Multilevel Modeling for Causal Inference in Oral Health Research

Multilevel modeling (MLM) is increasingly applied in oral health research to reflect the hierarchical structure of dental data, in which teeth are nested within individuals and individuals are embedded within social and environmental contexts. By partitioning both within- and between-individual variability, MLM improves estimate precision, reduces bias, and strengthens causal interpretation, thereby enhancing the evidence base for preventive strategies, clinical practice, and public health policy. This review synthesizes recent methodological developments and ongoing challenges in the application of MLM to oral health research, with particular emphasis on causal inference. Key issues include reciprocal relationships between individual- and contextual-level factors, establishing temporality, collinearity among contextual variables, population mobility, and the selection of appropriate geographic or institutional levels. We also highlight methodological advances, such as integrating propensity score weighting within multilevel frameworks, which offer promising approaches to improving causal assessment in observational studies. Accumulating evidence demonstrates the importance of contextual determinants, such as school and neighborhood environments, in shaping oral health outcomes across the life course. However, causal inference regarding these distal exposures remains underdeveloped. Persistent threats to validity include measurement error in contextual indicators, unmeasured multilevel confounding, and limited consideration of cross-level interactions. Using a school-based example, we illustrate how food insecurity mediates the association between household income and active dental caries, highlighting the value of analytical approaches that jointly address individual- and contextual-level determinants. Overall, MLM provides a robust framework for clarifying causal pathways, improving analytical rigor, and guiding interventions that target both behavioral and structural drivers of oral health inequalities.