Table 1 - uploaded by Brad Roudebush
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Source publication
This study used the Impairment Study Capture System (ISCS) to examine the relationship between mortality and body mass index (BMI) in an insured population, particularly BMI in isolation from other risk factors.
Large-scale studies of build in an insured population have traditionally been done on policies issued at standard premium rates. Insured m...
Contexts in source publication
Context 1
... A person's BMI is found by dividing weight in kilograms by height in meters squared. Classification of BMI is shown in Table 1. BMI, or its less specific historical namesake ''build,'' has been used in life insurance underwriting for over 100 years. 13 Masked by a high correlation with tuberculosis, un- derweight insureds were originally thought to present the worst mortality risk. ...
Context 2
... deaths were based on the 2001 Valuation Basic Tables (2001 VBT) created by the Society of Actuaries (SOA) Individual Life Insurance Valuation Mortality Research Task Force (Task Force) and published in its November 2001 report. 17 The experience studies underlying the 2001 VBT are based on fully underwritten policies and specifi- cally exclude substandard policies. ...
Context 3
... ratios were highest overall in policy durations 1-2 and especially so for underweights (SMR 278%, 95% CI 192%- 388%) and extremely obese (SMR 227%, 95% CI 177%-286%). (Table 10) ...
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Citations
... The study by Behan et al. (2010) provided a comprehensive literature review addressing how the obesity relates to mortality and morbidity costs. Niverthi and Ianovic (2001), Baldinger et al. (2006), and Roudebush et al. (2006) performed the observational cohort-type studies on insured populations in Switzerland and the United States. These studies followed a group of life insured policyholders from the time their policies were issued to death, to establish how the BMI affects the relative risk of death and standardized mortality ratio. ...
Modeling obesity prevalence is an important part of the evaluation of mortality risk. A large volume of literature exists in the area of modeling mortality rates, but very few models have been developed for modeling obesity prevalence. In this study we propose a new stochastic approach for modeling obesity prevalence that accounts for both period and cohort effects as well as the curvilinear effects of age. Our model has good predictive power as we utilize multivariate ARIMA models for forecasting future obesity rates. The proposed methodology is illustrated on the U.S. population, aged 23–90, during the period 1988–2012. Forecasts are validated on actual data for the period 2013–2015 and it is suggested that the proposed model performs better than existing models.
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This study used the Impairment Study Capture System (ISCS) to examine the relationship between mortality and participation in aviation and/or hazardous sports in an insured population.
With ever improving mortality in the industry, the significance of mortality from these "risky" activities may be more impactful than ever. This study fills a 20-year gap in intercompany studies of these risks.
We studied 45,206 policies submitted through the ISCS between 1989 and 2004 with codes signifying recent or anticipated participation in aviation or hazardous sports. Aviation activity included both private and commercial flying and was crudely stratified by hours flown. Hazardous sports included motor vehicle racing, flying in other than conventional aircraft, underwater sports, and other. Excess death rates per thousand, relative to the 2001 VBT were computed. Results were stratified by underwriting factors of interest.
Sixty-nine deaths were observed in the aviation study and 60 in hazardous sports over an average followup of 3.2 years. An additional 6 deaths were observed in policies belonging to both studies. Extra mortality was observed for aviation in early durations only whereas mortality from hazardous sports persisted longer. Mortality was higher for policies rated for these activities vs those issued at standard rates. No other variable of underwriting significance was meaningful.
Life insurance underwriting identifies the least risky of these activities and classifies them appropriately. The absence of extra mortality in later durations may be real or could be the artifact of study design.
