A poisson-based prediction model and warning system for MRSA daily burden.
OBJECTIVE
This study was designed to demonstrate that the number of methicillin-resistant Staphylococcus aureus (MRSA) isolates collected daily in a community hospital is Poisson distributed and that using a one-sided Poisson control table is a fast and easy way to recognize unusually high numbers of MRSA isolates collected daily that may signal possible outbreaks.


METHODS
A retrospective analysis of MRSA isolates collected daily over a three year period (2005-2007, N = 934) was performed. Observed MRSA isolate frequencies are compared to Poisson frequencies using chi-square goodness-of-fit tests. A regression equation on the mean number of MRSA isolates collected daily for the years 2005, 2006, and 2007 is used to predict the mean number of MRSA isolates for 2008. A warning system for MRSA isolates collected daily is presented and a one-tailed, mean + 2 sigma control table is provided.


METHODS
One-hundred-fifty bed community hospital in central Massachusetts.


RESULTS
Goodness-of-fit tests showed close agreement between actual MRSA isolates collected daily and Poisson frequencies for 2005 (chi4(2) = 4.045, p = 0.39), 2006 (chi4(2) = 2.807, p = 0.59), and 2007 (chi4(2) = 1.494, p = 0.83).


CONCLUSIONS
Theoretical and empirical support is provided for the Poisson probability model. The model can be used to identify unusually high occurrences ofMRSA isolates collected daily. This study was limited to a single community healthcare system but the results may be generalized to other types of healthcare settings.
Perla
Rocco J
RJ
Institute for Healthcare Improvement, 20 University Road, 7th Floor, Cambridge, MA 02138, USA. rperla@IHI.org
Allen
Bradford D
BD
D016009:Chi-Square Distribution; D003428:Cross Infection / Q000453:epidemiology*; D006764:Hospitals, Community; D006801:Humans; D055624:Methicillin-Resistant Staphylococcus aureus / Q000302:isolation & purification*; D016012:Poisson Distribution; D011159:Population Surveillance / Q000379:methods*; D012044:Regression Analysis; D012189:Retrospective Studies; D013203:Staphylococcal Infections / Q000453:epidemiology* / Q000382:microbiology