OBJECTIVES: In a budget impact analysis (BIA), the population is supposed to include all patients eligible for the new intervention during the time span of decision: new incident cases and patients leaving due to death, cure, migration or other reasons, must be captured. Additionally, temporal changes in the actual number of individuals receiving the intervention must be quantified. While most of the guidelines for BIA emphasize this population dynamics aspect, recommendations on how to technically implement it are lacking. We introduce a method for implementing population dynamics in a chronic disease with multiple disease subtypes using object-oriented programming (OOP), and demonstrate its application in modelling health and budget impact of a treatment strategy for ankylosing spondylitis (AS). METHODS: A generic hierarchical patient class structure was developed to systematically organize the prevalent population and incident cohorts of patients with different disease subtypes. Using the concepts of inheritance and polymorphism in OOP, we formulated algorithms to efficiently compute health measures and resource utilization of individual patients in different classes, and to perform probabilistic sensitivity analysis. In the case study, a dynamic population model was developed to predict the burden of AS in the Dutch society over a 20-year period from January 1, 2015 onwards when a sequential treatment strategy including tumour necrosis factor antagonists was applied. Data for model parameterization were obtained from the Outcomes Assessment in AS International Study (OASIS) and literature. RESULTS: The case study demonstrated that our dynamic population modelling method offers an efficient approach to quantify annual as well as total health and costs for different decision time spans. Annual direct costs incurred by the AS patients in the Dutch Society would range from 471 to 496 million Euros between 2015-2035. CONCLUSIONS: Our method for implementation of population dynamics is efficient and generic, so it can be applied to other chronic diseases.