The war and siege in Tigray led millions to displace internally. More than three-fourths of the health facilities were either destroyed or not functional as the equipment and other resources were stolen. Furthermore, the remaining functioning health facilities were flooded beyond their capacity, resulting in many patients received treatment late, and ending with complications including life loss. Mekelle City is one of the largest towns with many internally displaced people from different areas of Tigray. To provide services for the most vulnerable populations, 11 IDP clinics were opened for internally displaced people and the surrounding host community in Mekelle. A total of 6732 patients received clinical services, of which 3465 were males. The age of the patients was in ranged of 24 days to 95 years. A total of 364 patients were emergency cases and 428 outbreaks were seen. A total of 722 patients with chronic illnesses received follow-up services, the most common being hypertension (112), diabetes (79), and asthma (70). Overall, 1198 investigations were done and 1339 were referred to higher-level healthcare facilities. Upper respiratory infection (n = 976), acute gastroenteritis (n = 667), and pneumonia (n = 612) were the most common disease conditions in IDP clinics. Antibiotics were the most commonly prescribed medication for 2468 patients, followed by anti-pain/pyretic (1402). This community engagement showed us that, it is possible to continue healthcare services when health facilities get collapsed during crisis owing to the relocation and mobilization of available resources.
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http://dx.doi.org/10.2147/RMHP.S426627 | DOI Listing |
Neuroimage
December 2024
Department of Computer Science, University of California, Irvine, CA 92617, USA. Electronic address:
We show in this work that incorporating geometric features and geometry processing algorithms for mouse brain image registration broadens the applicability of registration algorithms and improves the registration accuracy of existing methods. We introduce the preprocessing and postprocessing steps in our proposed framework as RegBoost. We develop a method to align the axis of 3D image stacks by detecting the central planes that pass symmetrically through the image volumes.
View Article and Find Full Text PDFEur J Orthop Surg Traumatol
December 2024
University Hospitals Cleveland Medical Center, Cleveland, USA.
Purpose: Olecranon osteotomy has been associated with loss of reduction, nonunion, implant failure, and migration of wires. We aim to evaluate quality of reduction of the osteotomy site as a predictor of olecranon osteotomy nonunion.
Methods: One hundred and twenty-five distal humerus fractures that underwent open reduction internal fixation (ORIF) were reviewed.
J Bone Joint Surg Am
October 2024
Musculoskeletal Tumor Center, Department of Orthopedics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, People's Republic of China.
Background: Pelvic reconstruction after type I + II (or type I + II + III) internal hemipelvectomy with extensive ilium removal is a great challenge. In an attempt to anatomically reconstruct the hip rotation center (HRC) and achieve a low mechanical failure rate, a custom-made, 3D-printed prosthesis with a porous articular interface was developed. The aim of this study was to investigate the clinical outcomes of patients treated with this prosthesis.
View Article and Find Full Text PDFPrehosp Disaster Med
December 2024
Medicine and Surgery, The Aga Khan University, Karachi, Pakistan.
In the aftermath of the 2022 Pakistan flooding, disaster management faced critical challenges, particularly in mental health support. This study analyzed an incident where eighteen internally displaced individuals lost their lives in a bus fire. The current approach involves a comprehensive analysis of the incident, exploring the difficulties encountered in managing relief efforts, and providing mental health support.
View Article and Find Full Text PDFCureus
November 2024
Cardiology, University of Arizona College of Medicine, Phoenix, USA.
Artificial intelligence (AI) and machine learning (ML) have become critical components in the transformation of healthcare. They offer enhanced diagnostic accuracy, personalized treatment plans, and support for clinical decision-making. However, with these advancements come significant ethical challenges, including concerns around transparency, bias, data privacy, and the potential displacement of healthcare professionals.
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