Estimating racial disparities in emergency general surgery

Abstract Research documents that Black patients experience worse general surgery outcomes than White patients in the U.S. In this paper, we focus on an important but less-examined category: the surgical treatment of emergency general surgery (EGS) conditions, which refers to medical emergencies where the injury is internal, such as a burst appendix. Our goal is to assess racial disparities in outcomes after EGS treatment using administrative data. We also seek to understand the extent to which differences are attributable to patient-level risk factors vs. hospital-level factors, as well as to the decision to operate on EGS patients. To do so, we develop a class of linear weighting estimators that reweight White patients to have a similar distribution of baseline characteristics to Black patients. This framework nests many common approaches, including matching and linear regression, but offers important advantages over these methods in terms of controlling imbalance between groups, minimizing extrapolation, and reducing computation time. Applying this approach to the claims data, we find that disparities estimates that adjust for the admitting hospital are substantially smaller than estimates that adjust for patient baseline characteristics only, suggesting that hospital-specific factors are important drivers of racial disparities in EGS outcomes.