42: Generalizing a Causal Decomposition Analysis to Reduce a Health Disparity

Thomas M. Menino Convention & Exhibition Center 

Differences in health outcomes such as uncontrolled hypertension persist between social groups (class, race, etc.). Causal decomposition analysis models a hypothetical intervention while classifying confounders as just or unjust strata for differential outcomes or treatment. However, causal decomposition assumes that all variables are measured, which may not be true in observational data. Here, we generalize a causal decomposition from a study population (where all variables are measured) to a target population (with unmeasured variables). We define and identify a causal estimand and propose g-computation and weighting estimators. We apply our method to a dataset of over 30 local clinics in Maryland and Pennsylvania, some of which participated in the RICH LIFE study to reduce racial disparities in hypertension control. Our contributions are to generalize a non-randomized, stochastic intervention (a realistic yet under-explored setting) and to consider unmeasured variables and confounding in a health equity context.

Causality

Generalizability

Stochastic Intervention

Allowability

Disparity 

Health Policy Statistics Section