MIRA Risk Review: Multivariate metabolic risk calculator

Excessive body weight is a known cardiovascular risk factor – but what about
overweight individuals with normal blood pressure? Should they pay lower
premiums? How accurate are the insurance industry’s ratings for people with
an exceptionally high Body Mass Index (BMI) or cholesterol count? What
impact does a high blood glucose have on the mortality and morbidity of obese
applicants? And to what extent do high BMI loadings differ between countries
with higher average levels of obesity and those in which overweight individuals
are rare?
These are some of the questions we asked in our research for the multivariate
metabolic risk calculator, based on the fact that the complexity of the inter
action of cardiovascular risk factors clearly exceeds the scope of conventional
rating tables, which consider each disorder and its influence in isolation.
Understanding the condition
The influences of excessive weight, high blood pressure, elevated cholesterol
and elevated blood glucose on CVD risk and hence morbidity and mortality are
not simply additive, but rather are very closely correlative and interactive. In
response, we have completely revised the MIRA general calculator based on
data from more than 1.5 million insurance applicants. The multivariate meta
bolic risk calculator is specifically adjusted to each market and simultaneously
considers all risk factors including statistical interrelationships between them.
This central tool within MIRA is designed for maximum accuracy, greater secu
rity and user-friendliness. It enables you to assess risks more precisely than
ever, in many cases resulting in more advantageous ratings, e.g. in overweight
applicants with normal blood pressure. The following pages offer a closer look
at the methodology behind the calculator followed by a summary of each risk
factor.
The ultivariate metabolic risk calculator
draws on 1,500,000 applications and is
adapted to different markets.
Worldwide, cardiovascular diseases (CVDs) are among the most
widespread causes of death. They also play an important role in
morbidity and disability. Providing unprecedented detail and
precision, the highly sophisticated multivariate metabolic risk
calculator looks at all four main cardiovascular risk factors –
excessive weight, blood pressure, blood lipids and blood glucose–
as well as their correlation and interaction.
MIRA RISK REVIEW
The multivariate metabolic risk calculator
New methodology allows unprecedented
accuracy

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The multivariate metabolic
risk calculator
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Contents
Understanding the condition 1
Groundbreaking methodology 3
Build: Body Mass Index 7
Measuring build 8
Relevance for life insurance 8
Relevance for disability insurance 10
Relevance for critical illness insurance 11
Relevance for long term care insurance 11
Is the situation different in Asia? 11
Blood pressure 12
Lipids 15
Blood glucose 18
Understanding the condition 18
Risk assessment for abnormal blood sugar 20
Benefits 24
Contact 25
Literature 26

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Groundbreaking methodology
The many qualities contributing to the unique methodology behind the multi
variate metabolic risk calculator (MRC) begin with its sheer volume of data: an
unprecedented cohort of more than 1.5 million applicants in the United States
were observed over a period of ten years in order to create the MRC database.
The USA was chosen based on both the availability of consistent data and the
diversity of the country’s population. We gathered information on the three fac
tors, BMI (height, weight), blood pressure and lipids followed later with the
inclusion of a fourth factor - blood glucose. Throughout the development, com
parisons were made between our data and evidence from other countries, in
particular various Asian populations, to ensure that the risk information
excludes any form of ethnic bias thereby making the rates applicable for all
countries.
Figure 1: Milestones in developing the multivariate metabolic risk calculator
for MIRA
Notwithstanding the accuracy of conventional individual loading tables for
BMI, blood pressure and lipids, assessing the role of the complex correlations
and interactions between these factors has always posed a challenge. To over
come this and to improve upon the traditional one-dimensional view of each
isolated result, we introduced mechanisms to reveal how the different factors
are related and how they affect one another. The MRC replaces the BMI, blood
pressure and lipids loading tables within MIRA to achieve a new level of detail
and precision.
The multivariate metabolic risk calculator
uses data from a larger cohort than ever
seen before.
1,5 million applications
10 years of observation
Multivariate statistical
models (GLMs)
Linked to death register
Distilling the risk essence
for MIRA
Benchmarked against
international studies
Interfacing the calculator
into MIRA
The MRC replaces individual BMI, blood
pressure or lipid loading tables.

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Data was captured not only on finalised applications, but also on individuals
whose applications did not proceed. These include applications rejected by the
insurer as well as those in which the applicant declined to finalise the policy,
presumably due to higher premiums, cancelled or switched to a different cover.
By referring to national death records, it was possible to identify the date of
death of deceased applicants regardless of whether or not they were policy
holders. This ensured a realistic share of outliers within the database – a
significant shortcoming in conventional studies. Examples of extremes con
tained in the database include more than 150,000 applicants over the age of
60 as well as tens of thousands of individuals with exceptionally high readings
for individual risk factors, such as a BMI of greater than 40, cholesterol exceed
ing 300 mg/dl (7.8 mmol/l) or blood pressure over 180/110 mmHg.
At the same time, since all data was drawn from applicants, the database
realistically reflects the life insurance target group. By comparison, a random
cross-section of the population would include a significant share of people who
are unable to take out life insurance or ineligible, which would skew results
negatively.
Figure 2: Size of Munich Re database compared to renowned landmark
epidemiologic studies
Munich Re’s dataset on cardiovascular risk factors is of outstanding size even compared to
the most renowned studies worldwide.
The MRC uses rich, high-quality data from
insurance applicants.
Studies compared are: Framingham
Study (US), PROCAM (= Prospective
Cardiovascular Münster Study, Germany),
SCORE (= Systematic Coronary Risk
Evaluation, EU), MRFIT (Multiple Risk
Factor Intervention Trial, US), PSC
(Prospective Study Collaboration, US
and EU).
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
Framingham
(US)
PROCAM
(D)
SCORE
(EU)
MRFIT
(US)
PSC
(57 studies
from US
and EU)
Munich Re
5,000
50,000
250,000
360,000
900,000
1,500,000

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Figure 4: Risk profiles for BMI and cholesterol
These 3-D graphs show the simultaneous – i.e. multivariate – influence of BMI and systolic
blood pressure, respectively of BMI and total cholesterol, on mortality risk.
The coloured layers represent different risk levels, where blue depicts lowest and red
highest values.
Figure 3: Risk profile for BMI and systolic BP
Systolic BP BMI
Male 18–40 years, non-smoker
Very high risk
High risk
Medium risk
Standard/low risk
Source: Munich Re
Cholesterol BMI
Risk
Risk

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Figure 5: Analysing application data delivers more reliable results
Along the way from application to policy, a significant amount of data is lost, more specifi
cally, substandard risks which are of particular interest for underwriting rules and ratings.
By using application data in conjunction with public mortality registries, we therefore get
the most reliable ratings. In contrast, normal portfolio analysis only covers those applicants
which have materialised by becoming insureds.
In simultaneously considering the different variables and how they interact on
mortality or morbidity risks, it was also important to account for different levels
of disclosure encountered in typical underwriting. For this reason, a separate
model was developed for each possible data constellation: one model for cases
in which BMI only was known, another for BMI and blood pressure, a third for
BMI and lipids, a fourth for BMI, blood pressure and lipids and a fifth model for
BMI and blood glucose. In addition, the five models were applied within each of
four different age groups (40; 40–59; 60–69; 70+).
As a result, 20 models (five data constellations multiplied by four age groups)
run in the background of the MRC to automatically calculate rating recommen
dations for any given application. This sophisticated system of multiple sets of
variables – termed multivariate – does not, however, increase complexity for
the user: the familiar MIRA interface remains in place, the only difference
being that individual BMI, blood pressure or lipid loading tables are no longer
used. Instead, the total loading depends on the combination of variables given
on application. In addition, there is now a choice between the rounded total
ratings previously given in MIRA and incrementally variable exact ratings.
Applications
All applications are analysed
“Normal” portfolio analysis
Lost data – only insureds are analysed
Munich Re database Public
mortality data
Applications
Client rejects
loading
Declined by
insurer
Not taken up
Insureds
A total of 20 multivariate models allows
detailed assessment of correlation and
interaction.

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The importance of high volume and reliability of data in the development of the
MRC cannot be overemphasised. For this reason, the MRC database draws on
the US market, where insurance-relevant statistics are available in unpar-
alleled range and quality. In addition, it would in no other country have been
possible to cross-check market information against official death records.
The main data source was a large and representative pooled insurance data
base containing medical application details. Secondary sources of information
included medical and insurance articles, which were used as a basis for com
parison. As risk and frequency are considered separately, portfolio risk within
another market – for example, in Asia, where high BMI is less frequent than in
the USA – can be assessed accurately. According to our findings, the risk rela
tionship between different BMI, cholesterol or blood pressure levels always has
a comparable magnitude independent of ethnics and gender. What differs
between these groups are the absolute risk levels and frequencies of abnormal
values. As we separate relative risk from other parameters in our models, the
results can be transferred to all countries. We only have to add local frequency
information and local normal ranges to the market-specific versions of the
calculator.
The mortality rates from the database were derived from the database itself,
which means expected mortality was calculated internally. This ensures a
higher precision of the rates and a more genuine restitution of the behaviour of
the risk factors on mortality. The more common actuarial approach would have
been to compare the database externally to mortality tables, but this approach
would have skewed the view of interactions within the database. In other
words, using an internal reference enables modelling of the finest interactions
between risk factors, bringing the calculator to the peak of current medical
knowledge.
The MRC was developed primarily with generalised linear models (GLMs) to
create a multivariate state-of-the-art tool that combines precision with numer
ous entry fields, including current values, previous values and many different
convenient functionalities (radio buttons, drop-down lists, etc.). It has under
gone a comprehensive testing process which encompassed several phases. All
formulas have been programmed twice by two independent specialists to
exclude systematic errors. All outputs have been checked by extensive graphic
testing routines.
Build: Body Mass Index
The WHO estimates that worldwide obesity has nearly doubled since the
1980s. In 2008, approximately one third of the adult population was over
weight, with a tenth qualifying as obese1. Statistics vary widely between
different countries: in high income countries, the prevalence is higher in lower
socioeconomic groups, whereas this relationship is less clear and constantly
changing in transitional countries. Prevalence also varies between men and
women and according to age, level of affluence and region.
Insurance applicants generally represent a population of higher socioeconomic
standing and better education with correspondingly lower prevalence of exces
sive weight than in the general population. This relationship may be less clear
in emerging economies.
The US insurance market provides highest
quality statistics which have been proven
to be transferable to other markets too.
By taking the reference risk from the
database itself, the best possible precision
has been ensured.
Rating functions and outputs were
extensively tested for technical and
medical accuracy.
BMI statistics vary between regions,
insurance applicants representing a
favourable selection.

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BMI is the most widely accepted measure for classifying build in adults
(Figure 6). It is calculated by dividing the individual’s weight in kilograms
by the square of height in metres (kg/m2).
Figure 6: The international classification of adult underweight, overweight
and obesity according to BMI, adapted from WHO 2004
Excessive weight and obesity are established risk factors for mortality and
many chronic diseases such as cardiovascular diseases, diabetes, musculo
skeletal disorders and cancer. Large cohort scientific studies2, 3, 4 show a clear
association between BMI and mortality, with the lowest mortality around
BMI 20–25. Berrington et al. conducted a prospective cohort study on circa
1.5 million non-Hispanic Caucasian adults aged 19–84 years and observed
them for ten years (Figure 7). An analysis of 57 prospective studies, the Pro
spective Studies Collaboration, observed 900,000 participants mainly from
Europe and the USA for 13 years. In Asia, similar results were observed in large
well conducted cohort studies of 1.2 million Koreans14 and 142,000 Chinese16,
followed-up for 12–15 years.
Measuring build: BMI – weight divided by
height squared (kg/m2).
Classification BMI (kg/m2)
Principal cut-off points Additional cut-off points
Underweight 18.50 18.50
Severe thinness 16.00 16.00
Moderate thinness 16.00 – 16.99 16.00 – 16.99
Mild thinness 17.00 – 18.49 17.00 – 18.49
Normal range 18.50 – 24.99 18.50 – 22.99
23.00 – 24.99
Overweight ≥ 25.00 ≥ 25.00
Pre-obese 25.00 – 29.99 25.00 – 27.49
27.50 – 29.99
Obese ≥ 30.00 ≥ 30.00
Obese class I 30.00 – 34.99 30.00 – 32.49
32.50 – 34.99
Obese class II 35.00 – 39.99 35.00 – 37.49
37.50 – 39.99
Obese class III ≥ 40.00 ≥ 40.00
Build as an independent risk factor: large
cohort studies link BMI with mortality.

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Healthy non-smokers have a higher relative mortality risk if overweight than all subjects
(including smokers and persons with disease). Their absolute mortality risk is much lower
and therefore the relative risk increase from overweight is much steeper.
Hazard ratio
Healthy subjects who never smoked
All subjects
Figure 7: Adjusted hazard ratios for death from any cause according to
BMI for all study participants and for healthy subjects who never smoked
(no cancer or heart disease at baseline)
A Caucasian women
Source: 3 Body Mass Index
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0 15.0 17.5 20.0 22.5 25.0 27.5 30.0 32.5 35.0 37.5 40.0 42.5 45.0
2.02
1.34
1.06 1.00
1.03
1.11
1.25
1.58
1.99
1.47
1.14
1.00 1.00
1.09
1.19
1.44
1.88
2.51
B Caucasian men
Hazard ratio
Healthy subjects who never smoked
All subjects
Source: 3 Body Mass Index
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0 15.0 17.5 20.0 22.5 25.0 27.5 30.0 32.5 35.0 37.5 40.0 42.5 45.0
1.98
1.6
1.18
1.00
0.97 1.03
1.16
1.44
1.93
1.37
1.01 1.00 1.00
1.06
1.21
1.44
2.06
2.93

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In non-smokers without underlying disease, mortality increases 1.3-fold for
each 5-unit BMI increase above BMI 253, 4 and for each 5-unit BMI decrease
below BMI 254. Studies consistently show lower relative mortality in older age
groups and among smokers (Figure 7), which can be explained by higher base
line mortality rates in both of these groups. The associations between BMI and
mortality are broadly similar in men and women3.
Mortality is also increased in individuals with low BMI. This finding to a large
extent reflects the fact that low BMI is frequently linked to weight loss due to
pre-existing disease such as cancer, eating disorders or chronic lung disease.
This effect is more visible in smokers. Accordingly, Figure 7 shows lower
mortality risks at low BMIs in individuals who have never smoked. In our multi
variate metabolic risk calculator, we have accounted for the fact that a certain
proportion of these underlying diseases can be detected by medical underwrit
ing. Ratings for low BMI are hence lower than suggested by overall mortality
statistics.
Waist circumference (WC) as a measure for abdominal obesity is sometimes
suggested as an additional measure. The overall associations between BMI
and WC in relation to mortality and morbidity show a similar pattern. And
correlation between WC and BMI is high5, 6. Application forms rarely contain
information on WC, as it requires a physical examination. According to Pischon
et al.5, WC is mainly relevant in the normal to overweight range, i.e. BMI 18 to
30, where insurance guidelines do not apply ratings. In higher BMI regions,
above 30, a large WC is so common that its additional risk information is
negligible. Interestingly, as is the case with low BMI, mortality increases with
low WC values. In light of this, WC has been removed as a data point in our
calculator.
Many of the disorders associated with excessive weight negatively affect the
ability to work. Hypertension, diabetic metabolic status and an adverse lipid
profile increase with increasing BMI4. In turn, the incidence of myocardial
infarction, stroke and diabetes rises. Excess body weight also leads to an
increased incidence of renal failure (kidney failure), gall bladder and fatty liver
disease4. The World Cancer Research Fund reported associations between
elevated BMI and cancers of the oesophagus, pancreas and colon-rectum,
gynaecological tumours and possibly cancer of the gall bladder7. Elevated body
weight puts a great deal of pressure on the back and joints, resulting in osteo
arthritis, joint and back pain. It also constitutes one of the main risk factors for
obstructive sleep apnoea and has been discussed in the context of anxiety and
depression. All of these conditions in turn reduce the ability to work.
Accordingly, overweight and obese persons are more likely to become disabled
than individuals with a normal BMI. A large Swedish cohort found a threefold
increase in the risk of receiving a disability pension in persons with severe
obesity (BMI ≥ 35) compared with persons of normal weight9. Notably, excess
body weight has an even higher impact on disability than on mortality with the
risk of becoming disabled already clearly increasing at moderately elevated
BMIs50 (Figure 8). Much of this increase can be attributed to musculoskeletal
disorders, due to the above-mentioned pressure on the joints and the back.
Many underlying diseases which cause
both underweight and related mortality
are detected by medical underwriting.
For disability insurance, the implications of
BMI are complex.
High BMI is associated with many
conditions that cause disability.

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Figure 8: BMI-related mortality compared to disability
Disability increases with increasing body weight. Impact of BMI on disability is even higher
than on mortality.
Musculoskeletal and circulatory disorders are among the main triggers of disa
bility pension and have been shown to increase in frequency by factors of 1.5
and 2, respectively, in overweight individuals and 2 and 3.5 in obese persons9.
Disability pensions for conditions such as injuries, nervous system and
tumours are also more frequent in the obese9.
Excessive weight and obesity lead to an increased incidence of diabetes,
vascular disease and cancer4. Cancer and vascular diseases are the leading
critical illness (CI) triggers (related covers: cancer, myocardial infarction, coro
nary artery bypass surgery and stroke) and account for the vast majority of
claims. Complications of diabetes account for further CI-covered conditions
such as nephropathy (related cover: kidney failure) and retinopathy (related
cover: blindness).
Conditions that are relevant for disability insurance also play a major role for
long term care insurance. Stroke, neurological disorders, musculoskeletal
disease, cardiovascular disease, cancer, and complications of diabetes may
all lead to long term care disability and are more prevalent in overweight and
obese persons. Disability based on the activities of daily living increases with
increasing BMI10, 11, 12.
Excessive weight and obesity are less prevalent in Asia than in Western coun
tries. However, significant lifestyle changes in many Asian populations over
recent decades have led to increasing levels of weight and obesity. As this is a
recent development, long-term consequences have not yet been experienced to
the full extent and cannot be easily foreseen. In contrast to Western populations,
excess body fat is often – but not always – associated with better socioeconomic
status, living conditions and access to health services, hampering direct
Rate/1,000 person years
Age-adjusted rates of
mortality in women
mortality in men
incapacity for work in women
incapacity for work in men
16
14
12
10
8
6
4
2
0
22.5 22.5 25.0 27.5 30.0 32.5
Source: 50 Body Mass Index (kg/m2)
Critical illness insurance: high BMI is
linked to multiple triggers.
Many conditions leading to long term care
are increased in overweight persons.
Is the situation different in Asia?

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comparisons between Western and Asian countries. Moreover, Asians have a
different fat distribution to Europeans. With higher percentages of body fat at a
given BMI and higher risks of type 2 diabetes, hypertension and hyperlipidaemia
have been described at relatively low BMI levels. As this relationship differs
among different Asian populations, the WHO sees insufficient evidence to
support one different BMI cut-off point for Asian populations13. It is not clear
whether, in the long run, waist circumference as a measure for abdominal
obesity may prove to be a better measure of overweight and obesity in Asian
populations.
Large prospective cohort studies have been conducted in Asian populations
which all report similar associations between BMI and mortality in Asian and
European cohorts14, 16, supporting the view that the same overweight and
obesity BMI cut-off levels may be used for European and Asian populations.
Blood pressure
Hypertension (abnormally high blood pressure) is a very common disorder. A
large international study on hypertension prevalence in Europe, Canada and
the US showed that about 30 to 40 percent of the adult population aged 35 to
64 years is hypertensive18. Studies from India and China are reporting an
equally high level in urban populations46, 47. Worldwide, about a quarter of all
adults are said to be affected by hypertension. Thus, the prevalence of high
blood pressure is not limited to industrialised or urban populations, but also
affects rural areas of China, India or Africa19.
The term blood pressure (BP) refers to the pressure within the arterial blood
vessels, which rises and falls in the form of waves with every beat of the heart.
The highest point of the wave is called the systolic blood pressure and the
lowest point the diastolic blood pressure. Both values are measured in mmHg
(millimetres of mercury).
Risk increase from blood pressure is almost always related to high values. Low
blood pressure – hypotension – does not lead to increased mortality or morbid
ity in the majority of cases. Exceptions are found when hypotension is related
to other causes such as impaired heart function, endocrine disorders or side
effects from drugs.
Figure 9: International classification of blood pressure
Source: 17
Depending on the cause of the elevated blood pressure, a distinction is made
between essential (primary) and secondary hypertension. Approximately
90–95% of all blood pressure disease is classified as essential, i.e. the exact
cause is unknown. In many cases, however, there is a genetic predisposition;
hormonal factors (in women around menopause) and dietary habits (excessive
weight, high salt intake) also play a role. Essential hypertension often occurs
Despite differences, the role of BMI is
comparable in Asian and Western
countries.
Worldwide, one adult in four suffers from
high blood pressure.
Classification Systolic Diastolic
Optimal 120 80
Normal 130 85
High normal 130 – 139 85 – 89
Mild hypertension (Grade 1) 140 – 159 90 – 99
Moderate hypertension (Grade 2) 160 – 179 100 – 109
Severe hypertension (Grade 3) 180 110
Increased body weight is linked to high
blood pressure.

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together with increased weight and obesity, diabetes mellitus, disorders of
lipid metabolism and gout as part of the metabolic syndrome.
Secondary hypertension accounts for about 5–10% of blood pressure disease;
it arises as a result of a confirmed primary disease21, e.g. kidney diseases or
endocrine problems.
Effects of hypertension
Chronic hypertension leads to serious changes in various organs which often
only become manifest after years or even decades. Typical sequelae are coro
nary and hypertensive heart disease, cerebral stroke, renal failure and hyper
tensive retinopathy.
Life expectancy with essential hypertension decreases as the blood pressure
increases. It has been statistically confirmed that sustained control of blood
pressure clearly reduces the extent of possible complications, in particular
when additional risk factors (e.g. disorders of lipid metabolism or obesity) are
present17.
In addition to effective antihypertensive therapy, the prognosis of essential
hypertension depends on the concurrent presence of additional risk factors
(Figure 10)17. The interaction of hypertension with other risk factors has been
mathematically modelled in the revised MIRA calculator. For example, being
overweight and having hypertension results in higher rates than the simple
addition of each as a single risk factor.
Figure 10: Interaction of hypertension with other risk factors
Blood pressure is significant for life
insurance.
The combination of high BMI and high
blood pressure represents a very
significant risk.
Blood pressure (mmHg)
Other
risk factors,
OD or disease
Normal
SBP 120 – 129
or DBP 80 – 84
High normal
SBP 130 – 139
or DBP 85 – 89
Grade 1 HT
SBP 140 – 159
or DBP 90 – 99
Grade 2 HT
SBP 160 – 179
or DBP 100 – 109
Grade 3 HT
SBP ≥ 180
or DBP ≥ 110
No other
risk factors
Average
risk
Average
risk
Low added
risk
Moderate
added risk
High added
risk
1 – 2
risk factors
Low added
risk
Low added
risk
Moderate
added risk
Moderate
added risk
Very high
added risk
3 or more risk
factors, MS, OD
or diabetes
Moderate
added risk
High added
risk
High added
risk
High added
risk
Very high
added risk
Established CV
or renal disease
Very high
added risk
Very high
added risk
Very high
added risk
Very high
added risk
Very high
added risk
Source: 17 (OD: subclinical organ damage, MS: metabolic syndrome)
Hypertension interacts with other cardiovascular risk factors such as obesity, smoking and
hyperlipidaemia. This fact has been mathematically modelled in the MIRA calculator.

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Mortality increase from elevated blood pressure is predominantly attributable
to vascular complications such as stroke or myocardial infarction22, 24, 25, 26. The
relationship to nonvascular death is less strong but still significant. Examples
include deaths from renal disease and liver disease22. Mortality from vascular
disorders increases exponentially with increased blood pressure over the
whole range of values. Analogously, studies analysing the effects of blood
pressure on all-cause mortality also show growing numbers of deaths with
higher blood pressure readings24, 26, 50, 51, 52 (Figure 11). The increased mortality
risk caused by high blood pressure is similar in men and women34, 35, 38, 39.
Figure 11: All-cause mortality by systolic blood pressure – age 40–60
Worldwide, high blood pressure is the second-most widespread cause of dis
ability after childhood malnutrition29. High blood pressure has a significant
and independent impact on early retirement and disability pensions30. Looking
at the causes of increased disability with high blood pressure, besides stroke,
myocardial infarction and kidney problems, intermittent claudication and
diabetes are also found30. Even knowledge of a hypertension diagnosis itself
increases the rate of sick leave among affected persons32.
For critical illness insurance, the most important claims triggers related to
hypertension are cardiovascular diseases (related covers: myocardial infarc
tion, coronary bypass and stroke) and kidney diseases, where the latter can
either be the cause of high blood pressure or a sequela (related covers: renal
failure and organ transplant). Each 20 mmHg-increase of systolic blood
pressure doubles the risk of myocardial infarctions and almost triples the
incidence of stroke22. Approximately one third of all new cases of renal failure
in the USA are currently attributed to hypertension28. The effects of high blood
pressure on cardiovascular diseases are consistent across various Asian and
Western populations48.
Mortality is mainly attributable to CVD, but
liver and kidney disorders are also
significant.
Munich Re
Stamler et al.
Miura et al.
Sairenchi et al.
Murakami et al.
Port et al.
Polynomial (Munich Re)
5.00
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0
Mortality ratio
100 120 140 160 180 200 220
Sys BP mmHg
Source: 24, 26, 51, 52, 53
Mortality risk from blood pressure is increased for high values. The risk of death from
any cause increases with higher blood pressure. Results from Munich Re data have been
compared to international studies.
Many disability insurance triggers are
linked to high blood pressure.
For critical illness insurance, blood
pressure is an important risk factor.

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Hypertension is also a very important risk factor for long term care insurance.
One reason is the high risk of cerebral strokes. Of 100 stroke victims, 40 suffer
from moderate to severe impairments such as paralysis or speech disorders
and 10 even require care in a nursing home or other long term care facility. The
other reason is a strong relationship between hypertension and dementia,
because microvascular lesions from chronic elevated blood pressure in the
brain lead to cognitive and functional decline31.
Lipids
Elevated lipid levels are very widespread: over 50% of the European population
has at least slightly elevated cholesterol levels according to WHO publica
tions40. Up to 60% of the US population has abnormal triglycerides41, although
the figure varies according to age, gender and race. On average, most Asian
populations have lower cholesterol levels than their Western counterparts,
with exceptions such as Singapore40.
The term hypercholesterolaemia (high cholesterol) refers to total cholesterol or
LDL cholesterol, whereas HDL cholesterol is considered a “good” cholesterol.
By contrast, low HDL values are regarded as a risk.
Typical normal clinical ranges for lipids are given in Figure 12. With regard to
other cardiovascular risk factors, normal clinical ranges for lipids are also
mostly stricter than the limits used for medical underwriting in life insurance.
In particular, non-preferred insurance products mostly allow for wider normal
ranges, because too many people in the general population have abnormal
readings and would otherwise be classified as substandard.
Stroke and dementia are among the most
important complications from high blood
pressure with regard to long term care
insurance.
Elevated lipid levels, including cholesterol
and triglycerides, are common.
Figure 12: Typical normal clinical ranges for lipids
ATP III classification of LDL, total and HDL cholesterol (mg/dl)
LDL cholesterol – Primary target of therapy
100 Optimal
100 – 129 Near optimal/above optimal
130 – 159 Borderline high
160 – 189 High
≥ 190 Very high
Total cholesterol
200 Desirable
≥ 240 High
HDL Cholesterol
40 Low
≥ 60 High
ATP III classification of serum triglycerides (mg/dl)
150 Normal
200 – 499 High
≥ 500 Very high
Source: 37

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Causes of hypercholesterolaemia are mostly a combination of genetic and
lifestyle factors such as elevated body weight and diet. In many cases of high
cholesterol, however, a change in diet alone is not sufficient and drug treatment
may be necessary. Genetic causes are polygenetic in most cases, i.e. multiple
genes play a role. In the case of triglycerides, diet and other lifestyle factors
have a comparatively stronger influence. Typical causes of high triglycerides
are increased weight, hypercaloric diet (too much fat and sugar), diabetes
mellitus and high alcohol consumption. There are also familial types of hyper
triglyceridaemia with different degrees of severity.
High levels of lipids in the form of both cholesterol and triglycerides, as well as
low HDL are associated with an increased risk of cardiovascular disease, parti
cularly related to ischaemic heart disease. The influence on stroke is much
weaker33, 34, 41, 49. The risk of ischaemic heart disease grows exponentially with
increased cholesterol over the entire range of values and is similar for Asian
and Western populations49.
There is a consistent relationship between increased cholesterol and higher
mortality (Figure 13). As noted, the excess deaths are predominantly caused by
ischaemic heart disease34. The mortality caused by abnormal triglycerides also
rises significantly, but less steeply. For example relative mortality risk related
to a serum level of 500 mg/dl (5.7 mmol/l) triglycerides in male non-smokers
corresponds to those at a total cholesterol level of 216–250 mg/dl
(5.6–6.5 mmol/l)35.
At ages over 60, the relative mortality risk from high cholesterol steadily
decreases – yet remains significant – and low cholesterol values become more
important42, 43. This may be explained by underlying life-threatening diseases
such as cancer or severe liver disease, which, as a side effect, lead to falling
cholesterol levels, as found by the Framingham Study 30 years ago44.
Figure 13: All-cause mortality by total cholesterol – ages 40–60
High cholesterol is associated with body
weight and genetic factors.
Frequency of CVDs, especially ischaemic
heart disease, increases with high
cholesterol.
Increased cholesterol is highly significant
in life insurance.
Munich Re
Cai et al. (US subgroup)
Strandberg et al.
Ivanovic et al.
Keil et al.
Ulmer et al.
Polynomial (Munich Re)
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0
Mortality ratio
Chol mg%
Source: 35, 39, 54, 55, 56
Mortality risk from cholesterol is increased for high values as well as for exceptionally low
values. Results from Munich Re data have been compared to international studies.
100 150 200 250 300 350 400 450

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Approximately 40% of myocardial infarctions (MI) are lethal within the first
weeks and months. A large proportion of the survivors can return to work after
two or three months, depending on the kind of occupation and the severity of
their coronary disease. According to a Finnish study, after a follow-up of two
years, the majority of MI survivors are still fully employed in their occupa
tions45. Thus, compared to other disease groups such as psychiatric disorders
or musculoskeletal problems, myocardial infarction is not among the leading
causes of disability. For this reason, studies of the impact of high lipids on
job-related disability are scarce.
To cope with these limitations, we have calculated actuarial models for high
lipids that account for the relative risk increase of myocardial infarctions and
the expected disability claims for cardiovascular disease for various products.
We have found that ratings for elevated lipids in case of disability can be
considerably lower compared to life or critical illness insurance.
High cholesterol and triglycerides significantly increase rates of coronary
artery disease and myocardial infarction, which are among the key claims trig
gers for critical illness insurance (related covers: myocardial infarction, coro
nary bypass). For example, in young men, a cholesterol level above 280 mg/dl
(7.25 mmol/l) leads to a 12-fold increase in ischaemic heart disease36. In both
Asian and non-Asian populations, total cholesterol is equally strongly associ
ated with coronary artery disease49.
For long term care, the impact of high lipids and myocardial infarction as their
sequela is even lower than for disability because a high mortality risk from
coronary artery disease reduces longevity and thus the incidence of disabling
diseases of old age such as dementia, which are key triggers of long term care
claims.
The effect of cholesterol on disability
insurance claims is comparably low.
High cholesterol is linked to key critical
illness insurance triggers.
The relevance of cholesterol for long term
care insurance is quite low.

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Blood Glucose
Blood sugar, i.e. blood glucose or plasma glucose, serves as the primary source
of energy directly available to the cells of the body. Blood sugar levels are there
fore tightly regulated by the body within a narrow, optimal range. In case of
metabolic dysfunction with impaired glucose regulation, these levels tend to
increase above normal and lead to a prediabetic or diabetic state.
A chronic increase in blood sugar damages the walls of arterial blood vessels,
leading to impaired perfusion in a number of organs, notably the heart, nerves,
kidneys and eyes. Impaired glucose metabolism is frequently associated with
obesity and hence with cardiovascular risk factors, such as hypertension and
hyperlipidaemia, thus mutually reinforcing vascular complications. Both its
cardiovascular sequels and its link to obesity makes blood sugar an important
component of the multivariate metabolic risk calculator in MIRA.
Blood glucose is measured in mg/dl (milligram per decilitre) or mmol/l
(millimol per litre). The conversion factor from mg/dl to mmol/l is 0.0555,
i.e. 100 mg/dl is equal to 5.55 mmol/l.
As blood sugar levels are sensitive to the amount of carbohydrates obtained
from food sources, they should be measured in the fasting state by abstaining
from food (and drink) for at least eight hours, typically in the morning before
breakfast.
Fasting blood sugar (FBS) values normally range between 70 mg/dl
(3.90 mmol/l) and 100 mg/dl (5.55 mmol/l in international guidelines,
rounded up to 5.6 mmol/l). Diabetes mellitus is diagnosed if FBS repeatedly
exceeds 125 mg/dl (6.90 mmol/l). Values between 101 and 125 mg/dl are
linked to a prediabetic state called impaired fasting glucose, with a higher risk
of developing diabetes within the next few years. Hypoglycaemia, i.e. abnor
mally low blood sugar, is defined as FBS below 55 mg/dl (3.0 mmol/l).
Figure 14: Classification of fasting blood sugar
Condition Fasting blood sugar range
Hypoglycaemia 55 mg/dl 3.00 mmol/l
Normal
American Diabetes Association 100 mg/dl 5.60 mmol/l
The International Expert Committee
and World Health Organization 110 mg/dl 6.10 mmol/l
Impaired fasting glucose
American Diabetes Association 100–125 mg/dl 5.60–6.90 mmol/l
The International Expert Committee
and World Health Organization 110–125 mg/dl 6.10–6.90 mmol/l
Diabetes mellitus 125 mg/dl 6.90 mmol/l
Abnormally high blood sugar is a condition that is frequently encountered. In a
representative survey of the Australian population, 10,428 people aged 25 or
above were screened with fasting blood sugar and oral glucose tolerance tests,
of these 26% were found to have abnormally high results (see Figure 15).57 In
the adult US population, figures according to the National Health and Nutrition
Examination Survey (NHANES) are even higher, with abnormally high blood
sugar being found in more than 35% of those surveyed (see Figure 16).58
Blood sugar should be taken
in a fasting state.
High blood sugar is a frequent finding in
the general population.

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Figure 15: Blood sugar screening in Australian adults
Source: 57
Figure 16: Blood sugar screening in US American adults
Source: 58
HbA1c as a screening test for life insurance
To avoid inconveniencing insurance applicants by demanding food abstinence,
some insurance markets and companies have introduced HbA1c as a substi
tute test for fasting blood sugar screening. In medical practice HbA1c is tradi
tionally used as a laboratory marker for controlling blood sugar levels in dia
betic patients. It reflects the average blood sugar level over a period of four to
six weeks prior to the test. HbA1c screening for diabetes has also been
included in international guidelines as a diagnostic marker where values of
6.5% (48 mmol/mol) and above imply a diagnosis of diabetes mellitus. Inter
mediate HbA1c ranges have been shown to be associated with a higher risk of
developing diabetes, which is comparable to impaired fasting blood sugar. The
intermediate range has been defined as 5.7% to 6.4% (39 to 46 mmol/mol) by
the American Diabetes Association59, and 6.0% to 6.4% (42 to 46 mmol/mol)
by the International Expert Committee.60
80
70
60
50
40
30
20
10
0
Normal Impaired
fasting
glucose
Impaired
glucose
tolerance
Newly
diagnosed
diabetes
Known
diabetes
73
6
12
4 4
%
90
80
70
60
50
40
30
20
10
0
Normal Impaired
fasting
glucose
Newly
diagnosed
diabetes
Known
diabetes
82
60
42
16
30
38
1 3 6
2
7
15
%
Age
20–39
40–59
60+
HbA1c can be used as a substitute for
fasting bloog sugar to avoid inconvenience
for insurance applicants.

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Random blood sugar
Although fasting blood sugar and HbA1C are the preferred screening tests for
insurance applicants, we are often presented with random blood sugar results.
Within the Metabolic Risk Calculator blood sugar is catagorised according to
the fasting time, here blood sugar with a fasting time 8 hours is considered
random blood sugar, and blood sugar with a fasting time ≥8 hours is fasting
blood sugar.
Risk assessment for abnormal blood sugar
For risk assessment in MIRA we mainly differentiate between abnormal blood
sugar screening and established diabetes mellitus. The latter is comprehen
sively discussed in the MIRA Risk Review Diabetes Mellitus. Here the focus
is on abnormal blood sugar screening tests without a definite diagnosis of
diabetes.
If an applicant’s fasting blood sugar is increased, there are three possible
causes:
−− Impaired fasting glucose
−− Diabetes mellitus
−− False positive test mostly due to non-fasting or – extremely rare – a lab error
Particularly if a test result is above the diabetes defining limit of 125 mg/dl
(6.9 mmol/l) there is a very high probability that the applicant genuinely
suffers from diabetes. However, more than one single test result must be
abnormal if the diagnosis of diabetes is to be confirmed. Even when fasting
glucose remains merely within the “prediabetic” range of 100 to 125 mg/dl
(5.6–6.9 mmol/l), the probability of diabetes being present is between 12%
and 17%.61
Therefore, when assessing the risk of abnormal fasting glucose we have to
consider the possibility of underlying diabetes mellitus. Because our suspicion
may never be clarified, there will always be uncertainty concerning whether
and when diabetes will be diagnosed and controlled. Such rates may be even
higher, therefore, than those for recently diagnosed diabetes.
Long-standing high blood sugar is associated with a multitude of organ prob
lems involving vascular damage caused by the high sugar concentrations, par
ticularly retinopathy, nephropathy, neuropathy, and cardiovascular diseases
such as myocardial infarction or stroke. Due to widespread media coverage,
most people are well-informed about the deadly impact of diabetes mellitus.
A less commonly known fact, however, is that blood sugar in the high yet nor
mal range has an influence on mortality and morbidity.
The renowned Australian Diabetes, Obesity and Lifestyle Study (AusDiab)
found that FBS has an impact on both all-cause and cardiovascular mortality
with blood sugar levels of 95 mg/dl (5.3 mmol/l) or more (see Figure 17 and
18).62 This result has been confirmed by others, including the Emerging Risk
Factors Collaboration who analysed 97 prospective studies with 820,900 dia
betics and more than 123,000 deaths.63, 64 Munich Re’s analysis of internal data
leads to the same conclusion (see Figure 19).
Abnormal blood sugar indicates a high
probability of underlying diabetes.
Blood sugar in the high yet normal range
may be related to increased risk.

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In accordance with the results of studies into FBS, other scientific groups
that examined the risk of HbA1c likewise revealed that mortality and
morbidity risks start to increase with high normal values of just 5.0% or more
(see figure 20).65, 66, 67
When fasting blood sugar test results are above normal, particularly above
125 mg/dl (6.9 mmol/l), it has to be assumed that the probability of diabetes
mellitus is increased. The risk therefore has to be seen in relation to this. Mor
tality from diabetes is strongly related to the quality of blood sugar control.68, 69
If diabetes is not controlled and blood sugar values rise, the risk of all types of
complications and mortality will be dramatically increased. This could be
confirmed by Munich Re’s analysis of FBS screening from application data in
which mortality almost doubles with an FBS of 150 mg/dl (8.3 mmol/l), and
increases to over threefold at 180 mg/dl (10 mmol/l) (see figure 19).
Figure 17: Fasting blood sugar in non-diabetics and all-cause mortality
Figure 18: Fasting blood sugar in non-diabetics and cardiovascular mortality
All-cause mortality rate per 1,000 py
25
10
5
1
5.1 5.1– 5.3– 5.6– 6.1– 7.0–
Categories of FPG (mmol/l)
Source: 62
CVD mortality rate per 1,000 py
25
10
5
1
0.5
5.1 5.1– 5.3– 5.6– 6.1– 7.0–
Categories of FPG (mmol/l)
Source: 62

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Figure 19: Fasting blood sugar in insurance applicants and all-cause mortality
Age group 40–59 years
Figure 20: HbA1c in non-diabetics and all-cause mortality
HbA1c and all-cause mortality
In the multivariate calculator, blood sugar is considered in association with
other risk factors such as obesity and high blood pressure. For example, due to
the combination of risks associated with severe obesity, high blood sugar may
lead to higher ratings compared to applicants who are not overweight. On the
other hand, normal blood sugar can even reduce the rating in obese applicants.
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0
40 60 80 100 120 140 160 180
RR
FBS mg/dl
Source: Munich Re
5.00
4.50
4.00
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0
5% 5.0–5.4% 5.5–5.9% 6.0–6.4% 6.5–6.9% ≥ 7%
Relative risk of mortality
HBA 1C
Source: 65 (male and female combined)
1.00 1.12
1.41
1.70
2.66
4.99

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Critical illness (CI)
High blood sugar is related to a variety of claim triggers for CI. Most important
are cardiovascular diseases (related cover: myocardial infarction, coronary
bypass and stroke), nephropathy (related cover: renal failure and organ trans
plant), and retinopathy (related cover: blindness). Cardiovascular diseases and
renal failure rank among the most frequent causes for CI claims; hence, critical
illness insurance is strongly affected. The risk of cardiovascular diseases is fur
ther increased if high blood sugar reaches the prediabetic range.62 As for mor
tality, consideration must be given to the possibility of underlying, uncontrolled
diabetes, an aspect that is extremely unfavourable in terms of critical illness
because a number of claim triggers can also arise directly in such a case. For
example, heart attacks are four to 14 times more frequent than normal with
type 2 diabetes.70, 71 The incidence of stroke is increased fivefold, and end-
stage renal disease 14- to 32-fold.72 If diabetes is present but has not been
diagnosed and controlled, the situation could even be worse.
Disability (DI)
Diabetes mellitus ranks among the leading causes of disability worldwide.71
The high rates of myocardial infarction, stroke, kidney dysfunction and blind
ness were already mentioned above. Additionally, the long-term complications
of diabetes typically affect the peripheral nerves and perfusion of the extremi
ties, leading to pain, functional impairment and amputation.72 Depression is a
common comorbidity in type 2 diabetes, with roughly 30% of diabetics experi
encing depressive symptoms and 10% major depression.73
Due to a wide variety of disabling conditions, the average work-related disability
is three to four times more frequent in diabetics than in non-diabetics.72, 74, 75, 76
Like CI, the risk in relation to disability insurance is extremely high, especially if
blood sugar control is less than adequate.
Long-term care (LTC)
Most disabling sequels of diabetes – particularly those related to mobility, the
nervous system and sensory function (e.g. stroke, neuropathy, blindness,
amputation) – also affect long-term care insurance. Depression is a common
comorbidity that affects about 30% of diabetics and has an aggravating effect
on LTC disability.73 Additionally, diabetes significantly increases the frequency
of dementia, one of the leading causes for LTC claims amongst the elderly.77, 78
Diabetes thus considerably increases the risk of LTC disability, a finding that
has been substantiated by several scientific studies.79, 80, 81, 82 Accordingly, LTC
claims from insured individuals aged 64–75 with diabetes at application were
2.5 times more frequent than normally expected.83
Critical Illness is highly impacted from
abnormal blood sugar.
Complications from Diabetes are potential
claims triggers for Disability.
Nervous and sensory function sequels from
high blood sugar can affect LTC claims risk.

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Benefits
The MRC allows holistic risk assessment that takes complex correlations and
interactions of relevant factors into account. Alongside convenient and practi
cal support in day-to-day underwriting, the advantages to you and your appli
cants include:
−− Greater accuracy and fairness
−− Increased security in risk assessment, based on state-of-the-art scientific
research
−− In many cases, more favourable ratings
Evidence-based underwriting
MIRA is dedicated to efficient, evidence-based risk assessment in a changing
world. For this reason, Munich Re continuously revises and updates the online
tool to reflect the latest scientific findings and risk trends. MIRA thus provides
maximum legal security regarding underwriting decisions. At the same time,
it serves as a solid basis for writing new business and optimising risk
management. The MIRA Risk Review series gives clients a clear overview of
each revision and its scientific background.

Contact
Dr. Jürgen Becher
Senior Medical Consultant
Centre of Competence
MIRA and Expert Underwriting Rules
Tel.: +49 89 38 91-99 88
Fax: +49 89 38 91-7 99 88
jbecher@munichre.com
Dr. Anne Zutavern, MSc
Medical Consultant
Tel.: +49 89 38 91-33 79
Fax: +49 89 38 91-7 33 79
azutavern@munichre.com
Edward Pickering
Consultant Actuarial Analysis
Tel.: +49 89 38 91-46 65
Fax: +49 89 38 91-7 46 65
epickering@munichre.com
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