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. 2025 Aug 3;15(1):28299.
doi: 10.1038/s41598-025-10120-z.

A multinational cross-sectional study on the prevalence and predictors of long COVID across 33 countries

Mohamed Sayed Zaazouee  1 , Eman Ayman Nada  2 , Mohammed Al-Kafarna  3 , Ahmed Shaheen  4 , Shivabalan Kathavarayan Ramu  5 , Abdelrahman H Hafez  6 , Sajeda Ghassan Matar  7 , Ahmed Assar  8 , Mohamed Elshennawy  9 , Hazem Abu El-Enien  10 , Hala Jamal Redwan  11 , Sarah Makram Elsayed  12 , Maha Jabir Omran  13 , Omar Hammam Salloum  14 , Hossam Almadhoon  15 , Mohamed Mamdouh  16 , Engy A Wahsh  17 , Anas Zakarya Nourelden  6 , Alaa Ahmed Elshanbary  4 , Walid Abdel-Aziz  9 , Hivan Haji Rashid  18 , Iman Basheti  19   20 , Khaled Mohamed Ragab  21 , Ahmed Taher Masoud  22 , Abdelrahman I Abushouk  23 ; Long-COVID International Team (LCIT)
Collaborators, Affiliations

A multinational cross-sectional study on the prevalence and predictors of long COVID across 33 countries

Mohamed Sayed Zaazouee et al. Sci Rep. .

Abstract

The symptoms of long COVID (LC) can be debilitating and may be associated with anxiety, social stigma, and quality of life deterioration. Identifying patients at risk of LC is important to offer follow-up care and plan population-level public health measures. The current multinational study aimed to assess the prevalence and predictors of LC in the general population. We conducted an online, multinational, cross-sectional survey between April 2022 and January 2023, targeting participants 18 years and older with a previously confirmed COVID-19 infection. We used convenience sampling to recruit participants through an online Google form. We collected demographic data, past medical history, infection details, post-COVID-19 symptoms, and quality of life. Responses were then translated into English. LC was defined as per the World Health Organization. A single-variable analysis was conducted to identify factors significantly associated with LC development. Following the removal of multicollinear variables, a generalized linear model was established to estimate the contribution of different predictors to LC occurrence. A total of 11,801 respondents from 33 countries were included in the analysis. The mean age for participants was 32.7 ± 12.8 years, with 61% being females. BMI averaged 25.2 ± 4.8 across participants, and 14.8% of them were smokers. Seventy-eight percent of participants reported receiving the COVID-19 vaccine. Respondents with PCR-confirmed COVID-19 were then categorized into those with LC (N = 2335, 19.8%) and without LC (N = 9466 individuals, 80.2%). Our model identified 25 significant predictors. The predictors of higher LC risk included ICU admission (OR 2.08; 95% CI 1.36, 3.18; P = 0.001), female sex (OR 1.8; 95% CI 1.61, 2.02; P < 0.001), fatigue during the infection (OR 1.6; 95% CI 1.43, 1.78; P < 0.001), identifying as Hispanic (OR 1.53; 95% CI 1.26, 1.85; P < 0.001), and pre-existing gastrointestinal disease (OR 1.48; 95% CI 1.22, 1.8; P < 0.001). In conclusion, we identified key LC predictors, including ICU admission, female sex, and acute fatigue as primary risk factors, while African American and Asian ethnicities and receiving even one dose of vaccination demonstrated protective effects.

Keywords: Covid-19; Long Covid; Multi-national; Re-infection; Vaccination.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval: The study protocol was approved by the Institutional Review Board of the Faculty of Medicine, Tanta University, Gharbia, Egypt (Approval number: 35960/10/22) and by the faculty of pharmacy, Applied science Private University, Amman, Jordan (Approval number: 2022-PHA-30). Informed consent to participate: Informed consent was obtained at the start of the online questionnaire after explaining the goal and methods of the study. No personal data were collected.

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