Introduction: Atrial fibrillation (AF) is the most common arrhythmia globally. It is associated with a fivefold risk in stroke, but early diagnosis and effective treatment can reduce this risk. AF is often underdiagnosed, particularly in low-income and middle-income countries (LMICs) where screening for AF is not always feasible or considered common practice in primary care settings. Epidemiological data on AF in LMICs is often incomplete particularly in vulnerable populations. This LMIC research collaborative aims to identify the prevalence of AF in the Northern Sri Lankan community.
Methods And Analysis: A cross-sectional household survey piloted and codesigned through a series of community engagement events will be administered in all five districts in Northern Province, Sri Lanka. A multistage cluster sampling approach will be used starting at district level, then the Divisional Secretariats followed by Grama Niladhari divisions. Twenty households will be selected from each cluster. The study aims to recruit 10 000 participants aged 50 years or older, 1 participant per household. Demographic and socioeconomic characteristics, well-being and lifestyle and anthropometric measurements will be collected using a digital data platform (REDCap, Research Electronic Data Capture) by trained data collectors. Participants will be screened for AF using a fingertip single-lead ECG via a smartphone application (AliveCor) with rhythm strips reviewed by a consultant cardiologist. Prevalence of AF and risk factors will be established at province and district-levels. Adjusted ORs and population attributable fractions for AF risk factors will be determined.
Ethics And Dissemination: This study was approved by the Ethics Review Committee of Faculty of Medicine at University of Jaffna. Written informed consent will be obtained from all participants. Findings will be disseminated through publication in a peer-reviewed journal and presentations at conferences. The findings will enable early treatment for new AF diagnoses and inform interventions to improve community-based management of AF in LMICs.
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http://dx.doi.org/10.1136/bmjopen-2021-056480 | DOI Listing |
J Eval Clin Pract
February 2025
Faculty of Health Sciences, Department of Nursing, Division of Public Health Nursing, Bandırma Onyedi Eylül University, Balıkesir, Turkey.
Aim: This study aimed to translate the Environmental Health Literacy Scale (EHLS) into Turkish and assess its construct validity and internal consistency.
Methods: This research employs a methodological design. The research was conducted during the 2022-2023 academic year with a sample of 500 students from the Faculty of Health Sciences.
AIDS Care
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International Health Program, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Voluntary medical male circumcision (VMMC) was identified as an effective strategy in HIV prevention. Although circumcision reduces heterosexual acquisition of HIV by 60%, there is low uptake of VMMC services in Eswatini. This study applies the health belief model (HBM) in understanding perceptions of young men in Eswatini towards VMMC for HIV prevention to upscale its adoption.
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Hepatobiliary and Pancreatic Medical Treatment Center, People's Hospital of Xinjiang Uygur, Autonomous Region, Tianchi road, Urumqi, 830011, China.
With the advancement of precise hepatobiliary surgery concepts, the diagnostic and therapeutic approaches for hepatic echinococcosis have undergone significant transformations. However, whether these changes have correspondingly improved patient outcomes remains unclear. A retrospective analysis of these changes will provide crucial guidance for the prevention and treatment of hepatic echinococcosis.
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December 2024
Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.
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