Division of Preventive Medicine, Department of Medicine, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama.
Department of Biostatistics, School of Public Health, The University of Alabama at Birmingham, Birmingham, Alabama.
Am J Prev Med. 2022 Jul;63(1 Suppl 1):S103-S108. doi: 10.1016/j.amepre.2022.01.034.
Including race as a biological construct in risk prediction models may guide clinical decisions in ways that cause harm and widen racial disparities. This study reports on using race versus social determinants of health (SDoH) in predicting the associations between cardiometabolic disease severity (assessed using cardiometabolic disease staging) and COVID-19 hospitalization.
Electronic medical record data on patients with a positive COVID-19 polymerase chain reaction test in 2020 and a previous encounter in the electronic medical record where cardiometabolic disease staging clinical data (BMI, blood glucose, blood pressure, high-density lipoprotein cholesterol, and triglycerides) were available from 2017 to 2020, were analyzed in 2021. Associations between cardiometabolic disease staging and COVID-19 hospitalization adding race and SDoH (individual and neighborhood level [e.g., Social Vulnerability Index]) in different models were examined. Area under the curve was used to assess predictive performance.
A total of 2,745 patients were included (mean age of 58 years, 59% female, 47% Black). In the cardiometabolic disease staging model, area under the curve was 0.767 vs 0.777 when race was included. Adding SDoH to the cardiometabolic model improved the area under the curve to 0.809 (p<0.001), whereas the addition of SDoH and race increased the area under the curve to 0.811. In race-stratified models, the area under the curve for non-Hispanic Blacks was 0.781, whereas the model for non-Hispanic Whites performed better with an area under the curve of 0.821.
Cardiometabolic disease staging was predictive of hospitalization after a positive COVID-19 test. Adding race did not markedly increase the predictive ability; however, adding SDoH to the model improved the area under the curve to ≥0.80. Future research should include SDoH with biological variables in prediction modeling to capture social experience of race.
将种族作为生物构建体纳入风险预测模型中，可能会以导致伤害和扩大种族差异的方式指导临床决策。本研究报告了使用种族与健康社会决定因素（SDoH）来预测心血管代谢疾病严重程度（使用心血管代谢疾病分期评估）与 COVID-19 住院之间的关联。
对 2020 年 COVID-19 聚合酶链反应检测呈阳性的患者的电子病历数据以及 2017 年至 2020 年电子病历中可获得心血管代谢疾病分期临床数据（BMI、血糖、血压、高密度脂蛋白胆固醇和甘油三酯）的先前就诊记录进行了分析。在不同模型中，通过添加种族和 SDoH（个体和邻里层面[例如，社会脆弱性指数]）来检查心血管代谢疾病分期与 COVID-19 住院之间的关联。使用曲线下面积来评估预测性能。
共纳入 2745 例患者（平均年龄 58 岁，59%为女性，47%为黑人）。在心血管代谢疾病分期模型中，当纳入种族时，曲线下面积为 0.767 比 0.777。将 SDoH 添加到心血管代谢模型中可将曲线下面积提高到 0.809（p<0.001），而 SDoH 和种族的加入可将曲线下面积提高到 0.811。在种族分层模型中，非西班牙裔黑人的曲线下面积为 0.781，而非西班牙裔白人的模型表现更好，曲线下面积为 0.821。
心血管代谢疾病分期可预测 COVID-19 检测呈阳性后的住院治疗。加入种族并没有明显增加预测能力；然而，将 SDoH 添加到模型中可将曲线下面积提高到≥0.80。未来的研究应将 SDoH 与生物变量纳入预测模型中，以捕捉种族的社会经历。
