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Variance in Death and Its Implications for Modeling and Forecasting Mortality
Shripad Tuljapurkar1, Ryan D Edwards1
1Morrison Professor of Population Studies, Department of Biological Sciences, Stanford University, Herrin Labs 454, Stanford, CA 94305-5020. tulja@stanford.edu .
Demographic Research
|October 21, 2014
Summary
Human lifespan variance is significant, both between and within groups. This study links lifespan variance to mortality patterns and frailty, impacting mortality modeling and forecasting accuracy.
Area of Science:
- Demography
- Biostatistics
- Epidemiology
Background:
- Survivorship functions reveal substantial variance in human lifespan.
- Lifespan variation exists between demographic groups (race, sex, socioeconomic status) and within narrowly defined groups.
- Within-group lifespan variance is inversely related to the average lifespan of the group.
Purpose of the Study:
- To investigate the relationship between lifespan variance and mortality.
- To explore the connection between variance in length of life and mortality slope.
- To analyze the implications for mortality modeling and forecasting.
Main Methods:
- Analysis of survivorship function slope and curvature.
- Examination of the relationship between lifespan variance and the Gompertz slope of log mortality.
- Investigation of the link between lifespan variance and a multiplicative frailty index.
Main Results:
- Lifespan variance is inversely related to the Gompertz slope of log mortality.
- A relationship was identified between lifespan variance and variance in a multiplicative frailty index.
- The proportional hazards assumption inadequately addresses subgroup variance differences.
Conclusions:
- Understanding lifespan variance is crucial for accurate mortality modeling.
- Current forecasting models may not fully capture temporal variance dynamics.
- Findings offer insights into improving demographic and epidemiological projections.
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