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一项关于33个国家中长新冠的患病率及预测因素的跨国横断面研究。

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

作者信息

Zaazouee Mohamed Sayed, Nada Eman Ayman, Al-Kafarna Mohammed, Shaheen Ahmed, Ramu Shivabalan Kathavarayan, Hafez Abdelrahman H, Matar Sajeda Ghassan, Assar Ahmed, Elshennawy Mohamed, Abu El-Enien Hazem, Redwan Hala Jamal, Elsayed Sarah Makram, Omran Maha Jabir, Salloum Omar Hammam, Almadhoon Hossam, Mamdouh Mohamed, Wahsh Engy A, Nourelden Anas Zakarya, Elshanbary Alaa Ahmed, Abdel-Aziz Walid, Rashid Hivan Haji, Basheti Iman, Ragab Khaled Mohamed, Masoud Ahmed Taher, Abushouk Abdelrahman I

机构信息

Faculty of Medicine, Al-Azhar University, Assiut, Egypt.

Faculty of Pharmacy, Tanta University, Tanta, Gharbia, Egypt.

出版信息

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


DOI:10.1038/s41598-025-10120-z
PMID:40754541
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12319108/
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.

摘要

长期新冠(LC)的症状可能使人虚弱,并且可能与焦虑、社会污名化以及生活质量下降有关。识别有患LC风险的患者对于提供后续护理和制定人群层面的公共卫生措施很重要。当前的这项跨国研究旨在评估普通人群中LC的患病率及预测因素。我们在2022年4月至2023年1月期间开展了一项在线跨国横断面调查,目标是18岁及以上曾确诊感染过新冠病毒的参与者。我们通过在线谷歌表单采用便利抽样的方式招募参与者。我们收集了人口统计学数据、既往病史、感染细节、新冠后症状以及生活质量。然后将回答内容翻译成英文。LC按照世界卫生组织的定义来界定。进行单变量分析以识别与LC发生显著相关的因素。在去除多重共线性变量后,建立广义线性模型来估计不同预测因素对LC发生的影响。分析纳入了来自33个国家的11,801名受访者。参与者的平均年龄为32.7±12.8岁,其中61%为女性。参与者的平均体重指数为25.2±4.8,14.8%的人吸烟。78%的参与者报告接种了新冠疫苗。PCR确诊感染新冠的受访者随后被分为患LC组(N = 2335,19.8%)和未患LC组(N = 9466人;80.2%)。我们的模型识别出了25个显著的预测因素。LC风险较高的预测因素包括入住重症监护病房(OR = 2.08;95%置信区间1.36,3.18;P = 0.001)、女性(OR = 1.8;95%置信区间1.61,2.02;P < 0.001)、感染期间出现疲劳(OR = 1.6;95%置信区间1.43,1.78;P < 0.001)、认定为西班牙裔(OR = 1.53;95%置信区间1.26,1.85;P < 0.001)以及既往有胃肠道疾病(OR = 1.48;95%置信区间1.22,1.8;P < 0.001)。总之,我们识别出了关键的LC预测因素,包括入住重症监护病房、女性以及急性疲劳作为主要风险因素,而非洲裔美国人和亚洲族裔以及哪怕只接种一剂疫苗都显示出保护作用。

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本文引用的文献

[1]
Unmasking the Long-Term Effects of COVID-19 With a Focus on Chronic Fatigue Syndrome: A Community-Based Study From India.

Cureus. 2025-3-14

[2]
Relationship Between Acute SARS-CoV-2 Viral Clearance and Long COVID-19 (Long COVID) Symptoms: A Cohort Study.

Clin Infect Dis. 2025-2-5

[3]
Mitochondrial dysfunction in long COVID: mechanisms, consequences, and potential therapeutic approaches.

Geroscience. 2024-10

[4]
Pathophysiological, immunological, and inflammatory features of long COVID.

Front Immunol. 2024

[5]
Immune Mechanisms Underpinning Long COVID: Collegium Internationale Allergologicum Update 2024.

Int Arch Allergy Immunol. 2024

[6]
Analysis of influencing factors on long COVID in COVID-19 patients infected with omicron variant three months after discharge: a cross-sectional study.

BMC Infect Dis. 2024-1-2

[7]
Cardiovascular autonomic dysfunction in "Long COVID": pathophysiology, heart rate variability, and inflammatory markers.

Front Cardiovasc Med. 2023-9-1

[8]
A systematic review and meta-analysis of long-term sequelae of COVID-19 2-year after SARS-CoV-2 infection: A call to action for neurological, physical, and psychological sciences.

J Med Virol. 2023-6

[9]
The controversial effect of smoking and nicotine in SARS-CoV-2 infection.

Allergy Asthma Clin Immunol. 2023-6-1

[10]
Efficacy of first dose of covid-19 vaccine versus no vaccination on symptoms of with long covid: target trial emulation based on ComPaRe e-cohort.

BMJ Med. 2023-2-1

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