Willis Towers Watson has developed the first widely-available mortality model to use medical science and the views of medical experts to improve predictiveness.
The model, called PulseModel, is based around seven main disease groups: heart disease, diabetes, cancer, stroke, respiratory, digestive/renal, and neurological (plus common comorbidities).
Matthew Edwards, head of mortality and longevity in Willis Towers Watson's life insurance practice, said: "We have been concerned for some time that the mortality models in common use do not properly incorporate medical information - such as whether people are healthy or have some disease history - quite apart from lifestyle information such as smoking status or basic medical markers."
It is designed to help insurance companies and pension funds accurately price insurance cover, calculate liabilities and manage risk.
It projects annual expected transitions from healthy to disease group, and also models the effect of the precise starting condition, for instance the type of cancer.
In addition, the model incorporates risk factors such as smoking status, blood glucose and BMI as well as the time since diagnosis.
Willis Towers Watson's model can quantify the scale of the diabetes epidemic facing the UK while highlighting how lifestyle and medical markers will influence its spread.
For example of 16% of a selected group, in this case healthy 50-year-old men, will develop type-2 diabetes in the next 20 years. This figure rises by almost 50% (to 23%) for a group of 50-year-old men who are obese smokers.
According to the model, ‘healthy' diabetics in this age range (50 to 70) would typically expect to live around six years less than non-diabetics, with obese smoker diabetics expected to live four years less than that.
In addition, it shows that the obese smokers are twice as likely to die during this period than the healthy 50-year-old men.
Dr. James Brown, lecturer in ageing metabolism, Aston Research Centre for Healthy Ageing, Aston University, said: "Type 2 diabetes is indisputably one of the gravest health issues facing us in the 21st century. Prediction of the future burden of diabetes has previously been poor, with trend-based analysis providing predictions that frequently underestimate disease burden.
"This model represents a potential step-change in our ability to accurately predict outcomes based upon the likely future trends in diabetes incidence and more importantly it provides quantifiable predictions of the potentially dramatic implications that diabetes might have on mortality and life expectancy."
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