Long COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative.
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 researc...
| Publicado en: | Clinical Infectious Diseases Vol. 80; no. 6; pp. 1247 - 1262 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
6/15/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186751817&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186751817 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10584838 46X jtl: Clinical Infectious Diseases issn: 10584838 maglogo: N pubinfo: dt: 6/15/2025 vid: 80 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 186751817 186751817 186751817 10.1093/cid/ciaf046 186751817 ppf: 1247 ppct: 15 formats: tig: atl: Long COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative. aug: au: Mandel, Hannah Yoo, Yun J Allen, Andrea J Abedian, Sajjad Verzani, Zoe Karlson, Elizabeth W Kleinman, Lawrence C Mudumbi, Praveen C Oliveira, Carlos R Muszynski, Jennifer A Gross, Rachel S Carton, Thomas W Kim, C Taylor, Emily Park, Heekyong Divers, Jasmin Kelly, J Daniel Arnold, Jonathan Geary, Carol Reynolds Zang, Chengxi affil: Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA sug: subj: Post-Acute COVID-19 Syndrome Epidemiology Post-Acute COVID-19 Syndrome Epidemiology Post-Acute COVID-19 Syndrome Trends COVID-19 Complications Human Funding Source Male Female Infant Child, Preschool Child Adolescence Adult Middle Age Aged Retrospective Design Electronic Health Records Record Review Prospective Studies Post-Acute COVID-19 Syndrome Symptoms Phenotype SARS-CoV-2 Descriptive Statistics Post-Acute COVID-19 Syndrome Prevention and Control Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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