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A Multivariable Model to Classify Methicillin-Resistant Staphylococcus aureus Infections as Health Care or Community Associated

Sievert, Dawn M. PhD, MS*†; Boulton, Matthew L. MD, MPH*; Wilson, Mark L. ScD*; Wilkins, Melinda J. DVM, PhD, MPH; Gillespie, Brenda W. PhD*

Infectious Diseases in Clinical Practice: January 2012 - Volume 20 - Issue 1 - p 42–48
doi: 10.1097/IPC.0b013e31823c49b6
Original Articles

Background Methicillin-resistant Staphylococcus aureus (MRSA) infections are often defined as health care (HA) or community-associated (CA) using common classification schemes involving health care risk factor, infection type, susceptibility pattern, or molecular typing. This investigation compared pulsed-field gel electrophoresis (PFGE) molecular typing results (dichotomized as HA or CA) with our new MRSA infection classification method. The goal was to develop an improved predictive model for PFGE-type based primarily on the other 3 classification variables.

Methods Methicillin-resistant S. aureus infections reported to the Michigan Department of Community Health from October 2004 to December 2005 were analyzed. Patients’ demographics, risk factors, infection information, and susceptibility results were collected for 2151 cases. A subset of 244 MRSA infections with available PFGE results was analyzed. Results of logistic regression are presented using a receiver operating characteristic curve analysis.

Results The multivariable models predicted the PFGE classification as HA or CA (Max-rescaled R 2 = 61%) better than health care risk factor, infection type, or susceptibility pattern alone (max-rescaled R 2 = 21%, 34%, and 46%, respectively). The best model included infection type, susceptibility pattern, age, and hospitalized during infection.

Conclusions This model provides a simpler, more accurate prediction of HA or CA status, thus enhancing efforts to control MRSA infections.

From the *School of Public Health, University of Michigan, Ann Arbor; and †Michigan Department of Community Health, Lansing, MI.

Correspondence to: Dawn M. Sievert, PhD, MS, 1600 Clifton Rd, NE MS A-24, Atlanta, GA 30333. E-mail:

The authors have no funding or conflicts of interest to disclose.

© 2012 Lippincott Williams & Wilkins, Inc.