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Comorbidity Patterns Before and After Juvenile Idiopathic Arthritis Diagnosis

In: AI, Society and Digital Transformation

Author

Listed:
  • Kazi Arman Ahmed

    (Oklahoma State University, School of Industrial Engineering and Management)

  • Hamid Hadidi

    (Oklahoma State University, Department of Management Science & Information Systems)

  • Mohammad Fili

    (Oklahoma State University, School of Industrial Engineering and Management)

  • Dursun Delen

    (Oklahoma State University, Department of Management Science & Information Systems)

  • Guiping Hu

    (Oklahoma State University, School of Industrial Engineering and Management)

  • Lizhi Wang

    (Oklahoma State University, School of Industrial Engineering and Management)

Abstract

Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disease that begins before the age of 16, characterized by persistent joint pain, swelling, and stiffness. In this study, we utilized the Oracle Real-World Data (formerly Cerner Real-World Data), to examine the demographic characteristics of JIA patients and to identify patterns of comorbidities both before and after diagnosis. Our analysis revealed that individuals identified as Black or African American are diagnosed with JIA at significantly older ages compared to other racial groups, highlighting a critical disparity given the importance of early detection for better clinical outcomes. Pre-diagnostic comorbidity analysis indicated that the presence of musculoskeletal pain, particularly when occurring alongside joint disorders, abdominal pain, or respiratory symptoms, could serve as early clinical indicators for JIA. Post-diagnosis, the comorbidity burden shifted from isolated musculoskeletal complaints to broader multi-system involvement, with joint, skin, and respiratory conditions becoming predominant.

Suggested Citation

  • Kazi Arman Ahmed & Hamid Hadidi & Mohammad Fili & Dursun Delen & Guiping Hu & Lizhi Wang, 2026. "Comorbidity Patterns Before and After Juvenile Idiopathic Arthritis Diagnosis," Lecture Notes in Operations Research, in: Xiaolei Xie & Kejia Hu & Guiping Hu & Weiwei Chen & Robin Qiu (ed.), AI, Society and Digital Transformation, pages 276-288, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-13116-4_22
    DOI: 10.1007/978-3-032-13116-4_22
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