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Evaluating mortality forecasts requires more than just average lifespan. This study suggests analyzing lifespan disparity alongside average lifespan improves forecast accuracy and assesses the plausibility of mortality trends.

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Area of Science:

  • Demography
  • Actuarial Science
  • Public Health

Background:

  • Assessing the accuracy of mortality forecasts is crucial for public health and economic planning.
  • Traditional methods using death rates and mean lifespan offer limited insight into forecast plausibility.
  • Existing forecast evaluations often fail to capture the full picture of mortality dynamics.

Purpose of the Study:

  • To introduce and validate a more comprehensive approach for evaluating mortality forecasts.
  • To assess the predictive performance of mortality forecasts by incorporating lifespan disparity.
  • To determine if analyzing the dispersion of age at death improves forecast methodology.

Main Methods:

  • Proposed a novel evaluation framework combining average lifespan (mean) and lifespan disparity (dispersion).
  • Validated the approach using mortality forecast data from Italy, Japan, and Denmark.
  • Compared forecasting methods based on dynamic age shifts versus invariant patterns.

Main Results:

  • Mortality forecast validation is more robust when considering both average lifespan and lifespan disparity.
  • Approaches that model dynamic age shifts in survival improvements outperform those with invariant patterns.
  • Analysis of lifespan disparity provides insights into the plausibility of forecasted mortality trends.

Conclusions:

  • Joint analysis of mean and dispersion of mortality offers a superior method for evaluating forecast accuracy.
  • Studying lifespan disparity can significantly enhance the methodology and predictive power of mortality forecasts.
  • This approach aids in understanding and improving the reliability of future mortality predictions.