Risk Factors and Characterization of Post-COVID-19 Syndrome in Jordan
Notice bibliographique
Résumé
Background There is controversial information about the sequelae of COVID-19 after recovery, or post-COVID-19 syndrome (PCS). Despite the considerable number of studies on COVID-19, proportionally, there is a scarcity of literature addressing PCS, particularly the risk factors causing this syndrome. Determining the prevalence, most common manifestations of PCS, and the possible related risk factors is an important issue. Objective To fill these gaps, the aim of this study was to detect the prevalence and risk factors for the development of PCS, and to identify the symptoms and their relation to the sociodemographic and medical characteristics of patients who survived COVID-19 after more than 3 months from onset of illness throughout Jordan. Methods A cross-sectional, online questionnaire–based study was conducted. This questionnaire was posted to the association of “My experience with COVID-19” in Jordan. Sociodemographic and COVID-19 illness information was collected from 657 patients who had recovered from COVID-19 at least 3 months after the illness started. Results The PCS prevalence was 71.9%, including patients who experienced at least one PCS symptom. The most common symptoms included dyspnea, fatigue, taste and smell impairment, cough, and depression. Six factors were found to significantly increase the risk of PCS: being female (odds ratio [OR] 2.06, 95% CI 1.409-2.856), aged ≥30 years (OR 1.64, 95% CI 1.16-2.33), diabetes mellitus (OR 2.978, 95% CI 1.08-8.21), hypertension (OR 2.22, 95% CI 1.118-4.423), respiratory disease (OR 2.33, 95% CI 1.21-4.501), and neuropsychological disturbance during illness (OR 3.79, 95% CI 2.574-5.573). These patients also showed a significantly higher rate of PCS than their counter groups. Therefore, females, aged ≥30 years, comorbidity, and neuropsychological disturbance during illness are considered to be risk factors for PCS. Conclusions The PCS prevalence is high in Jordan, particularly among certain populations such as females; aged ≥30 years; those with a neuropsychological disturbance during illness; and having a comorbidity such as diabetes, hypertension, and respiratory diseases, which were associated with a significantly higher risk for the development of PCS manifestations. In other words, these populations should be considered as a risk group for PCS occurrence. Therefore, COVID-19 infection treatment should not only be administered during the acute episode but should continue for several months after recovery of the patient. In addition, the PCS period will require further scientific study and investigation along with early interventions, including rehabilitation. Therefore, we now have to start the steps in preparing for this unavoidable problem to improve the health care system and enhance the management of patients during the PCS period. Psychological and medical support is highly recommended during and after a COVID-19 episode, particularly for the high-risk groups.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».