A Markov Chain Model of Population Growth with an Application in Assessing Healthcare Demand: Kazakhstan Case Study

Background: A good understanding of the demand for health services requires not only an analysis of the current and historical volume of health care provided but also relies on accurate forecasting of trends in the future. Such trends provide invaluable information for needs assessment, resource planning, facility evaluation and policy formulation. We set the task of assessing the load on the healthcare system of Kazakhstan in the next decade using indicators of general morbidity, outpatient visits, the need for medical personnel and financial costs of medical care. Methods: This study applies a dynamic Markov chain model to forecast population dynamics and ARIMAX method for healthcare demand forecasting. Results: In the next decade, the population of Kazakhstan is expected to grow by an average of 1.4% per year, reaching 23,334,397 people by 2033. The population growth will lead to an increase in the overall morbidity of the population in absolute values. The growth rate for children will be 1.6%, for adults - 0.8% per year. In this regard, the number of visits to medical specialists will increase by approximately 1.5% per year. Therefore, the demand for internists will increase by an average of 4.7% per year. The need for labour and material resources will entail an increase in financial costs. Total costs by 2033 will increase by 11.6% compared to 2023. Conclusions: Understanding the demand for health services requires not only the analysis of past and current health data but also relies on accurate forecasting of future trends. Assessing such trends provides invaluable information for identifying needs, planning resources, evaluating facilities, and formulating policies.