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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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Antibiotic resistant bacteria like community-onset methicillin resistant Staphylococcus aureus (CO-MRSA) have continued to cause infections in children at alarming rates and are associated with health disparities. Geospatial analyses of individual and area level data can enhance disease surveillance and identify socio-demographic and geographic indicators to explain CO-MRSA disease transmission patterns and risks., Methods: A case control epidemiology approach was undertaken to compare children with CO-MRSA to a noninfectious condition (unintentional traumatic brain injury (uTBI)). In order to better understand the impact of place based risks in developing these types of infections, data from electronic health records (EHR) were obtained from CO-MRSA cases and compared to EHR data from controls (uTBI). US Census data was used to determine area level data. Multi-level statistical models were performed using risk factors determined a priori and geospatial analyses were conducted and mapped., Results: From 2002-2010, 4,613 with CO-MRSA and 34,758 with uTBI were seen from two pediatric hospitals in Atlanta, Georgia. Hispanic children had reduced odds of infection; females and public health insurance were more likely to have CO-MRSA. Spatial analyses indicate significant 'hot spots' for CO-MRSA and the overall spatial cluster locations, differed between CO-MRSA cases and uTBI controls., Conclusions: Differences exist in race, age, and type of health insurance between CO-MRSA cases compared to noninfectious control group. Geographic clustering of cases is distinct from controls, suggesting placed based factors impact risk for CO-MRSA infection., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2019 The Author(s).)

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.

Background: Randomized trials demonstrate clear benefits of mammography screening in women through age 74 years. We explored age- and race-specific rates of mammography screening and breast cancer mortality among women aged 69 to 84 years., Methods: We analyzed Medicare claims data for women residing within Surveillance, Epidemiology and End Results geographic areas from 1995 to 2009 from 64,384 non-Hispanic women (4886 black and 59,498 white) and ascertained all primary breast cancer cases diagnosed between ages 69 and 84 years. The exposure was annual or biennial screening mammography during the 4 years immediately preceding diagnosis. The outcome was breast cancer mortality during the 10 years immediately after diagnosis., Results: After adjustment for stage at diagnosis, radiation therapy, chemotherapy, comorbid conditions, and contextual socioeconomic status, hazard ratios (and 95% confidence intervals) for breast cancer mortality relative to no/irregular mammography at 10 years for women aged 69 to 84 years at diagnosis were 0.31 (0.29-0.33) for annual mammography and 0.47 (0.44-0.51) for biennial mammography among whites and 0.36 (0.29-0.44) for annual mammography and 0.47 (0.37-0.58) for biennial mammography among blacks. Trends were similar at 5 years overall and stratified by ages 69 to 74 years, 75 to 78 years, and 79 to 84 years., Conclusions: In these Medicare claims and Surveillance, Epidemiology and End Results data, elderly non-Hispanic women who self-selected for annual mammography had lower 10-year breast cancer mortality than corresponding women who self-selected for biennial or no/irregular mammography. These findings were similar among black and white women. The data highlight the evidentiary limitations of data used for current screening mammography recommendations., (Copyright © 2015 Elsevier Inc. All rights reserved.)