The Science Behind Lumiant: The HALO Approach [Best Practices]

About Lumiant

Our mission is to help make sure that no one runs out of money in

retirement due to longevity risks and healthcare costs.

 

The HALO Planner (Health Analysis and Longevity Optimizer)

provides financial advisors with an elegant client-facing solution that

personalizes longevity and healthspan projections with the associated

health and eldercare costs.

*Note: Health and eldercare costs are available only in the US. 

 

Our software is designed to complement the relationship between

financial professionals and their clients, so they can provide a well-

planned and secure financial future.

 

The Science Behind Lumiant:

The HALO Approach

 

No one can see perfectly into the future, however, Lumiant uses innovative

methodology and powerful analytics based on millions of data points to

make personalized longevity, health, and eldercare cost projections possible

in the financial industry. This document provides an overview of our

methodology and approach. HALO offers a unique process for educating clients and prospects and

identifying planning opportunities by pinpointing where they have “gaps” in

their protection, resulting in moving clients towards informed decisions

much faster. This enables clients and advisors to make unbiased decisions

regarding risk mitigation strategies and provides personalized client

recommendations on how to live well longer.

 

Health Matters

Traditional approaches for planning for retirement ignore or marginalize the

effect of health on future finances.

However, studies show that health is a major factor in determining one’s economic wellbeing, and high health care costs are associated with bankruptcy1 and significant depletion of

retirement assets. Health determines many factors, such as out-of-pocket

care costs (e.g. hospital bill co-pays), length of life, and time spent in elder

care (e.g., nursing homes) that can affect retirement planning. Thus, any

financial advisor wishing to give their clients a full picture of their financial

future must take health into consideration

 

The Missing Opportunity

Unfortunately, there are very few data-driven resources available to

edu cate financial advisors and their clients on the relationship between

health and wealth. Furthermore, most tools available are difficult to use and

understand, and lack scientific grounding. The HALO Planner,

however, uses a heuristic decision-making model to create a solution that is

easy-to-use and based on years of scientific medical research

 

Risk Factors Considered

The HALO predictive model focuses on the most important

scientifically backed factors that affect longevity and years of

disability. Unlike other risk models that ask the user to complete long

and tedious questionnaires, which often include risk factors shown to

have a relatively small impact on mortality and morbidity, the Lumiant

approach covers all of the most statistically important risk factors

(some of which, ironically, are excluded by some of the longer

questionnaires).

 

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. Genomic data, if available, may eventually become factors

within the model.

 

Lumiant Data Sources

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 Center for Disease Control and Prevention (CDC), the SEER

Cancer database, the Kaiser Family Foundation, and Social Security

Administration.

 

In addition, the Lumiant 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.

As new clinical studies are published, the team reviews the data and

where appropriate, updates the models accordingly.

 

Lumiant Genetic Age

Lumiant’s concept of Genetic Age lays the foundation for our unique

analytic approach. Genetic 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 Genetic Age of 45 years with

respect to breast cancer.

 

An overall Genetic Age is calculated for each person individually by

taking a weighted average of the disease-specific Genetic Age for each

of the fifteen most common disease conditions. HALO focuses

on the most critical and common health conditions in the model, like

common forms of cancer and diabetes (it is estimated that heart

disease, cancer, and diabetes account for 7 of every 10 deaths in the

United States).

Most current metrics of risk (e.g., relative risk, odd

s ratios, etc.,) are difficult to understand by the average person, but the Genetic Age

metric translates risk into familiar terms of age. When explained using

the well-understood concept of age, the user can intuitively grasp how

their personal family health history is affecting their own risk of disease,

and, ultimately, their longevity.

 

Limitations of Genetic Tests and Actuarial Tables

Genetic tests typically cannot be used alone to accurately predict

longevity. Genetic tests are extremely useful in certain families with a

very strong family health history of disease, especially Mendelian

conditions, to understand potential inherited risks. However, for most

individuals with families that include multiple common conditions like

diabetes, stroke, and heart disease, family health history and lifestyle

are better predictors of risk, as compared to genetic tests alone.

Actuarial tables are based on data from broad populations of people

and can be useful in some situations, such as determining the average

life expectancy for people of certain age and gender.

 

However, actuarial tables are not personalized to each individual and their

particular life journey or circumstance. The tables fail to consider

detailed family health history information and many important lifestyle

factors such as smoking and exercise.

 

To understand the life expectancy of a specific individual, such as a 35-

year old man with a family health history of diabetes, BMI of 24, and a

smoking habit, an actuarial table based on a broad swath of individuals

will not be sufficient to provide the accuracy and fidelity of a unique,

personalized model

 

 

Conclusion

In a complex environment where there is a lot of information and

uncertainty about the future, a heuristic model for decision making is

superior to regression or probabilistic models because of the

demonstrated less-is-more effect. The reliance on rules based on

previous knowledge (a heuristic) to guide the result is more accurate

than a complex series of statistical tests incorporating all the data

points to cover all the bases across all possible outcomes.

 

People’s health, wealth, and family are inseparable and complex.

Employing a heuristic model to the relationship between family health

history, personal lifestyle and financial planning is the most effective

tool for empowering people to make good decisions about their health,

wealth, and financial goals.

 

Lumiant Team

The algorithm and science behind Lumiant was developed by Emily Chang,

Ph.D. Dr. Chang completed her doctoral work in theoretical, computational

chemistry at Stanford University with post-doctoral work in computational

genetics at Stanford Medical School. After that, she was a health scientist at

the consumer genetics company, 23andMe. Using her experience in

statistics, algorithm design, data analysis, and scientific study, she has

created a unique research-driven approach for understanding risk of

disease, projecting longevity, and using that for financial planning.

 

A few of Dr. Chang's Contributions to Scientific Research: 

1. Rains, Emily K., and Hans C. Andersen. "A Bayesian method for

construction of Markov models to describe dynamics on various time-

scales." The Journal of Chemical Physics 133.14 (2010): 144113.

2. Rains, Emily K., and Hans C. Andersen. 2009. A Bayesian approach for the

construction of Markov models. Abstract for poster presentation for the

World Molecular Kinetics Workshop 2009, Berlin, Germany.

3. Rains, Emily K., and Hans C. Andersen. 2009. Constructing Markov

Models for Protein Folding Simulation. Abstract for poster presentation for

the World Molecular Kinetics Workshop 2008, Berkeley, California.

4. Marusak, Rosemary A., Kate Doan, and Scott D. Cummings. Integrated

Approach to Coordination Chemistry: An Inorganic Laboratory Guide.

Hoboken, NJ: Wiley-Interscience, 2007. Print.

Consumer-facing research:

1. 23andMe’s Health Content Scientists. 23andMe Blog. June 28, 2013. Web. https://blog.23andme.com/23and...

[1] Dobkin C, Finkelstein A, Kluender R, Notowidigdo MJ. The Economic

Consequences of Hospital Admissions. Am Econ Rev. 2018;102(2):308-352.