Publications
1.
Wuller, Shannon; Singer, Nora G.; Lewis, Colby; Karlson, Elizabeth W.; Schulert, Grant S.; Goldman, Jason D.; Hadlock, Jennider; Arnold, Jonathan; Hirabayashi, Kathryn; Stiles, Lauren E.; Kleinman, Lawrence C.; Cowell, Lindsay G.; Hornig, Mady; Hall, Margaret A.; Weiner, Mark G.; Koropsak, Michael; Lamendola-Essel, Michelle F.; Kenney, Rachel; Moffitt, Richard A.; Abedian, Sajjad; Esquenazi-Karonika, Shari; Johnson, Steven G.; Stroebel, Stephenson; Wallace, Zachary S.; Costenbader, Karen H.
Severity of acute SARS-CoV-2 infection and risk of new-onset autoimmune disease: A RECOVER initiative study in nationwide U.S. cohorts Journal Article
In: PLoS One, vol. 20, iss. 6, pp. e0324513, 2025.
Abstract | Links | BibTeX | Tags: autoimmune disease, COVID-19
@article{nokey,
title = {Severity of acute SARS-CoV-2 infection and risk of new-onset autoimmune disease: A RECOVER initiative study in nationwide U.S. cohorts },
author = {Shannon Wuller and Nora G. Singer and Colby Lewis and Elizabeth W. Karlson and Grant S. Schulert and Jason D. Goldman and Jennider Hadlock and Jonathan Arnold and Kathryn Hirabayashi and Lauren E. Stiles and Lawrence C. Kleinman and Lindsay G. Cowell and Mady Hornig and Margaret A. Hall and Mark G. Weiner and Michael Koropsak and Michelle F. Lamendola-Essel and Rachel Kenney and Richard A. Moffitt and Sajjad Abedian and Shari Esquenazi-Karonika and Steven G. Johnson and Stephenson Stroebel and Zachary S. Wallace and Karen H. Costenbader},
doi = {10.1371/journal.pone.0324513},
year = {2025},
date = {2025-06-04},
urldate = {2025-06-04},
journal = {PLoS One},
volume = {20},
issue = {6},
pages = {e0324513},
abstract = {SARS-CoV-2 infection has been associated with increased autoimmune disease risk. Past studies have not aligned regarding the most prevalent autoimmune diseases after infection, however. Furthermore, the relationship between infection severity and new autoimmune disease risk has not been well examined. We used RECOVER's electronic health record (EHR) networks, N3C, PCORnet, and PEDSnet, to estimate types and frequency of autoimmune diseases arising after SARS-CoV-2 infection and assessed how infection severity related to autoimmune disease risk. We identified patients of any age with SARS-CoV-2 infection between April 1, 2020 and April 1, 2021, and assigned them to a World Health Organization COVID-19 severity category for adults or the PEDSnet acute COVID-19 illness severity classification system for children (<age 21). We collected baseline covariates from the EHR in the year pre-index infection date and followed patients for 2 years for new autoimmune disease, defined as ≥ 2 new ICD-9, ICD-10, or SNOMED codes in the same concept set, starting >30 days after SARS-CoV-2 infection index date and occurring ≥1 day apart. We calculated overall and infection severity-stratified incidence ratesper 1000 person-years for all autoimmune diseases. With least severe COVID-19 severity as reference, survival analyses examined incident autoimmune disease risk. The most common new-onset autoimmune diseases in all networks were thyroid disease, psoriasis/psoriatic arthritis, and inflammatory bowel disease. Among adults, inflammatory arthritis was the most common, and Sjögren's disease also had high incidence. Incident type 1 diabetes and hematological autoimmune diseases were specifically found in children. Across networks, after adjustment, patients with highest COVID-19 severity had highest risk for new autoimmune disease vs. those with least severe disease (N3C: adjusted Hazard Ratio, (aHR) 1.47 (95%CI 1.33-1.66); PCORnet aHR 1.14 (95%CI 1.02-1.26); PEDSnet: aHR 3.14 (95%CI 2.42-4.07)]. Overall, severe acute COVID-19 was most strongly associated with autoimmune disease risk in three EHR networks.},
keywords = {autoimmune disease, COVID-19},
pubstate = {published},
tppubtype = {article}
}
SARS-CoV-2 infection has been associated with increased autoimmune disease risk. Past studies have not aligned regarding the most prevalent autoimmune diseases after infection, however. Furthermore, the relationship between infection severity and new autoimmune disease risk has not been well examined. We used RECOVER's electronic health record (EHR) networks, N3C, PCORnet, and PEDSnet, to estimate types and frequency of autoimmune diseases arising after SARS-CoV-2 infection and assessed how infection severity related to autoimmune disease risk. We identified patients of any age with SARS-CoV-2 infection between April 1, 2020 and April 1, 2021, and assigned them to a World Health Organization COVID-19 severity category for adults or the PEDSnet acute COVID-19 illness severity classification system for children (<age 21). We collected baseline covariates from the EHR in the year pre-index infection date and followed patients for 2 years for new autoimmune disease, defined as ≥ 2 new ICD-9, ICD-10, or SNOMED codes in the same concept set, starting >30 days after SARS-CoV-2 infection index date and occurring ≥1 day apart. We calculated overall and infection severity-stratified incidence ratesper 1000 person-years for all autoimmune diseases. With least severe COVID-19 severity as reference, survival analyses examined incident autoimmune disease risk. The most common new-onset autoimmune diseases in all networks were thyroid disease, psoriasis/psoriatic arthritis, and inflammatory bowel disease. Among adults, inflammatory arthritis was the most common, and Sjögren's disease also had high incidence. Incident type 1 diabetes and hematological autoimmune diseases were specifically found in children. Across networks, after adjustment, patients with highest COVID-19 severity had highest risk for new autoimmune disease vs. those with least severe disease (N3C: adjusted Hazard Ratio, (aHR) 1.47 (95%CI 1.33-1.66); PCORnet aHR 1.14 (95%CI 1.02-1.26); PEDSnet: aHR 3.14 (95%CI 2.42-4.07)]. Overall, severe acute COVID-19 was most strongly associated with autoimmune disease risk in three EHR networks.
2.
Abend, Aaron H.; He, Ingrid; Bahroos, Neil; Christianakis, Stratos; Crew, Ashley B.; Wise, Leanna M.; Lipori, Gloria P.; He, Xing; Murphy, Shawn N.; Herrick, Christopher D.; Avasarala, Jagannadha; Weiner, Mark G.; Zelko, Jacob S.; Matute-Arcos, Erica; Abajian, Mark; Payne, Philip Ro; Lai, Albert M.; Davis, Heath A.; Hoberg, Asher A.; Ortman, Chris E.; Gode, Amit D.; Taylor, Bradley W.; Osinski, Kristen I.; Florio, Damian N. Di; Rose, Noel R.; Miller, Fredrick W.; Tsokos, George C.; Fairweather, DeLisa
Estimation of prevalence of autoimmune diseases in the United States using electronic health record data Journal Article
In: The Journal of Clinical Investigation, vol. 135, iss. 4, pp. e178722, 2024.
Abstract | Links | BibTeX | Tags: autoimmune disease
@article{nokey,
title = {Estimation of prevalence of autoimmune diseases in the United States using electronic health record data},
author = {Aaron H. Abend and Ingrid He and Neil Bahroos and Stratos Christianakis and Ashley B. Crew and Leanna M. Wise and Gloria P. Lipori and Xing He and Shawn N. Murphy and Christopher D. Herrick and Jagannadha Avasarala and Mark G. Weiner and Jacob S. Zelko and Erica Matute-Arcos and Mark Abajian and Philip Ro Payne and Albert M. Lai and Heath A. Davis and Asher A. Hoberg and Chris E. Ortman and Amit D. Gode and Bradley W. Taylor and Kristen I. Osinski and Damian N. Di Florio and Noel R. Rose and Fredrick W. Miller and George C. Tsokos and DeLisa Fairweather},
doi = {10.1172/JCI178722},
year = {2024},
date = {2024-12-12},
journal = {The Journal of Clinical Investigation},
volume = {135},
issue = {4},
pages = {e178722},
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.CONCLUSIONHere, 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.},
keywords = {autoimmune disease},
pubstate = {published},
tppubtype = {article}
}
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.CONCLUSIONHere, 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.
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.CONCLUSIONHere, 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.