flat Scenarios-
With Cardiovascular Disease, Breast and Lung cancer switched to steep
Observations
Heat Chart
Covers range of ages pictoriallyProjections against ActualSmoothing has been applied to Actual “Gaussian
Heat Charts
smoothing”Projected 20 years into the futureInvestigate the Heat Charts for group cause and sub-causeInvestigate the impact of scenarios on the aggregate level
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9
Heat Chart – Cardiovascular (m) - FlatHeat Charts
Diagonal to follow birth cohort(1930)
Calendar year across horizontal axis
Age on 1 5% b d
Colour scale
Age on vertical axis
Hot diagonal –exceptional
improvements
1.5% boundarycooler colours <
warmer colours >
Heat chart based on smoothed actuals (Gauss) – disregard
edges due to distortions
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Heat Chart – Cardiovascular (m) - Steep
Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Heat Chart – Cardiovascular(m)Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
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10
Aggregate Flat - MalesHeat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Aggregate Flat with Cardiovascular Steep (m)
Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Heat Chart – Breast Cancer (f) - FlatHeat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
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11
Heat Chart – Breast Cancer (f) - SteepHeat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Heat Chart – Breast Cancer (f)Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Aggregate Flat (f)Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
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12
Aggregate Flat with Breast Cancer Steep (f)
Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Heat Chart – Lung Cancer (m) - FlatHeat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Heat Chart – Lung Cancer (m) - SteepHeat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
3/19/2009
13
Heat Chart – Lung Cancer (m)Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Aggregate Flat - MalesHeat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
Aggregate Flat with Lung Cancer steep (m)
Heat Charts
Source: XL Re Heat chart tool, ONS data using EASui software (TSAP)
3/19/2009
14
Heat Charts - Observations
CardiovascularImprovements have some way to goBut diluted at aggregate level as becoming smaller cause and trend flattening
Scenario funnel against Lee-Carter Percentiles
trend flattening Breast Cancer
Screening has created improvements in mortality trendFuture improvements more at younger ages (50-60) rather than the older ages (60+)At Aggregate level impact small
Heat Charts - Observations
Lung CancerImpact of steep much more significant at younger ages.But steep scenario impacts all ages
Scenario funnel against Lee-Carter Percentiles
Females benefit slightly more compared to males –female smoking epidemic started later compared to males and therefore has further to go in steep scenarioAt aggregate level small impact
Outline
IntroductionScenarios – flat and steep improvement trendsHeat ChartsHeat ChartsScenario funnel against Lee-Carter PercentilesCaveats and ConclusionsWhere Next
3/19/2009
15
Background: Lee-Carter
Lee-Carter stochastic model Using ReMetrica software (Aon Benfield) for stochastic outputBasic model Calibrated using HMD from 1979 to 2006
Scenario funnel against Lee-Carter Percentiles
Calibrated using HMD from 1979 to 2006Used 20 intervals (for each year), 10000 trialsProjected in single ages but grouped post simulation
No jumps to allow for extreme mortality eventsi.e. Pandemics
UK – caveat – cohort effect is not captured in this model
Graphic of scenario funnel against Lee-Carter Percentiles, all cause mortality, age 75-79 males
Scenario funnel against Lee-Carter Percentiles
0.08
0.1
0.12
0
0.02
0.04
0.06
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
Source: ONS data, EASui software (TSAP) and ReMetrica software (Aon Benfield)
Graphic of scenario funnel against Lee-Carter Percentiles, all cause mortality, age 75-79 males
Scenario funnel against Lee-Carter Percentiles
0.08
0.1
0.1297.5 to 99.5
90 to 97.5
80 to 90
70 to 80
60 to 70
50 to 60
0
0.02
0.04
0.06
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
40 to 50
30 to 40
20 to 30
10 to 20
2.5 to 10
0.5 to 2.5
Median
Source: ONS data, EASui software (TSAP) and ReMetrica software (Aon Benfield)
3/19/2009
16
Graphic of scenario funnel against Lee-Carter Percentiles, all cause mortality, age 75-79 males
Scenario funnel against Lee-Carter Percentiles
0.08
0.1
0.12
0.08
0.1
0.1297.5 to 99.5
90 to 97.5
80 to 90
70 to 80
60 to 70
50 to 60
40 to 50
0
0.02
0.04
0.06
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
0
0.02
0.04
0.06
40 to 50
30 to 40
20 to 30
10 to 20
2.5 to 10
0.5 to 2.5
Median
Group_Steep
Group_Flat
Source: ONS data, EASui software (TSAP) and ReMetrica software (Aon Benfield)
Graphic of scenario funnel against Lee-Carter Percentiles, all cause mortality, age 75-79 males
Scenario funnel against Lee-Carter Percentiles
0.08
0.1
0.12
0.08
0.1
0.12 97.5 to 99.5
90 to 97.5
80 to 90
70 to 80
60 to 70
50 to 60
40 to 50
0
0.02
0.04
0.06
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
0
0.02
0.04
0.06
30 to 40
20 to 30
10 to 20
2.5 to 10
0.5 to 2.5
Median
Subgroup_Steep
Group_Steep
Subgroup_Flat
Group_Flat
Source: ONS data, EASui software (TSAP) and ReMetrica software (Aon Benfield)
Graphic of scenario funnel against Lee-Carter Percentiles, all cause mortality, age 75-79 males
Scenario funnel against Lee-Carter Percentiles
0.08
0.1
0.12
0.08
0.1
0.12 97.5 to 99.5
90 to 97.5
80 to 90
70 to 80
60 to 70
50 to 60
40 to 50
30 to 40
20 to 30
0
0.02
0.04
0.06
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
0
0.02
0.04
0.06
20 to 30
10 to 20
2.5 to 10
0.5 to 2.5
Median
Medium Cohort witha 1.5% underpinQiS4 Scenario
Subgroup_Steep
Group_Steep
Subgroup_Flat
Group_Flat
1 year shock or permanent ??
Source: ONS data, EASui software (TSAP) and ReMetrica software (Aon Benfield)
3/19/2009
17
Graphic of scenario funnel against Lee-Carter Percentiles, all cause mortality, age 60-64 males
Scenario funnel against Lee-Carter Percentiles
0.015
0.02
0.025
0.015
0.02
0.025 97.5 to 99.5
90 to 97.5
80 to 90
70 to 80
60 to 70
50 to 60
40 to 50
30 to 40
20 to 30
0
0.005
0.01
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
0
0.005
0.01
20 to 30
10 to 20
2.5 to 10
0.5 to 2.5
Median
Medium Cohort witha 1.5% underpinQiS4 Scenario
Subgroup_Steep
Group_Steep
Subgroup_Flat
Group_Flat
Source: ONS data, EASui software (TSAP) and ReMetrica software (Aon Benfield)
Observations
Age group 75-79Output is consistent with funnelsCohort effect is strong for this age group and the Lee-Carter variant allowing for cohort effect will be steeper
Scenario funnel against Lee-Carter Percentiles
variant allowing for cohort effect will be steeperAge group 60-64
Output is steeper compared to scenario funnelsLee-Carter will replicate historical data into future
Lee-Carter fans rapidly in first few years and then widens very gradually thereafter
Observations
QiS 4 is extremely conservative and significantly outside our funnel scenariosOur funnel scenarios are extreme as we are assuming
ll diti ith fl t t
Scenario funnel against Lee-Carter Percentiles
all conditions are either flat or steep“The longevity shock to be applied is a (permanent) 25% decrease in mortality rates for each age” (CEIOPS (2008))
Does permanent make sense in context of Solvency II ?Is it a 1 year shock or permanent ?
3/19/2009
18
Outline
IntroductionScenarios – flat and steep improvement trendsHeat ChartsHeat ChartsScenario funnel against Lee-Carter PercentilesCaveats and ConclusionsWhere Next
Caveats
Based on past extrapolation of dependent ratesCorrelations poorly understoodCause of death codification subject to changes
Conclusions
Cause of death codification subject to changes in practice and errorBase projection has no deliberate allowance for new medical advances accelerating pace of improvement....
Conclusions
Scenarios by cause give great insightReasonable feel for the extreme boundariesCan allow for medical input
Conclusion
pAllows lucid Communication of variability
Allows validation against stochastic outputGives a reasonable shape for future trends compared to short, medium and long with an under pin.
3/19/2009
19
Outline
IntroductionScenarios – flat and steep improvement trendsHeat ChartsHeat ChartsScenario funnel against Lee-Carter PercentilesCaveats and ConclusionsWhere Next
Where Next
Consolidation of research workPeer review within Actuarial ProfessionPublication of findingsPublication of findingsContinuing research and model refinementDecision maker education