The COVID-19 has spread to everywhere since its emergence from Wuhan. In countries with a low vaccination rate, the use of facemasks is essential to limit the risk of COVID-19 transmission. We have conducted this study in June 2021 to estimate the prevalence of facemask usage, and investigate the use of different types of facemasks and their distribution among pedestrians in the most crowded urban districts of Kabul during the third COVID-19 wave in Afghanistan. Using a multistage sampling method, a total of 5,000 pedestrians were selected from five most crowded urban districts of the city. The data was gathered by an observational method. The percentage, mean, and standard deviation were used to describe the variables. The χ2 test analysis was used to assess the relationship between two categorical variables. Of the 5,000 observations, the most common age group was 10-39 years with high participation of male (87.2%). A total of 2,013 (40.3%) people used facemasks (95% CI). Females used facemasks significantly more than males (64.6% versus 36.7%, P < 0.001). Among the pedestrians who used a facemask, most of them (88.6%) wore their facemask correctly. In conclusion the prevalence of facemask use in Kabul was fairly low especially among elderly people (≥ 60 years). Hence, the observed rates probably cannot protect the community against the COVID-19. Therefore, it is important to emphasize the public health recommendations via educational programs and national campaigns to support the strict use of facemasks in public places.
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http://dx.doi.org/10.4269/ajtmh.21-1070 | DOI Listing |
Sci Rep
January 2025
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.
Given the increasing urban population and frenetic mobility, understanding how individuals perceive crowding at large-scale events is crucial for effective crowd management and safety. This study focuses on Tokyo Big Sight in Japan exhibitions to examine participants' perceptions of peak crowding times, locations, and local density, and compare them with the actual measurements. Our methodology integrated questionnaires with beacon tag data.
View Article and Find Full Text PDFPrehosp Emerg Care
January 2025
Department of Emergency Medicine, MetroHealth Medical Center, Cleveland, Ohio.
Objectives: Opioid-associated fatal and non-fatal overdose rates continue to rise. Prehospital overdose education and naloxone distribution (OEND) programs are attractive harm-reduction strategies, as patients who are not transported by EMS after receiving naloxone have limited access to other interventions. This narrative summary describes our experiences with prehospital implementation of evidence-based OEND practices across Ohio as part of the HEALing Communities Study (HCS).
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation.
View Article and Find Full Text PDFInj Prev
January 2025
Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA.
Introduction: George Floyd's death in 2020 galvanised large protests around the country, including the emergence of the Capitol Hill Autonomous Zone (CHAZ) in Seattle, Washington, a non-policed, organised protest region that may have differing injury risks than other regions. We sought to quantitatively describe characteristics of injuries related to protests documented at visits to two nearby major emergency departments, including the only Level 1 trauma centre in the state.
Methods: Using the International Classification of Diseases, 10th Revision code inclusion criteria, we identified 1938 unique patient visits across the two emergency departments from 29 May 2020 and 1 July 2020.
Am J Emerg Med
December 2024
Department of Emergency Medicine, Sisli Hamidiye Etfal Training and Research Hospital, Istanbul, Turkey.
Background: The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accuracy. This study evaluates the performance of ChatGPT and Copilot in triage at a high-volume urban hospital, hypothesizing that these tools can match trained physicians' accuracy and reduce human bias amidst ED crowding challenges.
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