Woo JM, Simmonds F, Dennos A, Son MBF, Lewandowski LB, Rubinstein TB. Health Equity Implications of Missing Data Among Youths With Childhood-Onset Systemic Lupus Erythematosus: A Proof-of-Concept Study in the Childhood Arthritis and Rheumatology Research Alliance Registry.
Arthritis Care Res (Hoboken) 2023;
75:2285-2294. [PMID:
37093036 PMCID:
PMC10593908 DOI:
10.1002/acr.25136]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 03/15/2023] [Accepted: 04/18/2023] [Indexed: 04/25/2023]
Abstract
OBJECTIVE
Health disparities in childhood-onset systemic lupus erythematosus (SLE) disproportionately impact marginalized populations. Socioeconomically patterned missing data can magnify existing health inequities by supporting inferences that may misrepresent populations of interest. Our objective was to assess missing data and subsequent health equity implications among participants with childhood-onset SLE enrolled in a large pediatric rheumatology registry.
METHODS
We evaluated co-missingness of 12 variables representing demographics, socioeconomic position, and clinical factors (e.g., disease-related indices) using Childhood Arthritis and Rheumatology Research Alliance Registry childhood-onset SLE enrollment data (2015-2022; n = 766). We performed logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for missing disease-related indices at enrollment (Systemic Lupus Erythematosus Disease Activity Index 2000 [SLEDAI-2K] and/or Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index [SDI]) associated with data missingness. We used linear regression to assess the association between socioeconomic factors and SLEDAI-2K at enrollment using 3 analytic methods for missing data: complete case analysis, multiple imputation, and nonprobabilistic bias analyses, with missing values imputed to represent extreme low or high disadvantage.
RESULTS
On average, participants were missing 6.2% of data, with over 50% of participants missing at least 1 variable. Missing data correlated most closely with variables within data categories (i.e., demographic). Government-assisted health insurance was associated with missing SLEDAI-2K and/or SDI scores compared to private health insurance (OR 2.04 [95% CI 1.22, 3.41]). The different analytic approaches resulted in varying analytic sample sizes and fundamentally conflicting estimated associations.
CONCLUSION
Our results support intentional evaluation of missing data to inform effect estimate interpretation and critical assessment of causal statements that might otherwise misrepresent health inequities.
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