Modeling and prediction of age-specific mortality rates using the Lee–Carter model

Abstract

BACKGROUND: High mortality remains one of the most significant health concerns in Russia. One of the priorities of the state policy is to reduce mortality rates among the working-age population and increase life expectancy. Predicting population mortality rates serves as a valuable tool for effectively allocating the available resources. AIM: To perform mathematical modeling and prediction of mortality rates of the population of the Orenburg region using the Lee–Carter model. MATERIAL AND METHODS: The age- and sex-specific mortality rates and the population size of the Orenburg region for the period 1991–2020 was used as a study base. The Lee–Carter method was applied to model and predict population mortality. By deriving key parameters, a random walk model with drift was developed, and an accuracy assessment was performed. RESULTS: The Lee-Carter model has been utilized to analyze the mortality rates of the male population in the Orenburg region. Through this modeling process, an accuracy rate of 87% was achieved, providing a reliable basis for long-term prediction. Mortality forecasts have been generated up to the year 2035, allowing for a comprehensive evaluation of future trends in the region. CONCLUSION: The analysis of the results indicates that the pandemic's impact on population mortality is expected to be short-term. In the upcoming years, the mortality rate of the male population in the Orenburg region is projected to continue decreasing.