Structural Racism and Quantitative Causal Inference: A Life Course Mediation Framework for Decomposing Racial Health Disparities

Abstract

Quantitative studies of racial health disparities often use static measures of self-reported race and conventional regression estimators, which critics argue is inconsistent with social-constructivist theories of race, racialization, and racism. We demonstrate an alternative counterfactual approach to explain how multiple racialized systems dynamically shape health over time, examining racial inequities in cardiometabolic risk in the National Longitudinal Study of Adolescent to Adult Health. This framework accounts for the dynamics of time-varying confounding and mediation that is required in operationalizing a "race" variable as part of a social process (racism) rather than a separable, individual characteristic. We decompose the observed disparity into three types of effects: a controlled direct effect ("unobserved racism"), proportions attributable to interaction ("racial discrimination"), and pure indirect effects ("emergent discrimination"). We discuss the limitations of counterfactual approaches while highlighting how they can be combined with critical theories to quantify how interlocking systems produce racial health inequities.

Keywords: g-computation; life course; mediation; racial health disparities; racism.

Figure 1.

Directed-acyclic graph describing how the connection between Race and Health ($Y$) is mediated by material deprivation, denoted $M$. Includes confounders of $M\to Y$ denoted $L$, and Arrow (b) includes an interaction between Race and $M$ in influencing $Y$.

Figure 2.

Directed-acyclic graph describing the relationship between race and cardio-metabolic risk by adulthood. Note: For visual clarity, two pieces of information are suppressed here compared to Figure 1: 1) Multiple observed mediators measured at the same time point are suppressed to a single node and set of arrows (e.g. ${\mathit{M}}^{\left(1\right)}$) and 2) all arrows except those originating from Race contain an interaction with Race.

Figure 3.

Decomposition estimates for the total Black-white disparity in cardio-metabolic risk Notes: The x-axis indicates the counterfactual change in the normalized cardio-metabolic risk index (i.e. standard deviations) attributable to that pathway had the population racialized as Black instead been treated as the population racialized as white by all mediating systems.