  There is a constant need for many healthcare programs to timely address
problems with infection prevention and control (IP&C). For example, pathogens
can be transmitted among patients with cystic fibrosis (CF) in both the
inpatient and outpatient settings within the healthcare system even with the
existing recommended IP&C practices, and these pathogens are often associated
with negative clinical outcomes. Because of limited and delayed data sharing,
CF programs need a reliable method to track infection rates. There are three
complex structures in CF registry data: recurrent infections, missing data, and
multilevel correlation due to repeated measures within a patient and
patient-to-patient transmissions. A step-by-step analysis pipeline was proposed
to develop and validate a risk-adjusted model to help healthcare programs
monitor the number of recurrent events while taking into account missing data
and the hierarchies of repeated measures in right-censored data. We extended
the mixed-effect Andersen-Gill model (the frailty model), adjusted for
important risk factors, and provided confidence intervals for the predicted
number of events where the variability of the prediction was estimated from
three identified sources. The coverage of the estimated confidence intervals
was used to evaluate model performance. Simulation results indicated that the
coverage of our method was close to the desired confidence level. To
demonstrate its clinical practicality, our pipeline was applied to monitor the
infection incidence rate of two key CF pathogens using a U.S. registry. Results
showed that years closer to the time of interest were better at predicting
future incidence rates in the CF example.
