Patterns of emergency department use in rural and metropolitan New South Wales from 2012 to 2018.

OBJECTIVE: To explore the patterns of and investigate the factors associated with rises in emergency department presentations in rural and metropolitan New South Wales from 2012 to 2018. DESIGN: A retrospective descriptive study of de-identified data from the New South Wales Emergency Department Data Collection. SETTING: New South Wales, Australia. PARTICIPANTS: All individuals presenting to 99 New South Wales emergency departments, which continuously reported to the Emergency Department Data Collection between 2012 and 2018. A total of 2 166 449 presentations recorded throughout New South Wales in 2012 (rural 786 278; metropolitan 1 380 171) and 2 477 192 in 2018 (rural 861 761; metropolitan 1 615 431). MAIN OUTCOME MEASURES: Total emergency department presentations, plus Poisson regression modelled annual changes in emergency department presentations over the period 2012-2018. RESULTS: Growth in emergency department presentations outpaced population growth in both rural and metropolitan New South Wales between 2012 and 2018. The patterns of age-standardised rates of presentations were broadly similar between rural and metropolitan areas, with highest rates observed in the youngest (0-4 years) and oldest (85+ years) cohorts. The rural sample also displayed a distinct third peak in ages 15-39 years, and rates were higher across all age groups. Rural New South Wales displayed disproportionately higher emergency department presentations in the two most deprived socio-economic status quintiles. While rural New South Wales displayed significant reductions in triage category 5 (non-urgent cases) over time, the relative proportion remained approximately double that of metropolitan sites. CONCLUSIONS: There are differences between rural and metropolitan emergency department presentations relating to demographic factors, triage levels, acuity and admissions. Detailed local investigations are required to determine specific contextual issues that impact on emergency department demand.

Identification of metabolic pathways contributing to ER+ breast cancer disparities using a machine-learning pipeline.

African American (AA) women in the United States have a 40% higher breast cancer mortality rate than Non-Hispanic White (NHW) women. The survival disparity is particularly striking among (estrogen receptor positive) ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ breast cancer. We collected pretreatment plasma from AA and NHW ER+ breast cancer cases (AA n = 48, NHW n = 54) and cancer-free controls (AA n = 100, NHW n = 48) to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC-MS) to identify metabolites that may be altered in the different racial groups. Unpaired t-test combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from The Cancer Genome Atlas Program (TCGA). We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed decreased circulating levels of amino acids compared to healthy controls, while fatty acids were significantly higher in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant associations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene Negative Elongation Factor Complex E (NELFE) with histone methyltransferase activity, was associated with poor survival exclusively for AA individuals. We employed a comprehensive and novel approach that integrates multiple machine learning and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women. It may ultimately lead to the identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities.