Department of Epidemiology, University of Florida, Gainesville, FL 32611, USA.
Pac Symp Biocomput. 2024;29:419-432.
This study quantifies health outcome disparities in invasive Methicillin-Resistant Staphylococcus aureus (MRSA) infections by leveraging a novel artificial intelligence (AI) fairness algorithm, the Fairness-Aware Causal paThs (FACTS) decomposition, and applying it to real-world electronic health record (EHR) data. We spatiotemporally linked 9 years of EHRs from a large healthcare provider in Florida, USA, with contextual social determinants of health (SDoH). We first created a causal structure graph connecting SDoH with individual clinical measurements before/upon diagnosis of invasive MRSA infection, treatments, side effects, and outcomes; then, we applied FACTS to quantify outcome potential disparities of different causal pathways including SDoH, clinical and demographic variables. We found moderate disparity with respect to demographics and SDoH, and all the top ranked pathways that led to outcome disparities in age, gender, race, and income, included comorbidity. Prior kidney impairment, vancomycin use, and timing were associated with racial disparity, while income, rurality, and available healthcare facilities contributed to gender disparity. From an intervention standpoint, our results highlight the necessity of devising policies that consider both clinical factors and SDoH. In conclusion, this work demonstrates a practical utility of fairness AI methods in public health settings.
本研究通过利用一种新颖的人工智能（AI）公平算法——公平感知因果路径分解（FACTS），并将其应用于真实的电子健康记录（EHR）数据，量化了侵袭性耐甲氧西林金黄色葡萄球菌（MRSA）感染的健康结果差异。我们将美国佛罗里达州一家大型医疗机构的 9 年 EHR 与健康相关的社会决定因素（SDoH）进行时空关联。我们首先创建了一个因果结构图，将 SDoH 与个体在诊断侵袭性 MRSA 感染之前/之后的临床测量值、治疗方法、副作用和结果连接起来；然后，我们应用 FACTS 来量化不同因果路径（包括 SDoH、临床和人口统计学变量）导致的结果潜在差异。我们发现，在人口统计学和 SDoH 方面存在中度差异，而导致年龄、性别、种族和收入结果差异的排名最高的所有路径都包含合并症。先前的肾脏损伤、万古霉素的使用和时机与种族差异有关，而收入、农村地区和可用的医疗保健设施则导致了性别差异。从干预的角度来看，我们的结果强调了制定政策时必须同时考虑临床因素和 SDoH。总之，这项工作展示了公平 AI 方法在公共卫生领域的实际应用。
