See a preview of what trouble may come, so you can put together a gameplan years in advance

 

Base clients’ longevity estimates on their unique health, behavior and family, not some generic actuarial table.

Plan around an accurate estimate for this year, next year and every other year all the way through retirement.

Using her experience in statistics, algorithm design, data analysis, and scientific study, Emily Chang Pd.D has created a unique research-driven approach for understanding risk of disease, projecting longevity, and using that for financial planning. Dr. Chang completed her doctoral work in theoritcal, computational chemistry at Stanford, with post-doctoral work in computational genetics at Stanford Medical School. Previously, she was a health scientist at the consumer genetics company, 23andMe.

Genivity HALO’s projections are powered by over 100 million scientifically relevant data points from more than 90 carefully-vetted and curated data sources including validated data from large studies by the CDC, the SEER Cancer database, the Kaiser Family Foundation, and Social Security Administration. In addition, the Genivity team has evaluated hundreds of studies in high-quality, peer-reviewed academic journals, such as The Journal of the American Medical Association and The New England Journal of Medicine, to find the best parameters for inclusion in the HALO models.

HALO covers all the most statistically important risk factors that affect longevity and years of disability. The model puts a strong emphasis on family health history, including factors such as: Alzheimer’s/dementia, stroke, diabetes, heart disease, obesity, cancer (bladder cancer, colon cancer, breast cancer, kidney cancer, lung cancer, ovarian cancer, pancreatic cancer, skin cancer and prostate cancer), and the overall longevity of parents and grandparents. In addition, the most important lifestyle factors (smoking, exercise, diet, alcohol consumption, BMI, and social support), as well as demographic factors like age, gender, personal health history and ethnicity, are also considered.

Biological Age is the age at which a person’s risk of disease, based on their family health history, is comparable to an average person of the same gender in the general population. For example, if a 35-year-old woman has a health history of breast cancer, her risk of breast cancer may be more comparable to a typical 45-year-old woman. The model would suggest that this woman with an elevated risk of breast cancer has a Biological Age of 45 years with respect to breast cancer. An overall Biological Age is calculated for each person individually by taking a weighted average of the disease-specific Biological Age for each of the thirteen most common disease conditions. This Biological Age is continuously updated in preparation for the next review of a person’s financial and retirement plan.