طبقه بندی بیماران و تعیین هم وقوعی علایم کووید ۱۹ و عوامل موثر بر آن در بیماران ایرانی با استفاده از مدل Latent Class Analysis

عنوان انگليسی

Clustering of COVID-19 Symptoms Among Iranian Patients: The Role of Preexisting Comorbidity on Latent Class Membership

خلاصه انگلیسی

A wide range of symptoms has been reported for coronavirus since the onset of the pandemic, the most important of which include fever, cough, shortness of breath, sore throat, headache, body aches, diarrhea, and nauseas.1-3 Latent class analysis (LCA) is a person-centered approach that uses categorical data to find subtypes of related cases and finally yield latent classes.4 Since there is limited information about the patterns and clustering of coronavirus disease 2019 (COVID-19) symptoms in Iran, this study aimed to identify the subgroups of patients based on symptoms of COVID-19 and assess the role of preexisting comorbidity on the membership of patients in each latent class.

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