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).