Assessment of Missing Data on Glaucoma Severity Among Participants in the NIH All of Us Research Program of the United States
Year: 2024
Type: article
Study design: Observational Study
Abstract: PRÉCIS: There were statistically significant differences across multiple socioeconomic characteristics and self-reported barriers to care among primary glaucoma patients with severity staging data versus those missing this data in the NIH All of Us database. PURPOSE: To characterize missing data among glaucoma patients within All of Us . MATERIALS AND METHODS: We used diagnosis codes to define cohorts of primary glaucoma patients with and without severity staging specified. Descriptive analyses were conducted by presence of disease severity stage. Analysis of missing data was conducted using a set intersection plot and the Little Test of Missing Completely at Random. T tests were performed to evaluate differences. RESULTS: Of 2982 participants, 1714 (57%) did not have glaucoma severity stage specified, and 11 of 23 analyzed variables had missing data. The Little Test indicated data was not missing completely at random ( P <0.001). Significant differences existed between the 2 cohorts with respect to age, age of first glaucoma diagnosis, gender, ethnicity, education, income, insurance, history of glaucoma surgery and medication use, and answers regarding the ability to afford eyeglasses and having seen an eye care provider in the last 12 months (all P- values ≤0.01). CONCLUSION: There were significant differences between glaucoma participants with glaucoma severity stage specified versus those with unstaged disease across multiple socioeconomic characteristics and self-reported barriers to care. Glaucoma severity staging data was not missing completely at random. The unstaged cohort included higher rates of multiple underrepresented communities, which may potentially contribute to bias in ophthalmology research as participants from vulnerable populations may be disproportionately excluded from electronic health records or claims data studies where diagnosis codes with severity/staging levels are used to examine risk factors for disease, progression, and treatment efficacy.
Source: Journal of Glaucoma
Institution: University of California San Diego
Language: English
Subfield: Ophthalmology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma, G1610538956, Healthy Americas: All of Us Research Program, G2179675123, G2369080991, G2928778974, G403864660, G545054580, University of Arizona-Banner Health All of Us Research Program , G6255259697, SouthEast Enrollment Center (SEEC) , G8453215056, G9000004253, G9000006085, G9000011423, G9000011771, G9000013175, G9000021463, G9000021538, G9000032397, G9000035207, G9000041420, G9000042288, G9000051044, G9000055272, G9000063695, G9000063797, G9000072038, G9000077829, G9000079772, G9000085573, G9000092963, G9000098550, G9000128449, G9000147789, G9000200762, G9000219966, G9000222291, G9000264818, G9000279044, G9000290334, G9000303384, Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical Intervention
OpenAlex ID: W4401513909