Use of strain typing data to estimate bacterial transmission rates in healthcare settings.
OBJECTIVE
To create an affordable and accurate method for continuously monitoring bacterial transmission rates in healthcare settings.


METHODS
We present a discrete simulation model that relies on the relationship between in-hospital transmission rates and strain diversity. We also present a proof of concept application of this model to a prospective molecular epidemiology data set to estimate transmission rates for Pseudomonas aeruginosa and Staphylococcus aureus.


METHODS
Inpatient units of an academic referral center.


METHODS
All inpatients with nosocomial infections.


METHODS
Mathematical model to estimate transmission rates.


RESULTS
Maximum likelihood estimates for transmission rates of these two species on different hospital units ranged from 0 to 0.36 transmission event per colonized patient per day.


CONCLUSIONS
This approach is feasible, although estimates of transmission rates based solely on strain typed clinical cultures may be too imprecise for routine use in infection control. A modest level of surveillance sampling substantially improves the estimation accuracy.
Jackson
Brian R
BR
Department of Medical Informatics, University of Utah, Salt Lake City, Utah, USA.
Thomas
Alun
A
Carroll
Karen C
KC
Adler
Frederick R
FR
Samore
Matthew H
MH
D001424:Bacterial Infections / Q000382:microbiology* / Q000635:transmission*; D015373:Bacterial Typing Techniques / Q000379:methods*; D016001:Confidence Intervals; D003428:Cross Infection / Q000382:microbiology* / Q000635:transmission*; D018562:Disease Transmission, Infectious; D006090:Gram-Negative Bacteria / Q000145:classification; D006801:Humans; D007902:Length of Stay; D016013:Likelihood Functions; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011446:Prospective Studies; D013211:Staphylococcus aureus / Q000145:classification