OBJECTIVES/GOALS: Health inequities represent complex structural and systematic processes that lead to disparate outcomes for populations or subgroups within populations. This project aims to improve the available structural and systematic approaches to the study of such inequities at the population level. METHODS/STUDY POPULATION: Using examples from diabetes research, two critical factors that may impact the validity or utility of health equity models will be examined; and proposed methodological approaches to offsetting potentially resulting biases will be offered. The factors include: (1) inequitably missing and misclassified data in large datasets and (2) the presumed positioning of socially constructed variables such as race, ethnicity, and gender within modeled structural and systematic mechanisms. This examination intersects theories and praxis in epidemiological modeling and health equity promotion with the goal of advancing rigorous, equity-focused epidemiological methods. RESULTS/ANTICIPATED RESULTS: Inequitably missing and misclassified data are generally expected to obscure inequities. Treatment of missing or misclassified data as informative measures of inequity is expected to partially offset this bias. The implicitly modeled components of socially constructed variables are expected to be non-uniform across structural and systematic mechanisms of inequity. Models that apply these variables as informatively heterogeneous constructs, using multi-phase analyses to test modeling assumptions and to assess intersectionality, may provide better context about the mechanisms by which inequity has been distributed and, perhaps, by which equity may be achieved. DISCUSSION/SIGNIFICANCE: Equitable epidemiological methods are essential to the advancement of evidence-based health equity on the population level. Potential structural or systematic inequities in large-scale datasets and traditional data analyses should be thoughtfully reviewed through a health equity lens.