ArticleDemography2017
Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts.
Article in Demography, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A modal age at death approach to forecasting adult mortality.Population studies · 2025Article
- Article
- What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database.European journal of population = Revue europeenne de demographie · 2021Article
- Future life expectancy in Europe taking into account the impact of smoking, obesity, and alcohol.eLife · 2021Article
- Smoothing, Decomposing and Forecasting Mortality Rates.European journal of population = Revue europeenne de demographie · 2021Article
- Progress of Inequality in Age at Death in India: Role of Adult Mortality.European journal of population = Revue europeenne de demographie · 2021Article
- Demographic perspectives on the rise of longevity.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Mortality Forecasting with the Lee-Carter Method: Adjusting for Smoothing and Lifespan Disparity.European journal of population = Revue europeenne de demographie · 2021Article
- Advances in mortality forecasting: introduction.Genus · 2018Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evaluating the predictive ability of mortality forecasts is important yet difficult. Death rates and mean lifespan are basic life table functions typically used to analyze to what extent the forecasts deviate from their realized values. Although these parameters are useful for specifying precisely how mortality has been forecasted, they cannot be used to assess whether the underlying mortality developments are plausible. We therefore propose that in addition to looking at average lifespan, we should examine whether the forecasted variability of the age at death is a plausible continuation of past trends. The validation of mortality forecasts for Italy, Japan, and Denmark demonstrates that their predictive performance can be evaluated more comprehensively by analyzing both the average lifespan and lifespan disparity-that is, by jointly analyzing the mean and the dispersion of mortality. Approaches that account for dynamic age shifts in survival improvements appear to perform better than others that enforce relatively invariant patterns. However, because forecasting approaches are designed to capture trends in average mortality, we argue that studying lifespan disparity may also help to improve the methodology and thus the predictive ability of mortality forecasts.
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