Applying Association Mining and Group Based Trajectory Analyses to Characterize Spatial Temporal Trends of Community-Onset Antibiotic Resistant Staphylococcus aureus Infections in Children

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Abstract Staphylococcus aureus (S. aureus) remains a leading cause of infections, especially in the outpatient setting where it is the primary cause of skin and soft tissue infections (SSTI). S. aureus also is a commensal bacterium, known to colonize 25-40% of humans and is therefore, part of the human microbiome. In the late 1990s, a new clonal lineage emerged making S. aureus the source of epidemic proportions of community associated infections across the lifespan, with an increased risk among African American and indigenous children and older adults. Over time, the landscape of the antibiotic resistance changed, likely due to multifactorial processes, including the overprescribing of non β-lactam antibiotics (resistance to β-lactam class is the hall mark of methicillin resistant S. aureus (MRSA)).Two decades later, both non β-lactam S. aureus (methicillin sensitive or MSSA) and MRSA have gained additional resistance to other antibiotic classes, making S. aureus a multi drug resistant (MDR) bacteria and one of the top antibiotic resistant germs causing severe illnesses nationally and worldwide. While it has been well known that health disparities exist for MRSA, the disparities seen with community originated forms of MDR S. aureus (and risk factors associated with this strain) are different than those MDR S. aureus which originated from hospital settings. Socioecological conditions play a role in the risks seen with community-based infections. Our study proposes to use association mining of antibiotic resistant phenotypes seen with S. aureus with spatial trend analyses to detect transmission patterns over time and areas. Applying multi-level spatially relevant group- based trajectory modeling will allow us to detect ‘subtle’ changes in MDR patterns that occur over time, and delineate geographic areas associated with MDR patterns. Results generated by this research will contribute to current the knowledge on community-based S. aureus strains (the genotype and its associated phenotypes) which cause SSTI and additionally, address the gap of identifying the factors contributing to recurrence of these SSTI. Understanding strain specificity will serve as the basis to prevent the spread of antibiotic resistant S. aureus infections in community settings and help identify S. aureus antigenic determinants which potentially can serve as staphylococcal vaccine candidates. This study has the potential to curb the upward trajectory and increasing public health threat posed by this multi-drug resistant bacteria.