2.4 Statistical analysis
In our study, co-infection of other respiratory pathogens in COVID-19 patients, was the outcome measure and presented as the categorical variable. In order to determine the independent factors of co-infection, univariate and multivariate analysis were performed in sequence. Mann-Whitney U test, χ² test or Fisher’s exact test was first conducted to compare differences between groups with and without co-infection of other respiratory pathogens in COVID-19 patients. Next, factors with statistical significance (P <0.05) were further analyzed using the Logistic regression model, and their odds ratio (OR) were calculated. According to the types of respiratory pathogens, co-infection with bacteria and viruses were separately analyzed through the above statistical process.
Additionally, we used Cox regression to assess the impact of co-infection on prognosis of COVID-19. In our study, we focused on the negative conversion of SARS-CoV-2 to determine the variation of viral shedding duration associated with co-infection in COVID-19 patients. Co-infection was introduced into the Cox regression model, which was set as a categorical variable presented by no co-infection (endowed by 0), co-infection of only bacteria (endowed by 1), co-infection of only viruses (endowed by 2) and co-infection of mixed bacteria and viruses (endowed by 3), respectively. A previous study suggested that age older than 45 years and chest tightness are independent factors affecting negative conversion of SARS-CoV-2 RNA (Hu et al., 2020). Therefore, age older than 45 years and chest tightness were also introduced to control their impacts.
Continuous and categorical variables were presented as median (interquartile range, IQR) and n (%), respectively. A P value less than 0.05 (two-tailed) was considered statistically significant. Analyses were performed using SPSS software (version 22.0)and R software (version 3.6.3).