STATISTICS
The association between severity of COVID-19 and both the atopy status
and the clinical co-factors recorded was studied in both univariate and
multivariate analyses. Each variable of interest was dichotomized as
negative or positive to study the proportion of subjects with a given
clinical status. Categorical variables were analyzed using the Pearsons’
χ2 or Fisher’s exact test when indicated. Multiple
logistic regression was performed to estimate the degree of association
of the main exposure variable (i.e., atopy) with COVID-19 severity after
simultaneously adjusting for all the other variables of interest. P
values <0.05 were considered significant.
Relatedness of COVID-19 disease
grading was tested by applying unsupervised Eisen’s hierarchical cluster
methods to the data set, encompassing patient’ comorbidities and using
as agglomeration rule the average linkage clustering as implemented in
the program Genesis 1.7.2 (10,11). Unsupervised clustering involved the
sorting of both COVID-19 clinical status and patients comorbidities. The
COVID-19 disease grading tree was computed based on a full data set and
the distance between samples was computed by using the Pearson
correlation as similarity measures. As a result, coexistent clinical
comorbidities were grouped as hierarchical clusters and presented as
heat-maps. Each square in the heat-map represents the presence (red) or
the absence (black) of any given tested comorbidity.
The SPSS/PC+ statistical package for statistical evaluation (SPSS,
version 15, Chicago, IL) was used to analyze the data.