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Updated: Jan 11, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Forecasting the impact of Kazakhstan population growth on healthcare doctors demand
Berik Koichubekov1,2, Rozalya Begaidarova3, Nazgul Omarbekova3
1Karaganda Medical University, Gogol St. 40, Karaganda, 100008, Kazakhstan. koychubekov@qmu.kz.
Background:
A well-forecast healthcare workforce ensures that there are enough professionals to meet the growing and changing health needs of the population. The number of doctors in a country or region is influenced by various factors, including demographic and economic conditions, policies, and educational capacity. However, the most important factor is the change in population size. Aim of study is to forecast the need for doctors (physicians) in Kazakhstan over the next decade in the context of a growing population.
Methods:
A quantitative research design focused on time series analysis and forecasting was employed. This approach combines elements of descriptive and predictive designs, with the main goal of assessing future demand for medical personnel (doctors) while taking population dynamics as a key exogenous factor into account. The multi-stage approach consisted of several steps. First, key demographic indicators, such as fertility and mortality, were forecasted. These formed the basis for the population estimate, which was used as an external regressor in the ARIMAX model.
Results:
Over the next ten years, Kazakhstan is expected to experience an increase in the fertility rate of 1.5% per year on average, as well as a decrease in mortality rates for both males and females. Consequently, the population is expected to reach 23,681,890 by 2033, with 12,176,279 women and 11,505,611 men. It is forecasted that the demand for doctors will grow at an average rate of 2.7% per year in the coming years. In 2023, there were 80,795 doctors working in Kazakhstan's healthcare system. In 2033, there should be 104,887 (CI 93,330; 116,420).
Conclusions:
This article findings will contribute to staff training processes, reduce staff turnover in the industry, increase the attractiveness of the medical profession, and motivate workers in rural areas. In this context, regulatory authorities should pay more attention to planning and forecasting processes in the field of staffing.
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