Data Collection and Analysis
Data for outcomes and factors
associated with severe RSV infection were extracted from the EMR and
entered into standardized electronic case report forms maintained on a
secure server.
Chi-squared test of proportions was utilized to determine differences in
the distribution of severe illness by month and by surveillance year.
Odds ratios (OR) and 95% confidence intervals (CI) were used to
estimate the odds of severe RSV outcomes by demographic characteristics,
comorbid conditions, and living situation. We built a multivariate
logistic regression model using bidirectional elimination to evaluate
characteristics associated with severe RSV outcomes. Variables included
in the model were those hypothesized to be associated with severe
illness, i.e., older age, obesity, and heart, lung, and neurologic
comorbidities and any factors significantly associated with severe RSV
infection in the bivariate analysis. The final model was selected by
minimizing the Akaike information criterion (AIC) and maximizing the
coefficient of determination (r2).
We explored whether severe RSV outcomes impacted the discharge level of
care needed, using living situation as a surrogate for the level of
care. To do so, we compared the pre-admission and discharge living
situation for each surviving patient. Changes in the patient’s living
situation were categorized as increased (e.g., living independently
before hospitalization and discharged to a nursing home), decreased
(e.g., living in the community with family before hospitalization and
discharged to living independently), or unchanged level of care. Odds
ratios and 95% confidence intervals were used to estimate the odds of
change in living situation among those with and without severe RSV
outcomes.
Fisher’s Exact test was utilized, when appropriate. A p-value of
<0.05 was considered statistically significant. R-Studio
(https://rstudio.com, Version 1.2.5001) packages and procedures were
used for data analysis.