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INTRODUCTION: National obesity rates are leading to higher rates of Type 2 Diabetes, increasing the number of people at risk of invasive infections with Methicillin Resistant Staphylococcus aureus (iMRSA) upon onset of ESRD and hemodialysis. However, an association between adiposity and risk of iMRSA has not been researched. AIM: The purpose of this study is to describe the epidemiological characteristics of an iMRSA cohort in the Atlanta metro area between 2005-2008; to examine BMI-related health outcomes within the cohort; and to compare proportions of BMI categories in this cohort to BRFSS data. METHODS: Surveillance data collected by the CDC EIP program on iMRSA in Atlanta, Georgia was used. BMI was calculated for each eligible case. Statistical analysis was carried out in SPSS. Univariate and multivariate binary logistic regression analysis was performed on select variables. A p-value of < 0.05 and a 95% confidence interval (CI) were used to determine significance. BRFSS BMI data for Georgia was compared to the study population. RESULTS: Overweight and obese cases were more likely to be diagnosed with diabetes by regression analysis. Cases with diabetes were at greater odds of having undergone dialysis within the previous year (univariate OR=2.3, p=0.000; multivariate OR=2.5, p=0.000). The proportion of iMRSA patients with diabetes is much greater (42.8%) than in the general population of the United States (7.8-10.7%). DISCUSSION: The results indicate that there may be a higher risk for iMRSA in overweight and obese individuals, particularly if other adiposity-related health problems are present.

Background: Controversy exists regarding the recommendation that Veterans Affairs Medical Centers implement mandatory screenings for MRSA nasal carriage upon admission and transfers with subsequent contact isolation of colonized patients to reduce transmission of methicillin-resistant Staphylococcus aureus (MRSA). It is uncertain whether patients identified only through active surveillance represent a significant risk for transmission. Inpatients may have already become sources of transmission before admission-PCR results become available and precautions are implemented. Costs associated with universal screening and delays between admission and discovery of carriage suggests a role for use of prediction rules to target screening and presumptive initial isolation of high-risk patients. Objective: To determine whether patients identified only through surveillance are as likely to contaminate their skin and environments as those identified clinically; to examine MRSA contamination before patient carriage status became known; and to develop rules to predict MRSA colonization for use in targeted admission screen, pre-emptive isolation, and rescreening. Methods: From July 2007 to July 2008, frequencies of skin and environmental contamination was compared between 58 MRSA carriers detected clinically versus 57 carriers detected through active surveillance at the Cleveland VAMC. Time to contamination and admission-PCR result notification was assessed by Kaplan-Meier method in 83 admissions from April to May 2008. Prediction rules for MRSA carriages were developed from a prospective cohort of 580 patients admitted from April to May 2008 and validated in158 patients.Results: MRSA carriers detected through surveillance only were as likely to have skin and environmental contamination as those identified clinically. Many patients with unconfirmed colonization of MRSA contaminate their skin and environment between screening on admission and indicated isolation. An algorithm requiring 10 clinical factors reduced the number needed to be screened by 40% while capturing 96% of MRSA nasal carriers. Conclusions: Strategies to limit MRSA transmission must address colonized as well as infected patients. The first few hours post-admission represent important opportunities to reduce risk of cross-transmission. Strategies to reduce delays in admission-PCR and result notification, to preemptively identify patients at high risk for disseminating MRSA, and to improve universal precautions are needed. Prediction rules may be used to target resource utilization.