Mixtures-of-Network Modeling to Improve Measurement of Structural Racism in Disparity Research

Oregon Convention Center 

Racial inequities in health outcomes in the United States are actionable. There is urgent need for studies to investigate and then address these. At the same time, the use of "race" as an explanatory variable in such studies is in question, considering that it is a social rather than biological construct. Accordingly, the field has moved towards studying the primary factor that underlies racial differences - structural racism. Structural racism exposure as a construct is vital to capture both for explanatory purposes and as a target for intervention. Structural racism domains have been elucidated in the literature, making the measurement task partly amenable to latent variable modeling. This paper argues that additional considerations are needed, however, including mutual reinforcement between domains and contextual specificity by place and time in the life course. We propose models unifying mixtures and network structure to accommodate these. Methods are illustrated using publicly available data on structural factors spanning the US and multiple time periods. The proposed methods aim to equip researchers with improved measures to elucidate and address health disparities.