Geographic surveillance of community associated MRSA infections in children using electronic health record data.

Background: Community- associated methicillin resistant Staphylococcus aureus (CA-MRSA) cause serious infections and rates continue to rise worldwide. Use of geocoded electronic health record (EHR) data to prevent spread of disease is limited in health service research. We demonstrate how geocoded EHR and spatial analyses can be used to identify risks for CA-MRSA in children, which are tied to place-based determinants and would not be uncovered using traditional EHR data analyses.

Methods: An epidemiology study was conducted on children from January 1, 2002 through December 31, 2010 who were treated for Staphylococcus aureus infections. A generalized estimated equations (GEE) model was developed and crude and adjusted odds ratios were based on S. aureus risks. We measured the risk of S. aureus as standardized incidence ratios (SIR) calculated within aggregated US 2010 Census tracts called spatially adaptive filters, and then created maps that differentiate the geographic patterns of antibiotic resistant and non-resistant forms of S. aureus.

Results: CA-MRSA rates increased at higher rates compared to non-resistant forms, p = 0.01. Children with no or public health insurance had higher odds of CA-MRSA infection. Black children were almost 1.5 times as likely as white children to have CA-MRSA infections (aOR 95% CI 1.44,1.75, p < 0.0001); this finding persisted at the block group level (p < 0.001) along with household crowding (p < 0.001). The youngest category of age (< 4 years) also had increased risk for CA-MRSA (aOR 1.65, 95%CI 1.48, 1.83, p < 0.0001). CA-MRSA encompasses larger areas with higher SIRs compared to non-resistant forms and were found in block groups with higher proportion of blacks (r = 0.517, p < 0.001), younger age (r = 0.137, p < 0.001), and crowding (r = 0.320, p < 0.001).

Conclusions: In the Atlanta MSA, the risk for CA-MRSA is associated with neighborhood-level measures of racial composition, household crowding, and age of children. Neighborhoods which have higher proportion of blacks, household crowding, and children < 4 years of age are at greatest risk. Understanding spatial relationship at a community level and how it relates to risks for antibiotic resistant infections is important to combat the growing numbers and spread of such infections like CA-MRSA.