tetano
Editor, Senior Moderator
Asia Pac J Public Health
. 2021 May 21;10105395211017755.
doi: 10.1177/10105395211017755. Online ahead of print.
Clustering of COVID-19 Symptoms Among Iranian Patients: The Role of Preexisting Comorbidity on Latent Class Membership
Eslam Moradi-Asl[SUP] 1 [/SUP], Davoud Adham[SUP] 1 [/SUP], Hassan Ghobadi[SUP] 2 [/SUP], Abbas Abbasi-Ghahramanloo[SUP] 1 [/SUP]
Affiliations
Abstract
This study aimed to identify subgroups of coronavirus disease 2019 (COVID-19) symptoms and assess the role that preexisting comorbidity on membership of specific subgroup. This cross-sectional study took place in Ardabil, northwest of Iran. All patients (16 183) who were admitted to the hospitals of Ardabil province were recruited. Six indicator variables were selected to identify latent subgroups of patients using the result of polymerase chain reaction (PCR) test as a grouping variable. Data analysis was performed using ?[SUP]2[/SUP], independent t test, and latent class analysis. This study found that among PCR-positive patients, there were 3 latent classes: (1) mild disease (16.1%), (2) semi-severe disease (62.5%), and (3) severe disease (21.3%). This study showed that having preexisting comorbidity increase the odds of membership in semi-severe disease (odds ratio = 2.30) and severe disease (odds ratio = 1.60) classes compared with mild disease class. Focusing on patients who experience co-occurrence of more symptoms may be helpful in control of COVID-19.
Keywords: COVID-19; Iran; latent class analysis; patients; symptoms.
. 2021 May 21;10105395211017755.
doi: 10.1177/10105395211017755. Online ahead of print.
Clustering of COVID-19 Symptoms Among Iranian Patients: The Role of Preexisting Comorbidity on Latent Class Membership
Eslam Moradi-Asl[SUP] 1 [/SUP], Davoud Adham[SUP] 1 [/SUP], Hassan Ghobadi[SUP] 2 [/SUP], Abbas Abbasi-Ghahramanloo[SUP] 1 [/SUP]
Affiliations
- PMID: 34018399
- DOI: 10.1177/10105395211017755
Abstract
This study aimed to identify subgroups of coronavirus disease 2019 (COVID-19) symptoms and assess the role that preexisting comorbidity on membership of specific subgroup. This cross-sectional study took place in Ardabil, northwest of Iran. All patients (16 183) who were admitted to the hospitals of Ardabil province were recruited. Six indicator variables were selected to identify latent subgroups of patients using the result of polymerase chain reaction (PCR) test as a grouping variable. Data analysis was performed using ?[SUP]2[/SUP], independent t test, and latent class analysis. This study found that among PCR-positive patients, there were 3 latent classes: (1) mild disease (16.1%), (2) semi-severe disease (62.5%), and (3) severe disease (21.3%). This study showed that having preexisting comorbidity increase the odds of membership in semi-severe disease (odds ratio = 2.30) and severe disease (odds ratio = 1.60) classes compared with mild disease class. Focusing on patients who experience co-occurrence of more symptoms may be helpful in control of COVID-19.
Keywords: COVID-19; Iran; latent class analysis; patients; symptoms.