ORIGINAL RESEARCH article

Front. Epidemiol.

Sec. Epidemiology of Chronic Diseases and Prevention

Volume 5 - 2025 | doi: 10.3389/fepid.2025.1597799

This article is part of the Research TopicUpdating Long COVID: Mechanisms, Risk Factors, and Treatment Volume IIView all 5 articles

Estimating Long COVID-19 prevalence across definitions and forms of sample selection

Provisionally accepted
Pietro  Giorgio LovaglioPietro Giorgio Lovaglio1Fabio  BorgonovoFabio Borgonovo2*Alessandro  Manzo MargiottaAlessandro Manzo Margiotta1Mohamed  MowafyMohamed Mowafy1Marta  ColaneriMarta Colaneri2,3Alessandra  BanderaAlessandra Bandera4,5Andrea  GoriAndrea Gori2,3Amedeo  Ferdinando CapettiAmedeo Ferdinando Capetti2
  • 1Department of Statistics and Quantitative Methods, University of Milano Bicocca, Italy, Milan, Italy
  • 2Department of Infectious Disease, ASST Fatebenefratelli Sacco, Milan, Italy
  • 3Department of Biomedical and Clinical Sciences, Faculty of Medicine and Surgery, University of Milan, Milan, Lombardy, Italy
  • 4Department of Pathophysiology and Transplantation, Faculty of Medicine and Surgery, University of Milan, Milan, Lombardy, Italy
  • 5Infertility Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Lombardy, Italy

The final, formatted version of the article will be published soon.

Long COVID (LC) is a multisystem condition with prolonged symptoms persisting beyond acute SARS-CoV-2 infection. However, prevalence estimates vary widely due to differences in case definitions and sampling methodologies. This study aims to determine the prevalence of LC across different definitions and correct for selection bias using advanced statistical modeling.We conducted a retrospective, observational study at Luigi Sacco Hospital (Milan, Italy), analyzing 3,344 COVID-19 patients from two pandemic waves (2020-2021). Participants included 1,537 outpatients from the ARCOVID clinic and 1,807 hospitalized patients. LC was defined based on WHO and NICE criteria, as well as two alternative definitions: symptoms persisting at 3 and 6 months post-infection. We used a bivariate censored Probit model to account for selection bias and estimate adjusted LC prevalence.LC prevalence varied across definitions: 67.4% (WHO), 76.3% (NICE), 80.2% (3 months), and 79.6% (6 months). Adjusted prevalence estimates remained consistent across definitions. The most common symptoms were fatigue (58.6%), dyspnea (41.1%), and joint/muscle pain (39.2%).Risk factors included female sex (OR 2.165-2.379), metabolic disease (OR 1.587-1.629), and older age (40-50 years, OR 1.847). Protective factors included antiplatelets (OR 0.640-0.689), statins (OR 0.616), and hypoglycemics (OR 0.593-0.706). Vaccination, hydroxychloroquine, and antibiotics were associated with an increased risk of LC. Selection bias significantly influenced prevalence estimates, underscoring the need for robust statistical adjustments.Our findings highlight the high prevalence of LC, particularly among specific subgroups, with strong selection effects influencing outpatient participation. Differences in prevalence estimates emphasize the impact of case definitions and study designs on LC research. The identification of risk and protective factors supports targeted interventions and patient management strategies.This study provides one of the most comprehensive analyses of LC prevalence while accounting for selection bias. Our findings call for standardized LC definitions, improved epidemiological methodologies, and targeted prevention strategies. Future research should explore prospective cohorts to refine LC prevalence estimates and investigate long-term health outcomes.

Keywords: Long Covid, Incidence, Risk factors, selection bias, clinical research

Received: 25 Mar 2025; Accepted: 14 May 2025.

Copyright: © 2025 Lovaglio, Borgonovo, Margiotta, Mowafy, Colaneri, Bandera, Gori and Capetti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Fabio Borgonovo, Department of Infectious Disease, ASST Fatebenefratelli Sacco, Milan, Italy

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