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

> **NIH NIH U54** · MOREHOUSE SCHOOL OF MEDICINE · 2023 · $314,798

## Abstract

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.

## Key facts

- **NIH application ID:** 10800503
- **Project number:** 2U54MD007602-36
- **Recipient organization:** MOREHOUSE SCHOOL OF MEDICINE
- **Principal Investigator:** Lilly Hsi-Chih Immergluck
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $314,798
- **Award type:** 2
- **Project period:** 1997-07-07 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10800503

## Citation

> US National Institutes of Health, RePORTER application 10800503, Applying Association Mining and Group Based Trajectory Analyses to Characterize Spatial Temporal Trends of Community-Onset Antibiotic Resistant Staphylococcus aureus Infections in Children (2U54MD007602-36). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10800503. Licensed CC0.

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