Kukhareva Polina V, O'Brien Matthew J, Malone Daniel C, Kawamoto Kensaku, Gouripeddi Ramkiran, Reddy Deepika, Zhang Mingyuan, Deshmukh Vikrant G, Danks David, Facelli Julio C
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.
Department of General Internal Medicine, Northwestern University, Chicago, IL 60611, United States.
JAMIA Open. 2025 Sep 4;8(5):ooaf095. doi: 10.1093/jamiaopen/ooaf095. eCollection 2025 Oct.
Type 2 diabetes (T2D) is a growing public health burden with persistent racial and ethnic disparities. . This study assessed the completeness of social determinants of health (SdoH) data for patients with T2D in Epic Cosmos, a nationwide, cross-institutional electronic health recors (EHR) database.
The study included adults with T2D (ICD-10: E11.*) with encounters between 2022 and 2024. We analyzed 11 individual-level SDoH data elements across 5 domains-financial strain, food insecurity, housing instability, intimate partner violence, and transportation needs-and 4 components of the Social Vulnerability Index (SVI), representing neighborhood-level SDoH. Data completeness for each data element (ie, the proportion of individuals with non-missing values) was evaluated using generalized linear models, adjusting for source healthcare organization, sex, and age.
Among 12 031 927 individuals with T2D, adjusted completeness for individual-level SDoH data elements ranged from 11.2% to 31.5%, varying by data element and racial/ethnic group. American Indian or Alaska Native, Asian, Hispanic, and Native Hawaiian or Other Pacific Islander individuals had lower completeness for all individual-level SDoH compared to White individuals. In contrast, SVI data elements were available for nearly all patients since they are derived from patient addresses routinely collected in EHRs.
While SVI data elements were widely available, individual-level SDoH data elements had significant missingness, limiting their usability for secondary analyses. Racial/ethnic disparities in SDoH completeness further complicate their use.
Standardized, equitable SDoH collection is critical to close documentation gaps, reduce disparities, and enable accurate, bias-resistant analyses in T2D care.
2型糖尿病(T2D)是一个日益加重的公共卫生负担,存在持续的种族和民族差异。本研究评估了全国性跨机构电子健康记录(EHR)数据库Epic Cosmos中T2D患者健康的社会决定因素(SdoH)数据的完整性。
该研究纳入了2022年至2024年间有就诊记录的成年T2D患者(国际疾病分类第十版:E11.*)。我们分析了5个领域的11个个体层面的SDoH数据元素——经济压力、粮食不安全、住房不稳定、亲密伴侣暴力和交通需求——以及社会脆弱性指数(SVI)的4个组成部分,代表社区层面的SDoH。使用广义线性模型评估每个数据元素的数据完整性(即无缺失值个体的比例),并对来源医疗机构、性别和年龄进行调整。
在12031927名T2D患者中,个体层面SDoH数据元素的调整后完整性从11.2%到31.5%不等,因数据元素和种族/民族群体而异。与白人个体相比,美国印第安人或阿拉斯加原住民、亚洲人、西班牙裔以及夏威夷原住民或其他太平洋岛民个体的所有个体层面SDoH完整性较低。相比之下,几乎所有患者都有SVI数据元素,因为它们来自EHR中常规收集的患者地址。
虽然SVI数据元素广泛可用,但个体层面的SDoH数据元素存在大量缺失,限制了它们在二次分析中的可用性。SDoH完整性方面的种族/民族差异进一步使其使用复杂化。
标准化、公平的SDoH收集对于弥合记录差距、减少差异以及在T2D护理中进行准确、无偏差分析至关重要。