1111. Evaluating Transmission Dynamics of Methicillin-Resistant Staphyloccus aureus Across Veterans Affairs Acute Care Facilities
Session: Poster Abstract Session: MRSA/VRE Epidemiology
Friday, October 9, 2015
Room: Poster Hall
Background: Studies of hospital-acquired infections (HAIs) often rely on estimates of acquisition rate and admission prevalence, which being subject to error from incomplete ascertainment of the time of acquisition, and imperfect surveillance test accuracy, may lead to inaccurate conclusions. Since surveillance tests for methicillin-resistant Staphylococcus aureus(MRSA) are performed at admission, transfer, and discharge for each patient admitted to acute care in the US Veterans Affairs (VA), we applied novel methods to the study of MRSA transmission dynamics to account for these difficulties.

Methods: We used MRSA surveillance data from 117 acute care facilities in the VA to study MRSA importation, acquisition, and loss of colonization (clearance) both in the inpatient and outpatient settings over a 3.5 year period, beginning in October 2007. Facilities with an average of 86 admissions per month and 1% admission surveillance testing were included. We developed a Bayesian transmission model that uses admission and discharge times, as well as surveillance culture times and results to estimate surveillance test false negative probability (FN), trends in transmission, importation, and time to clearance during and between admissions across all 117 facilities.

Results: Our estimates indicate that 87% of the facilities had declining transmission rates, with a median reduction in the transmission rate over the period of 43% (IQR; 20% - 59%). Across all 117 facilities, the median number of days to clearance was estimated to be 48 (IQR; 39-62) during an admission and 266 (IQR; 226-333) between admissions, median estimate of FN was 0.23 (IQR; 0.19 - 0.28) and importation probability was 10.5% (IQR; 9.2%-12.1%).

Conclusion: Our model incorporates information known to influence transmission dynamics, thus overcoming the challenge posed by misclassification and imperfect surveillance tests. Future work includes the incorporation of patient-level and facility-level covariates to more fully understand the factors that contribute to pathogen endemicity and transmission, and to better target infection control strategies.

Karim Khader, PhD, Ideas Center, VA Salt Lake City Health Care System, Salt Lake City, UT, Alun Thomas, PhD, Genetic Epidemiology, Department of Medicine, University of Utah, Salt Lake City, UT, Makoto Jones, MD, MS, Internal Medicine, University of Utah School of Medicine Division of Epidemiology, Salt Lake City, UT, Molly Leecaster, PhD, Division of Epidemiology, University of Utah School of Medicine, Salt Lake city, UT, Michael Rubin, MD, PhD, FIDSA, Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, Yue Zhang, PhD, Division of Epidemiology, Department of Medicine, University of Utah, Salt Lake City, UT, Andrew Redd, PhD, Ideas Center, VA Salt Lake City HCS, Salt Lake City, UT, Tom Greene, PhD, Medicine, University of Utah School of Medicine, Division of Epidemiology, Salt Lake City, UT and Matthew Samore, MD, FSHEA, University of Utah School of Medicine, Division of Epidemiology, Salt Lake City, UT

Disclosures:

K. Khader, None

A. Thomas, None

M. Jones, None

M. Leecaster, None

M. Rubin, None

Y. Zhang, None

A. Redd, None

T. Greene, None

M. Samore, None

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