State-transition and simulation-based modeling approaches for simulating the progression of dental caries: a scoping review

Dental caries is a common chronic disease in children, influenced by biological, behavioral, and environmental factors, and modeling approaches provide a structured way to simulate disease progression, evaluate preventive or therapeutic interventions, assess economic outcomes, and inform policy. Despite increasing use of decision-analytic and simulation-based frameworks in pediatric dental caries, a comprehensive overview of model types, structures, and applications is lacking. The objective of this scoping review is to identify and describe state-transition and simulation-based modeling approaches used to simulate the progression of dental caries in children, including models applied to the evaluation of preventive or therapeutic interventions and economic outcomes, and to summarize their structural characteristics, applications, and methodological features. This review includes studies of children and adolescents (birth to 18 years) from any health status or risk profile and in any geographic or clinical setting. Eligible studies described, developed, or applied state-transition or simulation-based models of dental caries progression, while studies limited to predictive, statistical, or cross-sectional approaches, as well as reviews and commentaries, were excluded. MEDLINE (PubMed) and Scopus were searched using a structured strategy, limited to English-language publications. Studies were screened and charted key model features independently by two authors. A total of 43 studies were included, most using state-transition approaches, primarily Markov cohort models and Markov microsimulations. Model structures ranged from simple caries presence to multi-state models incorporating enamel-dentin progression, ICDAS stages, or restorative pathways. These findings highlight how structural and methodological choices in caries modeling determine the scope of addressable research questions and underscore the need for standardized health-state classifications. This review was registered in the Open Science Framework, DOI 10.17605/OSF.IO/6BT7K.