Article ; Online: Who develops long COVID? Longitudinal pre-pandemic predictors of long COVID and symptom clusters in a representative Dutch population.
2024 , Page(s) 107048
Abstract: Objectives: Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom ... ...
Abstract | Objectives: Prior studies show that long COVID has a heterogeneous presentation. Whether specific risk factors are related to subclusters of long COVID remains unknown. This study aimed to determine pre-pandemic predictors of long COVID and symptom clustering. Methods: 3022 participants of a panel representative of the Dutch population completed an online survey about long COVID symptoms. Data was merged to 2018/2019 panel data covering sociodemographic, medical, and psychosocial predictors. A total of 415 participants were classified as having long COVID. K-means clustering was used to identify patient clusters. Multivariate and lasso regression was used to identify relevant predictors compared to a COVID-19 positive control group. Results: Predictors of long COVID included Western ethnicity, BMI, chronic disease, COVID-19 reinfections, severity, and symptoms, lower self-esteem, and higher positive affect (AUC=0.80, 95%CI 0.73-0.86). Four clusters were identified: a low and a high symptom severity cluster, a smell-taste and respiratory symptoms cluster, and a neuro-cognitive, psychosocial, and inflammatory symptom cluster. Predictors for the different clusters included regular health complaints, healthcare use, fear of COVID-19, anxiety, depressive symptoms, and neuroticism. Conclusions: A combination of sociodemographic, medical, and psychosocial factors predicted long COVID. Heterogenous symptom clusters suggest that there are different phenotypes of long COVID presentation. |
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Language | English |
Publishing date | 2024-04-10 |
Publishing country | Canada |
Document type | Journal Article |
ZDB-ID | 1331197-9 |
ISSN | 1878-3511 ; 1201-9712 |
ISSN (online) | 1878-3511 |
ISSN | 1201-9712 |
DOI | 10.1016/j.ijid.2024.107048 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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