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The territorialization of Severe Acute Respiratory Syndrome and its socioeconomic, demographic and public health policy risk factors in Belém, state of Pará, Eastern Amazon, Brazil: a cross-sectional and ecological study.
PLoS One
March 2025
Laboratory of Epidemiology and Geoprocessing of Amazon, University of the State of Pará (UEPA), Belém, Brazil.
Severe Acute Respiratory Syndrome is an important public health problem in Brazil due to the large number of cases. It has a high mortality rate related to risk factors that include systemic arterial hypertension, type 2 diabetes mellitus, male gender and advanced age. This cross-sectional and ecological study analyzed the spatial distribution of this disease related to the evolution of COVID-19 cases and their epidemiological, demographic, socioeconomic and public health policy conditions in the administrative districts of Belém, state of Pará, in the eastern Brazilian Amazon, from 2021 to 2023.
View Article and Find Full Text PDFUtilization of long-acting contraceptive methods and associated factor among women of reproductive age in East Africa: A multi-level analysis of recent Demographic and Health Surveys.
PLoS One
March 2025
Department of Optometry, School of Medicine, University of Gondar, Comprehensive Specialized Hospital, Gondar, Ethiopia.
Background: Comprehensive family planning is essential for reproductive health, allowing individuals to make informed choices about family size and enhancing maternal and child health. Long-acting contraceptives (LACs) are known for their high efficacy and consistent use. This study examines the prevalence and determinants of LAC utilization among women of reproductive-age in 11 East African countries.
View Article and Find Full Text PDFRemote work and long-term sickness absence due to mental disorder trends among Japanese workers pre/post COVID-19.
PLoS One
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Department of Neuropsychiatry, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan.
Aims: The aim of this study was to ascertain whether there has been an increase in the number of workers with long-term sickness absence due to mental disorders (LTSA-MD) and determine the impact of remote work on new LTSA-MD cases.
Methods: A web-based questionnaire was sent to 2,552 company offices with 150 or more workers in Osaka Prefecture. Data were obtained on the number of workers with LTSA-MD between April 1, 2019, and March 31, 2020 (fiscal year 2019) and between April 1, 2020, and March 31, 2021 (fiscal year 2020), along with their MD diagnoses (adjustment disorder [AD], depressive disorder [DEP], etc.
Women's attitudes towards physical intimate partner violence in India: Trends, patterns, and determinants.
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Department of Liberal Arts, Indian Institute of Technology Hyderabad, Sangareddy, Telangana, India.
Women's attitudes towards physical intimate partner violence are a major determinant of the likelihood of their exposure to physical intimate partner violence. In this study, we scrutinize the third, fourth, and fifth rounds of the National Family Health Survey using descriptive analyses and logistic regression models to understand the trends, patterns, and drivers of women's attitudes towards physical intimate partner violence across various demographic and socioeconomic groups in India. Our findings reveal a noticeable decline in the level of women's acceptability of physical intimate partner violence over the past 15 years, albeit at a slow pace.
View Article and Find Full Text PDFSustainable visions: unsupervised machine learning insights on global development goals.
PLoS One
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Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Coyoacán, Ciudad de México, México.
The 2030 Agenda for Sustainable Development of the United Nations outlines 17 goals for countries of the world to address global challenges in their development. However, the progress of countries towards these goal has been slower than expected and, consequently, there is a need to investigate the reasons behind this fact. In this study, we have used a novel data-driven methodology to analyze time-series data for over 20 years (2000-2022) from 107 countries using unsupervised machine learning (ML) techniques.
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