Staphylococcus aureus (S. aureus) is known to cause human infections and since the late 1990s, community-onset antibiotic resistant infections (methicillin resistant S. aureus (MRSA)) continue to cause significant infections in the United States. Skin and soft tissue infections (SSTIs) still account for the majority of these in the outpatient setting. Machine learning can predict the location-based risks for community-level S. aureus infections. Multi-year (2002–2016) electronic health records of children <19 years old with S. aureus infections were queried for patient level data for demographic, clinical, and laboratory information. Area level data (Block group) was abstracted from U.S. Census data. A machine learning ecological niche model, maximum entropy (MaxEnt), was applied to assess model performance of specific place-based factors (determined a priori) associated with S. aureus infections; analyses were structured to compare methicillin resistant (MRSA) against methicillin sensitive S. aureus (MSSA) infections. Differences in rates of MRSA and MSSA infections were determined by comparing those which occurred in the early phase (2002–2005) and those in the later phase (2006–2016). Multi-level modeling was applied to identify risks factors for S. aureus infections. Among 16,124 unique patients with community-onset MRSA and MSSA, majority occurred in the most densely populated neighborhoods of Atlanta’s metropolitan area. MaxEnt model performance showed the training AUC ranged from 0.771 to 0.824, while the testing AUC ranged from 0.769 to 0.839. Population density was the area variable which contributed the most in predicting S. aureus disease (stratified by CO-MRSA and CO-MSSA) across early and late periods. Race contributed more to CO-MRSA prediction models during the early and late periods than for CO-MSSA. Machine learning accurately predicts which densely populated areas are at highest and lowest risk for community-onset S. aureus infections over a 14-year time span.

(1) Background: With increasing international travel and mass population displacement due to war, famine, climate change, and immigration, pathogens, such as Staphylococcus aureus (S. aureus), can also spread across borders. Methicillin-resistant S. aureus (MRSA) most commonly causes skin and soft tissue infections (SSTIs), as well as more invasive infections. One clonal strain, S. aureus USA300, originating in the United States, has spread worldwide. We hypothesized that S. aureus USA300 would still be the leading clonal strain among US-born compared to non-US-born residents, even though risk factors for SSTIs may be similar in these two populations (2) Methods: In this study, 421 participants presenting with SSTIs were enrolled from six community health centers (CHCs) in New York City. The prevalence, risk factors, and molecular characteristics for MRSA and specifically clonal strain USA300 were examined in relation to the patients’ self-identified country of birth. (3) Results: Patients born in the US were more likely to have S. aureus SSTIs identified as MRSA USA300. While being male and sharing hygiene products with others were also significant risks for MRSA SSTI, we found exposure to animals, such as owning a pet or working at an animal facility, was specifically associated with risk for SSTIs caused by MRSA USA300. Latin American USA300 variant (LV USA300) was most common in participants born in Latin America. Spatial analysis showed that MRSA USA300 SSTI cases were more clustered together compared to other clonal types either from MRSA or methicillin-sensitive S. aureus (MSSA) SSTI cases. (4) Conclusions: Immigrants with S. aureus infections have unique risk factors and S. aureus molecular characteristics that may differ from US-born patients. Hence, it is important to identify birthplace in MRSA surveillance and monitoring. Spatial analysis may also capture additional information for surveillance that other methods do not.

Background: Into the third year of the COVID-19 pandemic and the second year of in-person learning for many K-12 schools in the United States, the benefits of mitigation strategies in this setting are still unclear. We compare COVID-19 cases in school-aged children and adolescents between a school district with a mandatory mask-wearing policy to one with an optional mask-wearing policy, during and after the peak period of the Delta variant wave of infection. Methods: COVID-19 cases during the Delta variant wave (August 2021) and post the wave (October 2021) were obtained from public health records. Cases of K-12 students, stratified by grade level (elementary, middle, and high school) and school districts across two counties, were included in the statistical and spatial analyses. COVID-19 case rates were determined and spatially mapped. Regression was performed adjusting for specific covariates. Results: Mask-wearing was associated with lower COVID-19 cases during the peak Delta variant period; overall, regardless of the Delta variant period, higher COVID-19 rates were seen in older aged students. Conclusion: This study highlights the need for more layered prevention strategies and policies that take into consideration local community transmission levels, age of students, and vaccination coverage to ensure that students remain safe at school while optimizing their learning environment.

Staphylococcus aureus (S. aureus) is a common cause of skin and soft tissue infections (SSTIs). Both the β-lactam resistant form, methicillin resistant S. aureus (MRSA), and non-resistant form, methicillin susceptible S. aureus (MSSA) are associated with disparate infections across age and race; community-associated infections are often due to S. aureus US300. Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) is a used in clinical microbiology laboratories to identify bacterial species, but limited data is available on if this tool can identify different strains of S. aureus. We hypothesize that MALDI-TOF MS can be used to identify strain-specific peaks of S. aureus USA300. Children were enrolled from emergency departments and swabbed for S. aureus (anterior nares, axilla, perirectal, oropharyngeal, and wounds) during two time periods (2006-2008 and 2015-2016). MALDI-TOF MS was performed on S. aureus cultured from study participants. Default parameters used for variance stability, smoothing, baseline subtraction, and normalization in R 4.4.3. and MALDIquant v1.22 were applied to raw data. Peak detection and alignment were performed with a signal-to-noise ratio of three and tolerance of 300 ppm. Binning and machine learning algorithms were used to identify intensity differences of each peak identified; significance of peaks was determined using Chi Square statistical analyses, comparing MRSA and MSSA, and then between all S. aureus isolates compared to USA300. Statistical significance was considered with p-value < 0.05 using R and SAS 9.4. From 57 MRSA and 53 MSSA isolates, 320 peaks were identified. 31 significant peaks were identified, with 14 unique peaks for MRSA and 17 for MSSA. With S. aureus USA300, eight of the 31 peaks remained statistically significant. MALDI-TOF MS can identify unique peak differences between MRSA and MSSA. This tool can be potentially used to distinguish S. aureus at the strain level in a clinical setting. MALDI-TOF MS can be an impactful and rapid diagnostic tool to detect antibiotic resistance in S. aureus in a clinical setting. Lilly Immergluck, MD,MS, Melinta: Grant/Research Support|Melinta: Grant/Research Support