Modeling and Forecasting Healthy Life Expectancy With Compositional Data Analysis

Area of Science:

• Demography
• Public Health
• Gerontology

Background:

• Increasing life expectancy raises questions about healthspan.
• Accurate forecasting of healthy life expectancy (HLE) is vital for societal planning.
• Existing HLE forecasting models are limited.

Purpose of the Study:

• To propose and evaluate two novel models for simultaneous and coherent forecasting of health and mortality.
• To improve the accuracy of healthy life expectancy (HLE) forecasts.

Main Methods:

• Developed two models: one based on the Sullivan method and another on multistate life tables.
• Utilized Compositional Data Analysis to ensure coherence between health and mortality forecasts.
• Applied models to forecast mortality and health for females aged 50+ in France, Spain, Sweden, and the UK.

Main Results:

• Both proposed models yielded largely nonsignificantly different estimates and forecasts for HLE.
• The new models demonstrated potential for improved forecast accuracy compared to existing methods.
• Coherent forecasting of health and mortality was achieved.

Conclusions:

• The developed models offer a robust approach to forecasting healthy life expectancy (HLE).
• These models provide reliable and coherent health and mortality forecasts, aiding future planning.
• Accurate HLE forecasting is essential for addressing the challenges of an aging population.