Skip to main navigation Skip to search Skip to main content

Estimation of prevalence of autoimmune diseases in the United States using electronic health record data

  • Aaron H. Abend
  • , Ingrid He
  • , Neil Bahroos
  • , Stratos Christianakis
  • , Ashley B. Crew
  • , Leanna M. Wise
  • , Gloria P. Lipori
  • , Xing He
  • , Shawn N. Murphy
  • , Christopher D. Herrick
  • , Jagannadha Avasarala
  • , Mark G. Weiner
  • , Jacob S. Zelko
  • , Erica Matute-Arcos
  • , Mark Abajian
  • , Philip R.O. Payne
  • , Albert M. Lai
  • , Heath A. Davis
  • , Asher A. Hoberg
  • , Chris E. Ortman
  • Amit D. Gode, Bradley W. Taylor, Kristen I. Osinski, Damian N. Di Florio, Noel R. Rose, Frederick W. Miller, George C. Tsokos, De Lisa Fairweather

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

BACKGROUND. Previous epidemiologic studies of autoimmune diseases in the US have included a limited number of diseases or used metaanalyses that rely on different data collection methods and analyses for each disease. METHODS. To estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from 6 large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time. RESULTS. Our findings indicate that over 15 million people, or 4.6% of the US population, have been diagnosed with at least 1 autoimmune disease from January 1, 2011, to June 1, 2022, and 34% of those are diagnosed with more than 1 autoimmune disease. As expected, females (63% of those with autoimmune disease) were almost twice as likely as males to be diagnosed with an autoimmune disease. We identified the top 20 autoimmune diseases based on prevalence and according to sex and age. CONCLUSION. Here, we provide, for what we believe to be the first time, a large-scale prevalence estimate of autoimmune disease in the US by sex and age. FUNDING. Autoimmune Registry Inc., the National Heart Lung and Blood Institute, the National Center for Advancing Translational Sciences, the Intramural Research Program of the National Institute of Environmental Health Sciences.

Original languageEnglish
Article numbere178722
JournalJournal of Clinical Investigation
Volume135
Issue number4
DOIs
StatePublished - Feb 17 2025

Bibliographical note

Publisher Copyright:
Copyright © 2024, Abend et al.

Funding

Support for the project was provided by the University of Southern California, University of Florida, Mass General Brigham, and the Autoimmune Registry Inc., a 501(c) (3) nonprofit organization. Research reported in this publication was supported by the National Institutes of Health (NIH) National Center for Advancing Translational Sciences under Award Number UM1TR004403 to HAD, TL1 TR002380 to DND and DF, and National Heart, Lung and Blood Institute grant R01 HL164520 to DF. This research was also supported in part by the Intramural Research Program of the NIH National Institute of Environmental Health Sciences to FWM. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We especially acknowledge the late Dr. Noel R. Rose, MD, PhD, who guided the initial research, and whose dedication to scientific inquiry inspired this work.

FundersFunder number
University of Southern California
National Institutes of Health (NIH)
NIH/National Institute of Environmental Health Sciences
Massachusetts General Hospital
National Institutes of Health/National Institute of Environmental Health Sciences
Autoimmune Registry Inc.
National Center for Advancing Translational Sciences (NCATS)UM1TR004403, TL1 TR002380
National Heart, Lung, and Blood Institute (NHLBI)R01 HL164520

    ASJC Scopus subject areas

    • General Medicine

    Fingerprint

    Dive into the research topics of 'Estimation of prevalence of autoimmune diseases in the United States using electronic health record data'. Together they form a unique fingerprint.

    Cite this