急诊普通外科中种族差异的估算
Estimating Racial Disparities in Emergency General Surgery
AI总结：
本研究针对美国急诊普通外科的种族差异问题，采用线性加权估计量分析三州医院理赔数据，发现医院层面因素是导致术后结局种族差异的重要驱动因素。
摘要：
研究表明，在美国黑人患者的普通外科手术结局比白人患者更差。本文聚焦于一个重要但研究较少的类别：急诊普通外科（EGS）疾病的手术治疗，这类疾病指损伤为“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理赔行政数据库，评估EGS治疗后常见结局的种族差异，并明确患者层面风险因素与医院层面因素分别在多大程度上导致了这些差异。为此，我们使用一类线性加权估计量，对白人患者重新加权，使其基线特征分布与黑人患者相似。该框架包含了匹配、线性回归等许多常用方法，但在控制组间不平衡、最小化外推和减少计算时间方面相比这些方法具有显著优势。将该方法应用于理赔数据后我们发现，对收治医院进行调整后的差异估算值，远小于仅调整患者基线特征的估算值，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。
英文摘要：
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.
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