#70 Predicting place-based factors for Staphylococcus aureus infections within pediatric population using machine learning. (14th June 2022) - Lin, Xiting
Abstract: Background: Community-onset Staphylococcus aureus (CO-S. aureus) infections, methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA), have a high burden on United States (US) Emergency Departments and urgent care, particularly within the pediatric population. Although individual risk factors have been well-studied, the influence of specific geographic locality and place-based risks for CO-S. aureus infections have not been well-characterized. Maximum entropy (MaxEnt) is a machine learning technique for ecological niche modeling, which predicts the distribution of disease vectors and their possible disease transmissions using environmental and other relevant risk factors. The aim of this study is to predict socioecological factors that contribute to the spread of CO-S. aureus in a major urban area. Method: Electronic medical records from children with staphylococcal infections, who were treated at two pediatric hospitals from 2002 to 2016, were retrospectively reviewed. Children were included in the analyses if they had a confirmed S. aureus infection within 48 hours of hospital admission (i.e., CO-S. aureus), less than 19 years old, and a geo-referenced address within Atlanta's metropolitan statistical area (MSA). The timespan was divided into two periods, 2002-2005 (early) and 2006-2010 (later), mimicking the trend of CO-S. aureus. Fourteen place-based factors, obtained at the US Census Bureau block group level, were included in the MaxEnt model: population under 18 years old, Caucasian, African American, ethnicity, poverty, low education (high school diploma), high education (Bachelor's degree and above), crowding, nursery school enrollment, kindergarten enrollment, distance to K-12 school, distance to a children's hospital, distance to a daycare center, and population density. These were based on factors previously determined a priori. A total of four models (CO-MRSA early, CO-MSSA early, CO-MRSA later, and CO-MSSA later) were run using the MaxEnt software (v.3.4.1). For each model, 75% and 25% of data was randomly assigned to training and testing groups, respectively. Models were assessed by jack-knife tests. Results: 16, 124 records met the eligibility criteria and were included in the MaxEnt models. Preliminary analyses of data from 2002-2010 suggest training Area Under the Curve (AUC) ranging from 0.802 to 0.828 and the test AUC ranging from 0.796 to 0.809, demonstrating these models are performing very well. Population density had the highest contribution in predicting CO-MRSA and CO-MSSA locations, which was confirmed by jack-knife tests. Conclusion: By applying MaxEnt to pediatric CO-S. aureus infections in the Atlanta MSA, it was found that higher risks of CO-S. aureus infections may exist in more densely populated areas. MaxEnt can be utilized to identify potential future areas of CO-MRSA and CO-MSSA infections based on estimated or predicted changes to the place-based factors used to build these models, most notably population density. Predicted risk areas should have more frequent monitoring to prevent S. aureus infection outbreaks, which will also allow for more time to pool public health resources for these areas to quickly and effectively control outbreaks.