Epidemiology, Symptomatology, and Risk Factors for Long COVID Symptoms: Population-Based, Multicenter Study
Notice bibliographique
Résumé
BACKGROUND: Long COVID induces a substantial global burden of disease. The pathogenesis, complications, and epidemiological and clinical characteristics of patients with COVID-19 in the acute phase have been evaluated, while few studies have characterized the epidemiology, symptomatology, and risk factors of long COVID symptoms. Its characteristics among patients with COVID-19 in the general population remain unaddressed. OBJECTIVE: We examined the prevalence of long COVID symptoms, its symptom patterns, and its risk factors in 4 major Chinese cities in order to fill the knowledge gap. METHODS: We performed a population-based, multicenter survey using a representative sampling strategy via the Qualtrics platform in Beijing, Shanghai, Guangzhou, and Hong Kong in June 2022. We included 2712 community-dwelling patients with COVID-19 and measured the prevalence of long COVID symptoms defined by the World Health Organization (WHO), and their risk factors. The primary outcomes were the symptoms of long COVID, with various levels of impact. A descriptive analysis of the prevalence and distribution of long COVID symptoms according to disease severity was conducted. A sensitivity analysis of increasing the number of long COVID symptoms was also conducted. Univariate and multivariate regression analyses were performed to examine the risk factors of severe long COVID symptoms, including age, gender, marital status, current occupation, educational level, living status, smoking habits, monthly household income, self-perceived health status, the presence of chronic diseases, the use of chronic medication, COVID-19 vaccination status, and the severity of COVID-19. RESULTS: The response rate was 63.6% (n=2712). The prevalence of long COVID, moderate or severe long COVID, and severe long COVID was 90.4% (n=2452), 62.4% (n=1692), and 31.0% (n=841), respectively. Fatigue (n=914, 33.7%), cough (n=865, 31.9%), sore throat (n=841, 31.0%), difficulty in concentrating (n=828, 30.5%), feeling of anxiety (n=817, 30.2%), myalgia (n=811, 29.9%), and arthralgia (n=811, 29.9%) were the most common severe long COVID symptoms. From multivariate regression analysis, female gender (adjusted odds ratio [aOR]=1.49, 95% CI 1.13-1.95); engagement in transportation, logistics, or the discipline workforce (aOR=2.52, 95% CI 1.58-4.03); living with domestic workers (aOR=2.37, 95% CI 1.39-4.03); smoking (aOR=1.55, 95% CI 1.17-2.05); poor or very poor self-perceived health status (aOR=15.4, 95% CI 7.88-30.00); ≥3 chronic diseases (aOR=2.71, 95% CI 1.54-4.79); chronic medication use (aOR=4.38, 95% CI 1.66-11.53); and critical severity of COVID-19 (aOR=1.52, 95% CI 1.07-2.15) were associated with severe long COVID. Prior vaccination with ≥2 doses of COVID-19 vaccines was a protective factor (aOR=0.35-0.22, 95% CI 0.08-0.90). CONCLUSIONS: We examined the prevalence of long COVID symptoms in 4 Chinese cities according to the severity of COVID-19. We also evaluated the pattern of long COVID symptoms and their risk factors. These findings may inform early identification of patients with COVID-19 at risk of long COVID and planning of rehabilitative services.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».