SEATTLE, Feb. 11 /PRNewswire/ -- In recent years, increasing attention has been paid to the role of Lifestyle-Based Analytics (LBA) in health insurance underwriting. In some instances, proponents of LBA made overly-optimistic claims about the use of consumer data as a predictor in the underwriting process. Milliman expert Jonathan Shreve, FSA, offers a fresh perspective on the appropriate and effective use of Lifestyle-Based Analytics as an advance in risk selection and classification.
Medical studies have shown that lifestyle characteristics and habits have a clear impact on disease prevalence. LBA uses information about lifestyle to enhance the risk classification system for relevant conditions. This information comes from data aggregators, which collect information from a variety of sources. Statistics, when properly interpreted, can enable underwriters to identify relationships between lifestyle information and prevalence of various diseases, which may result in a strong correlation with expected claims. Hence, LBA can help to differentiate high cost and low cost insurance plan members.
According to Jon Shreve, "For some of the correlations we have found, we believe there is a clear cause and effect - people who exercise more have fewer cardiovascular problems, and people who live alone have greater rates of depression. Sometimes, the lifestyle data may reflect the condition, rather than the other way around. People who are obese may be more apt to indicate that they are "walking for health" - not that walking causes obesity!"
LBA is increasingly viewed as a high-quality advance in the art of risk
selection. It does not pick out specific individuals in a group who
definitely have a condition, thus limiting its application in individual
and disease management applications. Nonetheless, LBA does identify
meaningful differences from one person to another and from one group to
another in the likelihood of experiencing or developing advers
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