Long COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative
Hannah Mandel, Yun Jae Yoo, Andrea J. Allen, Sajjad Abedian, Zoe Verzani, Elizabeth W. Karlson, Lawrence C. Kleinman, Praveen C. Mudumbi, Carlos R. Oliveira, Jennifer A. Muszynski, Rachel S. Gross, Thomas W. Carton, Chun Kim, Emily Taylor, Heekyong Park, Jasmin Divers, J. Daniel Kelly, Jonathan Arnold, Carol Geary, Chengxi Zang, Kelan G. Tantisira, Kyung E. Rhee, Michael Koropsak, Sindhu Mohandas, Andrew Vasey, Abu Saleh Mohammad Mosa, Melissa Haendel, Christopher G. Chute, Shawn N. Murphy, Lisa O’Brien, Jacqueline Szmuszkovicz, Nicholas Güthe, Jorge Santana‐Bagur, Aliva De, Amanda Bogie, Katia C. Halabi, Lathika Mohanraj, Patricia A. Kinser, Samuel E. Packard, Katherine R Tuttle, Kathryn Hirabayashi, Rainu Kaushal, Emily Pfaff, Mark G. Weiner, Lorna E. Thorpe, Richard A. Moffitt
Abstract
BACKGROUND: Incidence estimates of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, also known as long COVID, have varied across studies and changed over time. We estimated long COVID incidence among adult and pediatric populations in 3 nationwide research networks of electronic health records (EHRs) participating in the RECOVER (Researching COVID to Enhance Recovery) Initiative using different classification algorithms (computable phenotypes). METHODS: This EHR-based retrospective cohort study included adult and pediatric patients with documented acute SARS-CoV-2 infection and 2 control groups: contemporary coronavirus disease 2019 (COVID-19)-negative and historical patients (2019). We examined the proportion of individuals identified as having symptoms or conditions consistent with probable long COVID within 30-180 days after COVID-19 infection (incidence proportion). Each network (the National COVID Cohort Collaborative [N3C], National Patient-Centered Clinical Research Network [PCORnet], and PEDSnet) implemented its own long COVID definition. We introduced a harmonized definition for adults in a supplementary analysis. RESULTS: Overall, 4% of children and 10%-26% of adults developed long COVID, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 1.5% in children and ranged from 5% to 6% among adults, representing a lower-bound incidence estimation based on our control groups. Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. CONCLUSIONS: Our findings indicate that preventing and mitigating long COVID remains a public health priority. Examining temporal patterns and risk factors for long COVID incidence informs our understanding of etiology and can improve prevention and management.