  Nosocomial outbreaks of bacteria are well-documented. Based on these
incidents, and the heavy usage of antibiotics in hospitals, it has been assumed
that antibiotic resistance evolves in hospital environments. To test this
assumption, we studied resistance phenotypes of bacteria collected from patient
isolates at a community hospital over a 2.5-year period. A graphical model
analysis shows no association between resistance and patient information other
than time of arrival. This allows us to focus on time course data.
  We introduce a Hospital Transmission Model, based on negative binomial delay.
Our main contribution is a statistical hypothesis test called the Nosocomial
Evolution of Resistance Detector (NERD). It calculates the significance of
resistance trends occurring in a hospital. It can inform hospital staff about
the effects of various practices and interventions, can help detect clonal
outbreaks, and is available as an R-package.
  We applied the NERD method to each of the 16 antibiotics in the study via 16
hypothesis tests. For 13 of the antibiotics, we found that the hospital
environment had no significant effect upon the evolution of resistance; the
hospital is merely a piece of the larger picture. The p-values obtained for the
other three antibiotics (Cefepime, Ceftazidime and Gentamicin) indicate that
particular care should be taken in hospital practices with these antibiotics.
One of the three, Ceftazidime, was significant after accounting for multiple
hypotheses, indicating a trend of decreased resistance for this drug.
