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Use of strain typing data to estimate bacterial transmission rates in healthcare settings
Brian R Jackson1, Alun Thomas, Karen C Carroll
1Department of Medical Informatics, University of Utah, Salt Lake City, Utah, USA.
Objective:
To create an affordable and accurate method for continuously monitoring bacterial transmission rates in healthcare settings.
Design:
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.
Setting:
Inpatient units of an academic referral center.
Patients:
All inpatients with nosocomial infections.
Intervention:
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.
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