Mortality Risk of Low BMI in Life Insurance Applicants

Author:

Rigatti Steven J.1,Stout Robert1

Affiliation:

1. Author Affiliations: Rigatti – Founder, Rigatti Risk Analytics, LLC, Consultant Medical Director, Clinical Reference Laboratories, Lenexa KS. Stout – Chief Scientific Officer/Laboratory Director, Clinical Reference Laboratories, Lenexa KS

Abstract

Objectives.—This study seeks to quantify the mortality effect of low levels of body mass index (BMI) on life insurance applicants who, based on their laboratory profile and other information, appear to be suitable for life insurance coverage. Background.—It has been demonstrated that low BMI is associated with higher mortality risk than normal or near-normal BMI. Methods.—Data were collected from over 4.7 million life insurance applicants with available BMI tested between 1995 and 2021, and vital status was assessed via the Social Security Death Master File. Cox models treating BMI as continuous and as a categorical variable were constructed, controlling for age, and split by sex after excluding those with laboratory or biometric test results, which were far enough outside the normal range to imply elevated mortality. Results.—Models treating BMI as a continuous variable and allowing an interaction term for age showed that low BMI was strongly associated with mortality at ages 50 and above in both sexes. In the categorical models, only the lowest category of BMI (below the 1st percentile) in men aged 40-60, the lowest 2 categories (below the 5th percentile) in women aged 40-60, and the lowest 3 categories (below the 10th percentile) in those aged 60-80 years, were significantly associated with elevated mortality. No elevated mortality was detected in those under age 40 with low BMI. Conclusion.—Based on this study, low BMI is associated with elevated mortality in otherwise healthy applicants, but this association is dependent on age.

Publisher

American Academy of Insurance Medicine

Reference9 articles.

1. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK;Bhaskaran;Lancet Diabetes Endocrinol,2018

2. The effect of low body mass index on the development of chronic obstructive pulmonary disease and mortality;Park;J Intern Med,2019

3. R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/

4. Accuracy of vital status ascertainment using the Social Security Death Master File in a deceased population;Ashley;J Insur Med,2012

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