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Background : The nationwide epidemiology and clinical practice patterns for younger children hospitalized with urinary tract infections (UTIs) were unclear. Methods: We conducted a retrospective observational study consisting of 32,653 children aged < 36 months who were hospitalized with UTIs from 856 medical facilities during fiscal years 2011–2018 using a nationally representative inpatient database in Japan. We investigated the epidemiology of UTIs and changes in clinical practice patterns (e.g., antibiotic use) over 8 years. A machine learning algorithm of multivariate time-series clustering with dynamic time warping was used to classify the hospitals based on antibiotic use for UTIs. Results: We observed marked male predominance among children aged < 6 months, slight female predominance among children aged > 12 months, and summer seasonality among children hospitalized with UTIs. Most physicians selected intravenous second- or third-generation cephalosporins as the empiric therapy for treating UTIs, which was switched to oral antibiotics during hospitalizations for 80% of inpatients. Whereas total antibiotic use was constant over the 8 years, broad-spectrum antibiotic use decreased gradually from 5.4 in 2011 to 2.5 days of therapy per 100 patient-days in 2018. The time-series clustering distinctively classified 5 clusters of hospitals based on antibiotic use patterns and identified hospital clusters that preferred to use broad-spectrum antibiotics (e.g., antipseudomonal penicillin and carbapenem). Conclusions: Our study provided novel insight into the epidemiology and practice patterns for pediatric UTIs. Time-series clustering can be useful to identify the hospitals with aberrant practice patterns to further promote antimicrobial stewardship. [ABSTRACT FROM AUTHOR]

Gravel riverbeds in the middle reaches of Japanese rivers are essential habitats for various plants and animals. Disturbance from flooding is necessary for the formation of gravel riverbeds, but human control of rivers, such as dams and channelization, has altered flow and sediment regimes, thereby reducing disturbance. The flooding generates a mosaic pattern characterized by varying frequencies and intensities of disturbance in gravel riverbeds. Understanding the disturbance regimes that form mosaic patterns is important for the conservation of biodiversity in rivers. In this study, we proposed a method to extract mosaic patterns from flow velocity regimes obtained by planar two‐dimensional analysis by classifying them with time‐series clustering. Based on the distribution of Anaphalis margaritacea var. yedoensis on gravel riverbanks, we compared several past flooding events to identify mosaic patterns that are important for A. margaritacea var. yedoensis. The study site is the Echi River, which flows through Shiga Prefecture in Japan and into Lake Biwa. Using a unmanned aerial vehicle (UAV), orthomosaic images with an average ground resolution of 3.3 mm/pixel were created, and colony polygons of A. margaritacea var. yedoensis were created using image detection and visual correction. Hydraulic analysis was performed using iRIC ver2.3 (Nays2DH ver1.0). Time‐series clustering was used to classify the flow velocity regimes for each computed mesh into 30 clusters. The relationship between the clusters of each flooding event and the distribution of A. margaritacea var. yedoensis was evaluated. Mosaic patterns were created by classifying the flow velocity regimes of each computational mesh calculated by planar 2D analysis into clusters using time‐series clustering. After analyzing the relationship between each cluster and the area of distribution of A. margaritacea var. yedoensis, the first flooding event was determined to be the mosaic pattern that best explained the distribution of A. margaritacea var. yedoensis. Cluster 1, the "low peak, short duration type," was considered the growth center of A. margaritacea var. yedoensis. The method used in this study is an innovative approach for obtaining mosaic patterns that quantifies these five elements of the disturbance regime. [ABSTRACT FROM AUTHOR]