Long COVID Incidence in a Large US Ambulatory Electronic Health Record System
- PMID: 37073410
- DOI: 10.1093/aje/kwad095
Long COVID Incidence in a Large US Ambulatory Electronic Health Record System
Abstract
Estimates of the prevalence of long-term symptoms of coronavirus disease 2019 (COVID-19), referred to as long COVID, vary widely. This retrospective cohort study describes the incidence of long COVID symptoms 12-20 weeks postdiagnosis in a US ambulatory care setting and identifies potential risk factors. We identified patients with and without a diagnosis of or positive test for COVID-19 between January 1, 2020, and March 13, 2022, in the Veradigm (Veradigm LLC, Chicago, Illinois) electronic health record database. We captured data on patient demographic characteristics, clinical characteristics, and COVID-19 comorbidity in the 12-month baseline period. We compared long COVID symptoms between matched cases and controls 12-20 weeks after the index date (COVID-19 diagnosis date (cases) or median visit date (controls)). Multivariable logistic regression was used to examine associations between baseline COVID-19 comorbid conditions and long COVID symptoms. Among 916,894 patients with COVID-19, 14.8% had at least 1 long COVID symptom in the 12-20 weeks postindex as compared with 2.9% of patients without documented COVID-19. Commonly reported symptoms were joint stiffness (4.5%), cough (3.0%), and fatigue (2.7%). Among patients with COVID-19, the adjusted odds of long COVID symptoms were significantly higher among patients with a baseline COVID-19 comorbid condition (odds ratio = 1.91, 95% confidence interval: 1.88, 1.95). In particular, prior diagnosis of cognitive disorder, transient ischemic attack, hypertension, or obesity was associated with higher odds of long COVID symptoms.
Keywords: COVID-19; SARS-CoV-2; United States; coronavirus disease 2019; long COVID; postacute sequalae; post–COVID-19 conditions; severe acute respiratory syndrome coronavirus 2.
© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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