Statistical Analysis
We summarized the demographic data among CF hospitalizations by age,
sex, race, geographic region, payer, hospital location and bed size and
compared those with and without a co-occurring diagnosis of CDI.
Continuous variables were summarized using means and standard deviations
while categorical variables were expressed in proportions. We then
compared demographic and clinical characteristics between patient
admissions for CF with and without CDI using non-parametric bivariate
tests as appropriate.
Next, we summarized outcomes of in-hospital mortality, LOS, and
healthcare expenditures overall and by presence or absence of CDI using
bivariate statistics. We then fit multivariate models to determine the
independent associations between CDI and study outcomes, adjusting for
variables associated with CDI at p<0.1 on bivariate analyses
(calendar year, sex, payer and hospital location/teaching status). For
mortality, we fit logistic regression models to estimate odds ratios and
95% confidence intervals. To accommodate the skewed distributions for
outcomes of LOS and hospital charges, we used general linear models with
gamma distribution and logarithmic transformation. We reported on the
percentage change from the referent along with 95% confidence interval.
We next evaluated trends in the proportion of CF hospitalizations with
co-existing C. difficile over time between the years 1997 to 2016
using the chi-square test of trend.
All analyses were conducted using SAS 9.4 (Cary, North Carolina).