Research documents that Black patients experience worse general surgery outcomes than white patients in the United States. 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 "endogenous," such as a burst appendix. Our goal is to assess racial disparities for common outcomes after EGS treatment using an administrative database of hospital claims in New York, Florida, and Pennsylvania, and to understand the extent to which differences are attributable to patient-level risk factors versus hospital-level factors. To do so, we use a class of linear weighting estimators that re-weight white patients to have a similar distribution of baseline characteristics as 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.

翻译：研究文献表明，在美国，黑人患者的外科手术结果普遍比白人患者更差。本文聚焦于一个重要但较少被研究的类别：急诊普外科（EGS）病症的手术治疗，这指的是损伤为“内源性”的医疗急症，例如阑尾破裂。我们的目标是利用纽约州、佛罗里达州和宾夕法尼亚州的医院索赔行政数据库，评估EGS治疗后常见结局中的种族差异，并理解这些差异在多大程度上归因于患者层面的风险因素与医院层面的因素。为此，我们采用一类线性加权估计量，通过对白人患者进行重新加权，使其基线特征分布与黑人患者相似。该框架包含了匹配和线性回归等多种常用方法，但在控制组间不平衡、最小化外推以及减少计算时间方面，相较于这些方法具有重要优势。将这一方法应用于索赔数据后，我们发现，调整入院医院后的差异估计值明显小于仅调整患者基线特征后的估计值，这表明医院特定因素是EGS结局中种族差异的重要驱动因素。