Objective: To develop a method of consistently identifying interfacility transfers (IFTs) in Medicare Claims using patients with ST-Elevation Myocardial Infarction (STEMI) as an example.
Data Sources/study Setting: 100% Medicare inpatient and outpatient Standard Analytic Files and 5% Carrier Files, 2011-2020.
Study Design: Observational, cross-sectional comparison of patient characteristics between proposed and existing methods.
Data Collection/extraction Methods: We limited to patients aged 65+ with STEMI diagnosis using both proposed and existing methods.
Principal Findings: We identified 62,668 more IFTs using the proposed method (86,128 versus 23,460). A separately billable interfacility ambulance trip was found for more IFTs using the proposed than existing method (86% vs. 79%). Compared with the existing method, transferred patients under the proposed method were more likely to live in rural (p < 0.001) and lower income (p < 0.001) counties and were located farther away from emergency departments, trauma centers, and intensive care units (p < 0.001).
Conclusions: Identifying transferred patients based on two consecutive inpatient claims results in an undercount of IFTs and under-represents rural and low-income patients.
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http://dx.doi.org/10.1111/1475-6773.14367 | DOI Listing |
Ying Yong Sheng Tai Xue Bao
October 2024
College of Energy and Environmental Engineering, Hebei University of Engineering/Hebei Key Laboratory of Air Pollution Cause and Impact/Hebei Engineering Research Center of Sewage Treatment and Resource Utilization, Handan 056038, Hebei, China.
Estuaries are transitional zones between rivers and marine environments, with intensive human activities. Pollutants pose a threat to the ecological systems of estuaries. Among these pollutants, microplastics and antibiotic resistant genes have gained significant attention due to their potential impacts on estuarine organisms and human health.
View Article and Find Full Text PDFYing Yong Sheng Tai Xue Bao
October 2024
College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730030, China.
The construction of an ecological security pattern is crucial to maintain ecosystem health and stability, with great significance for regional sustainable development. Following the research paradigm of "ecological source areas-ecological resistance surfaces-ecological corridors", based on the index framework of "sensitivity-importance-connectivity", we identified the ecological source areas, generated the ecological resistance surface through graded weighting of underlying surface factors and point of interest (POI) method, determined the ecological corridor, pinch point, and obstacle area using circuit theory, and constructed the ecological security pattern of Guizhou Pro-vince. Results showed that the areas of extremely sensitive of rocky desertification and soil erosion and the areas of extremely important areas of water resources forming, soil and water conservation and biodiversity in Guizhou Pro-vince were generally small and distributed differently, accounting for 1.
View Article and Find Full Text PDFMycoses
January 2025
Department of Dermatology and Venereology, Peking University First Hospital, Beijing, China.
Objectives: Tinea capitis remains a common fungal infection in children worldwide. Species identification is critical for determining the source of infection and reducing transmission. In conventional methods, macro- and microscopic analysis is time-consuming and results in slow fungal growth or low specificity.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, Wuhan 430079, China.
Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs.
View Article and Find Full Text PDFProc (IEEE Int Conf Healthc Inform)
June 2024
College of Medicine, University of Florida, Gainesville, FL, USA.
Multivariate clinical time series data, such as those contained in Electronic Health Records (EHR), often exhibit high levels of irregularity, notably, many missing values and varying time intervals. Existing methods usually construct deep neural network architectures that combine recurrent neural networks and time decay mechanisms to model variable correlations, impute missing values, and capture the impact of varying time intervals. The complete data matrices thus obtained from the imputation task are used for downstream risk prediction tasks.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!