Psychiatrists used a semi-structured Standardized Psychiatric Examination method to examine 810 adults drawn from a probability sample of eastern Baltimore residents in 1981. Of the population, 5.9% was found to be significantly depressed. DSM-III major depression (MD) had a prevalence of 1.1% and 'non-major depression' (nMD), our collective term for the other depressive disorder categories in DSM-III, had a prevalence of 3.4%. The two types of depression differed by sex ratio, age-specific prevalence, symptom severity, symptom profiles, and family history of suicide. Analyses using a multiple logistic regression model discerned that both types of depression were influenced by adverse life events, and that nMD was influenced strongly by gender, marital status, and lack of employment outside the home. Neither type of depression was influenced by income, education, or race. This study validates the concept of major depression as a clinical entity. Future studies of the aetiology, mechanism, and treatment of depression should distinguish between these two types of depression.
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http://dx.doi.org/10.1017/s0033291700038095 | DOI Listing |
Epidemiol Psychiatr Sci
January 2025
Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, Center for Global Mental Health, King's College London, London, UK.
In low- and middle-income countries, fewer than 1 in 10 people with mental health conditions are estimated to be accurately diagnosed in primary care. This is despite more than 90 countries providing mental health training for primary healthcare workers in the past two decades. The lack of accurate diagnoses is a major bottleneck to reducing the global mental health treatment gap.
View Article and Find Full Text PDFAppl Neuropsychol Adult
January 2025
Faculty Xavier Institute of Engineering, Mahim, India.
In the fields of engineering, science, technology, and medicine, artificial intelligence (AI) has made significant advancements. In particular, the application of AI techniques in medicine, such as machine learning (ML) and deep learning (DL), is rapidly growing and offers great potential for aiding physicians in the early diagnosis of illnesses. Depression, one of the most prevalent and debilitating mental illnesses, is projected to become the leading cause of disability worldwide by 2040.
View Article and Find Full Text PDFNaunyn Schmiedebergs Arch Pharmacol
January 2025
Graduate School of PLA Medical College, Chinese PLA General Hospital and PLA Medical College, 28 Fu Xing Road, Beijing, 100083, China.
Extensive researches illuminate a potential interplay between immune traits and psychiatric disorders. However, whether there is the causal relationship between the two remains an unresolved question. We conducted a two-sample bidirectional mendelian randomization by utilizing summary data of 731 immune cell traits from genome-wide association studies (GCST90001391-GCST90002121)) and 11 psychiatric disorders including attention deficit/hyperactivity disorder (ADHD), anxiety disorder, autism spectrum disorder (ASD), bipolar disorder (BIP), anorexia nervosa (AN), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), Tourette syndrome (TS), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), and substance use disorders (cannabis) (SUD) from the Psychiatric Genomics Consortium (PGC).
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Psychiatry, Nihon University School of Medicine, Tokyo, Japan.
Introduction: Preventing depression among nurses is a critical issue from the perspective of occupational welfare, but associations between depressive symptoms in nurses and stress-coping strategies remain unclear.
Methods: In the present study, an epidemiological study was conducted based on a cross-sectional questionnaire survey. Data obtained from 2,534 female nurses working at three general hospitals in Tokyo, Japan, were analyzed.
BMC Public Health
January 2025
The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Level 6, Jane Foss Russell Building, Sydney, NSW, 2006, Australia.
Background: Preventure is a selective school-based personality-targeted program that has shown long-term benefits in preventing student alcohol use, internalising and externalising problems when delivered by psychologists. In this first Australian randomised controlled trial of school staff implementation of Preventure, we aimed to examine i) acceptability, feasibility, and fidelity and ii) effectiveness of Preventure on student alcohol use, internalising, and externalising symptoms.
Methods: A cluster-randomised controlled implementation trial was conducted in Sydney, Australia and was guided by the RE-AIM framework (Glasgow et al.
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