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 interaction 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 metabolic 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 security and userfriendliness. 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 mostwidespread causes of death. They also play an important role inmorbidity and disability. Providing unprecedented detail andprecision, 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 calculatorNew methodology allows unprecedented accuracy

Munich ReMIRA RISK REVIEWThe multivariate metabolic risk calculator

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Contents

Understanding the condition 1

Groundbreaking methodology 3

Build: Body Mass Index 7Measuring build 8Relevance for life insurance 8Relevance for disability insurance 10Relevance for critical illness insurance 11Relevance for long term care insurance 11Is the situation different in Asia? 11

Blood pressure 12Relevance for life insurance 13Relevance for disability insurance 14Relevance for critical illness insurance 14Relevance for long term care insurance 15

Lipids 15Relevance for life insurance 16Relevance for disability insurance 17Relevance for critical illness insurance 17Relevance for long term care insurance 17

Blood glucose 18Understanding the condition 18Risk assessment for abnormal blood sugar 20Relevance for life insurance 20Relevance for critical illness insurance 23Relevance for disability insurance 23Relevance for long term care insurance 23

Benefits 24

Contact 25

Literature 26


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Groundbreaking methodology

The many qualities contributing to the unique methodology behind the multivariate 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 factors, BMI (height, weight), blood pressure and lipids followed later with the inclusion of a fourth factor blood glucose. Throughout the development, comparisons 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 overcome this and to improve upon the traditional onedimensional 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 calculatoruses data from a larger cohort than everseen before.

1,5 million applications10 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 policyholders. This ensured a realistic share of outliers within the database – a signifi cant shortcoming in conventional studies. Examples of extremes contained in t he 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 exceeding 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 crosssection 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, highquality 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

5,00050,000

250,000

360,000

900,000

1,500,000


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Figure 4: Risk profiles for BMI and cholesterol

These 3D 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, nonsmoker

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 specifically, 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 recommendations 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 analysisLost 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 allowsdetailed assessment of correlation andinteraction.


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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 insurancerelevant statistics are available in unparalleled range and quality. In addition, it would in no other country have been possible to crosscheck market information against official death records.

The main data source was a large and representative pooled insurance database containing medical application details. Secondary sources of information included medical and insurance articles, which were used as a basis for comparison. 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 relationship 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 marketspecific 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 stateoftheart tool that combines precision with numerous entry fields, including current values, previous values and many different convenient functionalities (radio buttons, dropdown lists, etc.). It has undergone 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 overweight, 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 excessive 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 wereextensively tested for technical andmedical accuracy.

BMI statistics vary between regions,insurance applicants representing afavourable 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, musculoskeletal 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 nonHispanic Caucasian adults aged 19–84 years and observed them for ten years (Figure 7). An analysis of 57 prospective studies, the Prospective 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, followedup 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 – 22.9923.00 – 24.99

Overweight ≥ 25.00 ≥ 25.00

Preobese 25.00 – 29.99 25.00 – 27.4927.50 – 29.99

Obese ≥ 30.00 ≥ 30.00

Obese class I 30.00 – 34.99 30.00 – 32.4932.50 – 34.99

Obese class II 35.00 – 39.99 35.00 – 37.4937.50 – 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 nonsmokers 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 1.111.25

1.58

1.99

1.47

1.141.00 1.00

1.09 1.191.44

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

1.181.00

0.97 1.031.16

1.44

1.93

1.37

1.01 1.00 1.00

1.061.21

1.44

2.06

2.93


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In nonsmokers without underlying disease, mortality increases 1.3fold for each 5unit BMI increase above BMI 253, 4 and for each 5unit 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 baseline 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 preexisting 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 multivariate metabolic risk calculator, we have accounted for the fact that a certain proportion of these underlying diseases can be detected by medical underwriting. 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 colonrectum, 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 osteoarthritis, 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 abovementioned 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 disability 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, coronary artery bypass surgery and stroke) and account for the vast majority of claims. Complications of diabetes account for further CIcovered 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 countries. 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, longterm 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

Ageadjusted 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 cutoff 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 cutoff 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 morbidity 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 coronary and hypertensive heart disease, cerebral stroke, renal failure and hypertensive 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 lifeinsurance.

The combination of high BMI and highblood pressure represents a verysignificant 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


Established CV or renal disease






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 allcause 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 secondmost widespread cause of disability 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 infarction, 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 mmHgincrease 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 220Sys 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 publications40. 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, nonpreferred 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

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 allcause 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 secondmost widespread cause of disability 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 infarction, 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 mmHgincrease 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.


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 220Sys 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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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 hypertriglyceridaemia 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, particularly 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 nonsmokers 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 lifethreatening 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.


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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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 hypertriglyceridaemia 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, particularly 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 nonsmokers 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 lifethreatening 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.


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

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 followup of two years, the majority of MI survivors are still fully employed in their occupations45. 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 jobrelated 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 triggers for critical illness insurance (related covers: myocardial infarction, coronary bypass). For example, in young men, a cholesterol level above 280 mg/dl (7.25 mmol/l) leads to a 12fold increase in ischaemic heart disease36. In both Asian and nonAsian populations, total cholesterol is equally strongly associated 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 therefore 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. abnormally 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 rangeHypoglycaemia < 55 mg/dl < 3.00 mmol/lNormal 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/lDiabetes 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 substitute test for fasting blood sugar screening. In medical practice HbA1c is traditionally used as a laboratory marker for controlling blood sugar levels in diabetic 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. Intermediate 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

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10

0

Normal Impaired fasting glucose

Impaired glucose tolerance

Newly diagnosed diabetes

Known diabetes

73

612

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

3038

1 3 62

715

%

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 comprehensively 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 nonfasting 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.

Longstanding high blood sugar is associated with a multitude of organ problems involving vascular damage caused by the high sugar concentrations, particularly retinopathy, nephropathy, neuropathy, and cardiovascular diseases such as myocardial infarction or stroke. Due to widespread media coverage, most people are wellinformed about the deadly impact of diabetes mellitus. A less commonly known fact, however, is that blood sugar in the high yet normal 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 allcause 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 diabetics 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. Mortality 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

Allcause 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 allcause 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

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0.5

0

40 60 80 100 120 140 160 180

RR

FBS mg/dlSource: Munich Re

5.004.504.003.503.002.502.001.501.000.500

< 5% 5.0–5.4% 5.5–5.9% 6.0–6.4% 6.5–6.9% ≥ 7%

Relative risk of mortality

HBA 1CSource: 65 (male and female combined)

1.00 1.121.41 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 transplant), 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 further increased if high blood sugar reaches the prediabetic range.62 As for mortality, 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 endstage renal disease 14 to 32fold.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 blindness were already mentioned above. Additionally, the longterm complications of diabetes typically affect the peripheral nerves and perfusion of the extremities, leading to pain, functional impairment and amputation.72 Depression is a common comorbidity in type 2 diabetes, with roughly 30% of diabetics experiencing depressive symptoms and 10% major depression.73

Due to a wide variety of disabling conditions, the average workrelated disability is three to four times more frequent in diabetics than in nondiabetics.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.

Longterm 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 longterm 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 practical support in daytoday underwriting, the advantages to you and your applicants include:

− Greater accuracy and fairness − Increased security in risk assessment, based on stateoftheart scientific research − In many cases, more favourable ratings

Evidence-based underwriting

MIRA is dedicated to efficient, evidencebased 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 BecherSenior Medical Consultant Centre of CompetenceMIRA and Expert Underwriting RulesTel.: +49 89 38 9199 88Fax: +49 89 38 917 99 [email protected]

Dr. Anne Zutavern, MScMedical ConsultantCentre of CompetenceMIRA and Expert Underwriting RulesTel.: +49 89 38 9133 79Fax: +49 89 38 917 33 [email protected]

Edward PickeringConsultant Actuarial AnalysisCentre of CompetenceMIRA and Expert Underwriting RulesTel.: +49 89 38 9146 65Fax: +49 89 38 917 46 [email protected]


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Literature

1 http://www.who.int/mediacentre/factsheets/ fs311/en/2 Adams KF et al. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71

years old. N Engl J Med. 2006;355:76378.3 Berrington de Gonzalez A et al. BodyMass Index and Mortality among 1.46 Million White Adults. N

Engl J Med 2010; 363:221122194 Prospective Studies Collaboration. Bodymass index and causespecific mortality in 900 000 adults:

collaborative analyses of 57 prospective studies. Lancet. 2009; 373:1083–1096.5 T. Pischon et al. General and Abdominal Adiposity and Risk of Death in Europe. N Engl J Med

2008;359:210520.6 Shields M et al. Measures of abdominal obesity within body mass index categories, 1981 and 2007

2009. Statistics Canada, Catalogue no. 82003XPE • Health Reports, Vol. 23, no. 2, June 20127 WCRF. World Cancer Research Fund. Food, Nutrition, Physical Activity, and the Prevention of Cancer:

a Global Perspective. 2nd edn. Washington, USA : American Institute for Cancer Research, 2007.8 Renehan AG et al. Bodymass index and incidence of cancer: a systematic review and metaanalysis

of prospective observational studies. Lancet 2008; 371: 569–789 Neovius M et al. Association between obesity status in young adulthood and disability pension.

International Journal of Obesity (2008) 32, 1319–132610 Al Snih S et al. The effect of obesity on disability vs mortality in older Americans. Arch Intern Med.

2007. 167:77480.11 Taş Ü et al. Incidence and risk factors of disability in the elderly: The Rotterdam Study. Preventive

Medicine 44 (2007) 272–27812 Wannamethee G et al. Overweight and obesity and the burden of disease and disability in elderly

men. International Journal of Obesity (2004) 28, 1374–138213 WHO Expert Consultation. Appropriate

bodymass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004 Jan 10; 363(9403):15763.

14 Jee SH et al. BodyMass Index and Mortality in Korean Men and Women. N Engl J Med 2006; 355:779787

15 Zheng W et al. Association between BodyMass Index and Risk of Death in More Than 1 Million Asians. N Engl J Med 2011; 364:719729

16 Chen Z et al. Body mass index and mortality in China: a 15year prospective study of 220 000 men. Int J Epidemiol. 2012;41:47281.

17 Mancia et al. 2007 Guidelines for the management of arterial hypertension. European Heart Journal, 2007; 28:14621536

18 WolfMaier K et al. Hypertension prevalence and blood pressure levels in 6 European countries, Canada and the United States. JAMA, 2003; 289:23632369

19 Kearney PM et al. Global burden of hypertension: analysis of worldwide data. Lancet, 2005; 365: 217–23

20 Miyai N et al. Blood pressure response to heart rate during exercise test and risk of future hypertension. Hypertension, 2002; 39:761766

21 Viera AJ, Neutze DM. Diagnosis of secondary hypertension: an agebased approach. Fam Physician, 2010; 82:14711478

22 Prospective studies collaboration. Agespecific relevance of usual blood pressure to vascular mortality: a metaanalysis of individual data for one million adults in 61 prospective studies. Lancet, 2002; 360: 19031913

23 Strandberg TE, Salomaa V. White coat effect, blood pressure and mortality in men: prospective cohort study. European Heart Journal, 2000; 21:17141718

24 Miura K et al. Relationship of blood pressure to 25year mortality due to coronary heart disease, cardiovascular disease and all causes in young adult men. Arch Intern Med, 2001; 161: 15011508

25 Pinkham AC, Invanovic B, Cumming ME. 2003 Swiss Re blood pressure study of insured lives. North American Actuarial Journal, 2005; 9:116

26 Sairenchi T et al. Age specific relationship between blood pressure and the risk of total and cardiovascular mortality in Japanese men and women. Hypertens Res, 2005; 28:901909

27 Guo Z,Viitanen M, Winblad B. Low blood pressure and fiveyear mortality in a Stockholm cohort of the very old: possible confounding by cognitive impairment and other factors. Am J Public Health, 1997; 87:623–628

28 KincaidSmith P. Hypothesis: obesity and the insulin resistance syndrome play a major role in endstage renal failure attributed to hypertension and labelled ‘hypertensive nephrosclerosis’. Journal of Hypertension, 2004;22:10511055

29 Lopez AD et al. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet, 2006; 367: 1747–57

30 Kark M, Karnehed N, Rasmussen F. Blood pressure in young adulthood and later disability pension. A populationbased study on 867 672 men from Sweden. Blood Pressure, 2007; 16: 362–366

31 Elias MF, Elias KP. Blood pressure and disability. Hypertension, 2007;50:10061008


/28

32 Leynen F et al. Increased absenteeism from work among aware and treated hypertensive and hyperholesterolaemic patents. Eur J Cardiovasc Prev REhabil, 2006; 13: 261267

33 Lawes CMM et al. High cholesterol. In Ezzati M et al. (editors). Comparative quantification of health risks global and regional burden of disease attributable to selected major risk factors. Volume 1. World Health Organization, 2004. pp 391496

34 Prospective Studies Collaboration. Blood cholesterol and vascular mortality by age, sex, and blood pressure: a metaanalysis of individual data from 61 prospective studies with 55000 vascular deaths. Lancet, 2007; 370: 18291839

35 Ivanovic B, Pinkham A. Relationship between serum lipids and subsequent mortality in an insured population. J Insur Med, 2003; 35: 1116

36 Stamler J et al. Relationship of baseline serum cholesterol levels in 3 large cohorts of younger men to longterm coronary, cardiovascular and allcause mortality and to longevity. JAMA, 2000; 284: 311318

37 National Cholesterol Education Program. ATP III guidelines ataglance quick desk reference. National Institute of Health, 2001. NIH Publication No. 013305

38 Wesley D, Cox HF. Modeling total cholesterol as predictor of mortality: the lowcholesterol paradox. J Insur Med, 2011; 42:6275

39 Keil: Keil U et al. Classical risk factors and their impact on incident nonfatal and fatal myocardial infarction and allcause mortality in southern Germany. European Heart Journal, 1998; 19: 11971207

40 http://www.who.int/gho/ncd/risk_factors/ cholesterol_text/en/41 Miller M et al. Triglycerides and cardiovascular disease: a scientific statement from the American

Heart Association. Circulation, 2011; 123:2292233342 Cabrera MAS, de Andrade SM, Dip RM. Lipids and allcause mortality among older adults: a 12year

followup study. The Scientific World Journal, 2012; doi:10.1100/2012/93013943 Upmeier E et al. Serum lipids and their association with mortality in the elderly: a prospective cohort

study. Aging Clin Exp Res, 2009; 21:42443044 Anderson KM, Castelli WP, Levy D. Cholesterol and mortality 30 years followup from the

Framingham Study. JAMA 1987; 16:2176218045 Hämäläinen H et al. Return to work after first myocardial infarction in 19911996 in Finnland.

European Journal of Public Health, 2004; 14:35035346 Gupta R. Trends in hypertension epidemiology in India. J Hum Hypertens, 2004; 18:737847 Gao Y et al. Prevalence of hypertension in China: a crosssectional study. PLOS ONE, 2013; 8: e65938.

doi:10.1371/journal. pone.006593848 Asia Pacific Cohort Studies Collaboration. Joint effects of systolic blood pressure and serum

cholesterol on cardiovascular disease in the Asian Pacific region. Circulation, 2005; 112:3384339049 Asia Pacific Cohort Studies Collaboration. Cholesterol, coronary heart disease, and stroke in the Asia

Pacific region. Int J Epidem, 2003; 32:56357250 A Rissanen, et al. Risk of disability and mortality due to overweight in a Finnish population. BMJ. Oct

13, 1990; 301: 83583751 Stamler J et al. Blood pressure, systolic and diastolic, and cardiovascular risks. US population data.

Arch Intern Med, 1993; 153:59861552 Murakami Y et al. Relation of Blood Pressure and AllCause Mortality in 180 000 Japanese

Participants: Pooled Analysis of 13 Cohort Studies. Hypertension, 2008;51:1483149153 Port S et al. Systolic blood pressure and mortality. Lancet, 2000; 355: 175–80 54 Cai J.et al. Total cholesterol and mortality in China, Poland, Russia, and the US. Ann Epidemiol,

2004;14:399–40855 Strandberg TE et al. Low cholesterol, mortality, and quality of life in old age during a 39Year

followup. J Am Coll Cardiol, 2004;44:1002– 100856 Ulmer H et al. Why Eve Is not Adam: prospective followup in 149,650 women and men of cholesterol

and other risk factors related to cardiovascular and allcause mortality. Journal of Woman’s Health, 2004;13:4153

57 Barr ELM et al. Risk of cardiovascular and allcause mortality in individuals with diabetes mellitus, impaired fasting glucose, and impaired glucose tolerance. Circulation, 2007; 116: 151–157

58 Cowie CC et al. Prevalence of diabetes and impaired fasting glucose in adults in the U.S. population. Diabetes Care, 2006; 29: 1263–1268

59 American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care (Supplement), 2010; 33: S62–S69

60 The International Expert Committee. International Expert Committee report on the role of A1c assay in the diagnosis of diabetes. Diabetes Care, 2009; 32: 1327–1334

61 World Health Organization. Screening for type 2 diabetes. Report of a WHO/IDF meeting. WHO/NMH/MNC/03.1. Geneva, 2003; World Health Organization

62 Barr ELM et al. Continuous relationship between nondiabetic hyperglycaemia and both cardiovascular disease and allcause mortality: the Australian Diabetes, Obesity, and Lifestyle (AusDiab) study. Diabetologia, 2009; 52: 415–424

63 The Emerging Risk Factors Collaboration. Diabetes mellitus, fasting glucose, and risk of causespecific death. N Engl J Med, 2011; 364: 829–841

64 Port SC et al. Blood glucose: a strong risk factor for mortality in nondiabetic patients with cardiovascular disease. Am Heart J, 2005; 150: 209–214


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65 Khaw KT et al. Association of hemoglobin A1c with cardiovascular disease and mortality in adults: The European Prospective Investigation into Cancer in Norfolk. Ann Intern Med, 2004; 141: 413–420

66 The Emerging Risk Factors Collaboration. Glycated haemoglobin measurement and prediction of cardiovascular disease. Jama, 2014; 311: 1225–1233

67 Selvin E et al. Glycated haemoglobin, diabetes, and cardiovascular risk in nondiabetic adults. N Engl J Med, 2010; 362: 800–811

68 Currie CJ et al. Survival as a function of HbA1c in people with type 2 diabetes: a retrospective cohort study. Lancet, 2010; 375: 481–489

69 ADVANCE collaborative group. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med, 2008; 358: 2560–2572

70 Hiller TA, Pedula KL. Complications in young adults with earlyonset type 2 diabetes. Diabetes Care, 2003; 26: 2999–3005

71 Khaw KT et al. Association of hemoglobin A1C with cardiovascular disease and mortality in adults: the European prospective investigation into cancer in Norfolk. Ann Intern Med, 2004; 141: 413–420

72 Naslafkih A, Sestier F. Diabetes mellitus related morbidity, risk of hospitalization and disability. J Insur Med, 2003; 35: 102–113

73 Egede LE. Diabetes, major depression, and functional disability among U.S. adults. Diabetes Care, 2004; 27: 421–428

74 Tuncelli K et al. The impact of diabetes on employment and work productivity. Diabetes Care, 2005; 28: 2662–2667

75 Mayfield JA et al. Work disability and diabetes. Diabetes Care, 1999; 22: 1105–110976 Korff MV et al. Work disability among individuals with diabetes. Diabetes Care, 2005; 28: 1326–133277 Ohara T et al. Glucose tolerance status and risk of dementia in the community: the Hisayama study.

Neurology, 2011; 77: 1126–113478 Launer LJ et al. Effects of intensive glucose lowering on brain structure and function in people with

type 2 diabetes (ACCORD MIND): a randomised openlabel substudy. Lancet Neurology, 2011; 10: 969–977

79 Valiyeva E et al. Lifestylerelated risk factors and risk of future nursing home admission. Ann Intern Med, 2006; 166: 985990

80 Bruce DG, Davis WA, Davis TME. Longitudinal predictors of reduced mobility and physical disability in patients with type 2 diabetes. Diabetes Care, 2005; 28: 2441–2447

81 Gregg EW et al. Diabetes and physical disability among older U.S. adults. Diabetes Care, 2000; 23: 1272–1277

82 Nihtilä EK et al. Chronic conditions and the risk of longterm institutionalization among older people. European Journal of Public Health, 2007; 18: 77–84

83 Holland SK et al. Stratifying longterm care risk by cardiovascular risk factors – an analysis of claims experience with atrial fibrillation. J Insur Med, 2006; 38: 253–258

NOT IF, BUT HOW

© 2015Münchener RückversicherungsGesellschaftKöniginstrasse 107, 80802 München, Germany

Order number 30208273

Münchener RückversicherungsGesellschaft (Munich Reinsurance Company) is a reinsurance company organised under the laws of Germany. In some countries, including in the United States, Munich Reinsurance Company holds the status of an unauthorised reinsurer. Policies are underwritten by Munich Reinsurance Company or its affiliated insurance and reinsurance subsidiaries. Certain coverages are not available in all jurisdictions. Any description in this document is for general information purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any product.

mira risk review: multivariate metabolic risk calculator

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