Researchers offer a more complete, yet easily modified model for estimating American life expectancy by age and sex, incorporating for the first time the decline in tobacco use, increase in obesity and well-known trends and patterns of mortality.

Population mortality forecasts are an important underpinning for many analyses. Generally, attempts to incorporate more information into models have resulted in less accurate forecasts, so modelers have continued to use simple linear extrapolations based on limited data. But here, researchers propose an alternative Bayesian hierarchical forecasting model which allows for the inclusion of more data.

Key Findings:

• The incorporation of data related to the decline in smoking and increase in obesity among Americans has a profound impact on demographic projections.
• The new model projects faster gains in life expectancy, particularly among men, than current models, estimating an increase in American male life expectancy at birth from 76.2 years in 2010, to 79.9 years in 2030. This is 1.8 years greater than the U.S. Social Security Administration (SSA) estimate and 1.5 years greater than U.S. Census Bureau estimate.
• The new model projects faster aging of the population. The inclusion of tobacco and obesity data drives the new model to forecast more rapidly declining mortality rates than current models, especially for men over age 50. Thus, while the SSA projects 39.5 persons over age 65 years for every 100 people of working age in 2030, the new model forecasts 40.6 elderly persons. Researchers note this 1.1 person increase is “considerable” in the context of historical U.S. data.

The authors observe their forecasts yield “venerable” demographic patterns, suggesting the model is statistically sound. Noting there is still much to learn about the demographic impact of risk factors, such as obesity and smoking, the authors believe the model provides a good base for future improvements because it is easy to modify.