On the relationship between human all-cause mortality and age.

Area of Science:

• Demography
• Biostatistics
• Gerontology

Background:

• Mortality rates and aging are complex phenomena studied using various mathematical models.
• Understanding these relationships is crucial for public health and gerontological research.

Purpose of the Study:

• To evaluate the effectiveness of different mathematical equations in modeling the relationship between mortality rates and age.
• To identify the best-fitting model for analyzing mortality patterns in developed countries.

Main Methods:

• Analysis of mortality data across developed countries using Gompertz, Weibull, logistic, polynomial, and age-period-cohort equations.
• Statistical correlation analysis to assess the fit of each model to ln mortality rates and age (35-84 years).

Main Results:

• All tested equations showed significant correlations between ln mortality rates and age (35-84).
• A second-degree polynomial equation provided the best fit (R2 > 0.99), closely followed by the Gompertz equation.
• A significant correlation between Gompertz equation parameters suggests a 'crossing-over' age around 85, where higher initial mortality may indicate stronger survivors.

Conclusions:

• Mathematical models, particularly polynomial and Gompertz equations, are highly effective for studying the aging process and mortality patterns.
• These models are independent of specific causes of death and applicable across developed countries.
• The findings highlight the utility of these equations for exploring factors influencing population mortality rates.