National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina.
National Institute of Arthritis and Musculoskeletal and Skin Diseases, Bethesda, Maryland.
Arthritis Care Res (Hoboken). 2023 Nov;75(11):2285-2294. doi: 10.1002/acr.25136. Epub 2023 May 30.
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.
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.
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.
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.
儿童发病系统性红斑狼疮（SLE）中的健康差异不成比例地影响到边缘化人群。社会经济模式化的缺失数据可能会通过支持可能对目标人群产生误解的推断，从而加剧现有的健康不平等现象。我们的目的是评估在参加大型儿科风湿病学注册中心的儿童发病 SLE 患者中缺失数据及其对后续健康公平性的影响。
我们使用儿童关节炎和风湿病研究联盟注册中心的儿童发病 SLE 登记数据（2015-2022 年；n=766）评估了 12 个变量的共缺失情况，这些变量代表人口统计学、社会经济地位和临床因素（例如疾病相关指标）。我们进行逻辑回归，以计算在登记时缺失疾病相关指标（SLE 疾病活动指数 2000[SLEDAI-2K]和/或系统性红斑狼疮国际合作临床/美国风湿病学会损害指数 [SDI]）的几率比（OR）和 95%置信区间（95%CI），这些缺失与数据缺失有关。我们使用线性回归，使用缺失数据的 3 种分析方法（完全案例分析、多重插补和非概率偏差分析），来评估社会经济因素与登记时 SLEDAI-2K 的关系，其中缺失值被用来代表极端的低或高劣势。
平均而言，参与者缺失了 6.2%的数据，超过 50%的参与者缺失了至少 1 个变量。缺失数据与数据类别内的变量最密切相关（即人口统计学）。与私人医疗保险相比，政府资助的医疗保险与 SLEDAI-2K 和/或 SDI 评分的缺失相关（OR 2.04[95%CI 1.22,3.41]）。不同的分析方法导致分析样本量不同，并且对估计的关联产生了根本冲突的估计。
我们的结果支持对缺失数据进行有针对性的评估，以告知效应估计解释，并对可能对健康不平等现象产生误解的因果陈述进行严格评估。
