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Prodromal symptoms of rheumatoid arthritis in a primary care database: variation by ethnicity and socioeconomic status

医学 社会经济地位 民族 优势比 人口学 类风湿性关节炎 逻辑回归 横断面研究 物理疗法 内科学 儿科 人口 环境卫生 人类学 病理 社会学
作者
Alexander d’Elia,Aliaksandra Baranskaya,Shamil Haroon,B. J. Hammond,Nicola J. Adderley,Krishnarajah Nirantharakumar,Joht Singh Chandan,Marie Falahee,Karim Raza
出处
期刊:Rheumatology [Oxford University Press]
标识
DOI:10.1093/rheumatology/keae157
摘要

Abstract Objectives To assess whether prodromal symptoms of RA, as recorded in the Clinical Practice Research Datalink Aurum (CPRD) database of English primary care records, differ by ethnicity and socioeconomic status. Methods A cross-sectional study to determine the coding of common symptoms (≥0.1% in the sample) in the 24 months preceding RA diagnosis in CPRD Aurum, recorded between 1 January 2004 and 1 May 2022. Eligible cases were adults with a code for RA diagnosis. For each symptom, a logistic regression was performed with the symptom as dependent variable, and ethnicity and socioeconomic status as independent variables. Results were adjusted for sex, age, BMI and smoking status. White ethnicity and the highest socioeconomic quintile were comparators. Results In total, 70 115 cases were eligible for inclusion, of which 66.4% were female. Twenty-one symptoms were coded in >0.1% of cases so were included in the analysis. Patients of South Asian ethnicity had higher frequency of codes for several symptoms, with the largest difference by odds ratio being muscle cramps (1.71, 99.76 % confidence interval 1.44–2.57) and shoulder pain (1.44, 1.25–1.66). Patients of Black ethnicity had higher prevalence of several codes including unintended weight loss (2.02, 1.25–3.28) and ankle pain (1.51, 1.02–2.23). Low socioeconomic status was associated with morning stiffness (1.74, 1.08–2.80) and falls (1.37, 2.03–1.82) Conclusion There are significant differences in coded symptoms between demographic groups, which must be considered in clinical practice in diverse populations and to avoid algorithmic bias in prediction tools derived from routinely collected healthcare data.

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