This study explores the social gradient of psychiatric morbidity. The Hong Kong Mental Morbidity Survey (HKMMS), consisting of 5719 Chinese adults aged 16 to 75 years, was used. The Chinese version of the Revised Clinical Interview Schedule (CIS-R) was employed for psychiatric assessment of common mental disorders (CMD). People with a less advantaged socioeconomic position (lower education, lower household income, unemployment, small living area and public rental housing) had a higher prevalence of depression and anxiety disorder. People with lower incomes had worse physical health (OR 2.01, 95% CI 1.05-3.82) and greater odds of having CMD in the presence of a family history of psychiatric illnesses (OR 1.67, 95% CI 1.18-2.36). Unemployment also had a greater impact for those in lower-income groups (OR 2.67; 95% CI 1.85-3.85), whereas no significant association was observed in high-income groups (OR 0.56; 95% CI 0.14-2.17). Mitigating strategies in terms of services and social support should target socially disadvantaged groups with a high risk of psychiatric morbidity. Such strategies include collaboration among government, civil society and business sectors in harnessing community resources.
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http://dx.doi.org/10.3390/ijerph19127095 | DOI Listing |
PLoS Med
January 2025
Division of Infectious Diseases, Department of Medicine II, Medical Centre and Faculty of Medicine, Albert-Ludwigs-University, Freiburg, Germany.
Background: Self-reported health problems following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are common and often include relatively non-specific complaints such as fatigue, exertional dyspnoea, concentration or memory disturbance and sleep problems. The long-term prognosis of such post-acute sequelae of COVID-19/post-COVID-19 syndrome (PCS) is unknown, and data finding and correlating organ dysfunction and pathology with self-reported symptoms in patients with non-recovery from PCS is scarce. We wanted to describe clinical characteristics and diagnostic findings among patients with PCS persisting for >1 year and assessed risk factors for PCS persistence versus improvement.
View Article and Find Full Text PDFGerontologist
January 2025
Department of Neurosciences, School of Medicine, University of California San Diego, San Diego, CA, USA.
Background And Objectives: While Hispanic/Latino populations in the U.S. are remarkably diverse in terms of birthplace and age at migration, we poorly understand how these factors are associated with cognitive aging.
View Article and Find Full Text PDFJ Acquir Immune Defic Syndr
January 2025
Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California-San Francisco, School of Medicine, San Francisco, California 675 18th Street, San Francisco, CA 94107.
Background: People with schizophrenia spectrum disorders are at elevated risk of HIV, and people with both HIV and schizophrenia are at elevated risk of death compared to individuals with either diagnosis alone. Limited research has assessed the HIV care cascade, and in particular retention in care, among people with HIV (PWH) and schizophrenia in the U.S.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
Department of Child and Adolescent Psychiatry-Psychotherapy, University Hospital Ulm, Ulm, Germany.
Importance: Associations between child maltreatment (CM) and health have been studied broadly, but most studies focus on multiplicity (number of experienced subtypes of CM). Studies assessing multiple CM characteristics are scarce, partly due to methodological challenges, and were mostly conducted in patient samples.
Objective: To determine the importance of CM characteristics in association with physical multimorbidity in adulthood for women and men in a German representative sample.
Proc Natl Acad Sci U S A
January 2025
Department of Psychology, City College, City University of New York, New York, NY 10031.
Looking at the world often involves not just seeing things, but feeling things. Modern feedforward machine vision systems that learn to perceive the world in the absence of active physiology, deliberative thought, or any form of feedback that resembles human affective experience offer tools to demystify the relationship between seeing and feeling, and to assess how much of visually evoked affective experiences may be a straightforward function of representation learning over natural image statistics. In this work, we deploy a diverse sample of 180 state-of-the-art deep neural network models trained only on canonical computer vision tasks to predict human ratings of arousal, valence, and beauty for images from multiple categories (objects, faces, landscapes, art) across two datasets.
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