Age-Specific Mortality Forecasting in Kazakhstan: Alternative Approaches to the Lee–Carter Model

Berik Koichubekov (), Bauyrzhan Omarkulov, Meruyert Mukhanova and Rimma Zakirova
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Berik Koichubekov: Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
Bauyrzhan Omarkulov: Department of Family Medicine, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
Meruyert Mukhanova: Department of Family Medicine, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan
Rimma Zakirova: Department of Informatics and Biostatistics, Karaganda Medical University, Gogol St. 40, Karaganda 100008, Kazakhstan

IJERPH, 2025, vol. 22, issue 3, 1-22

Abstract: Age-specific mortality forecasting in Kazakhstan plays a crucial role in public health planning and healthcare management. By predicting mortality rates across different age groups, policymakers, healthcare providers, and researchers can make informed decisions that improve health outcomes and allocate resources more effectively. We analyzed Kazakhstan’s annual mortality data from 1991 to 2023. The Lee–Carter model and its extensions were used to predict mortality. But they did not give satisfactory results for predicting mortality. Including external socio-economic factors in the model did not improve the forecasting accuracy. The accuracy of the forecast increased with a separate analysis of the subpopulations of children and adults. This was because, since 1991 in the children subpopulation there has been a pronounced linear downward trend, while in the adult subpopulation the global trend in mortality dynamics is nonlinear. As a result, it is possible to make forecasts for 7 years with a high degree of accuracy (error < 10%) and forecast for the 8th, 9th, and 10th years with a “good” degree of accuracy (error 10–20%). In 2024–2033, a further mortality decline is expected in most age groups. Only in groups over 80 years old is a slight increase in mortality predicted in the coming year, but then a downward trend will be observed again.

Keywords: public health; mortality; mortality rate modeling; time series forecasting (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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