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Objectives: Depression and anxiety are common mental disorders in later life. Few population-based studies have investigated their potential impacts on mortality in low- and middle-income countries (LMICs). The aim of this study is to examine the associations between depression, anxiety, their comorbidity, and mortality in later life using a population-based cohort study across eight LMICs.
Methods: This analysis was based on the 10/66 cohort study including 15 991 people aged 65 years or above in Cuba, Dominican Republic, Venezuela, Mexico, Peru, Puerto Rico, China, and India, with an average follow-up time of 3.9 years. Subthreshold and clinical levels of depression were determined using EURO-D and ICD-10 criteria, and anxiety was based on Geriatric Mental State (GMS)-Automated Geriatric Examination for Computer Assisted Taxonomy (AGECAT). Cox proportional hazard modelling was used to estimate how having depression, anxiety, or both was associated with mortality adjusting for sociodemographic and health factors.
Results: Participants with clinical depression (hazard ratio [HR]: 1.45; 95% CI, 1.24-1.70) and subthreshold anxiety (HR: 1.26; 95% CI, 1.15-1.38) had higher risk of mortality than those without the conditions after adjusting for sociodemographic factors and health conditions. Comorbidity of depression and anxiety was associated with a 30% increased risk of mortality but the effect sizes varied across countries (Higgins I = 58.8%), with the strongest association in India (HR: 1.99; 95% CI, 1.21-3.27).
Conclusions: Depression and anxiety appear to be associated with mortality in older people living in LMICs. Variation in effect sizes may indicate different barriers to health service access across countries. Future studies may investigate underlying mechanisms and identify potential interventions to reduce the impact of common mental disorders.
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http://dx.doi.org/10.1002/gps.5211 | DOI Listing |
BMC Psychol
December 2024
Beijing Infinite Brain Technology Co., Ltd, Beijing, 100022, PR China.
Background: Traditional cognitive assessments, often reliant on paper-and-pencil tests and professional evaluators, suffer from subjectivity and limited result discrimination. This study introduces the Baguan Online Cognitive Assessment System (BOCAS), a tablet-based system that evaluates both general cognitive ability (GCA) and domain-specific functions across six domains: sensory-motor skills, processing speed, sustained attention, working memory, cognitive flexibility, and spatial ability.
Methods: BOCAS was validated with 151 healthy Chinese adults aged 18-40.
J Transl Med
December 2024
Department of Rheumatology and Immunology, Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Tianhe District, Guangzhou, China.
Background: Corona virus disease 2019 (COVID-19) reinfection, particularly short-term reinfection, poses challenges to the management of rheumatic diseases and may increase adverse clinical outcomes. This study aims to develop machine learning models to predict and identify the risk of short-term COVID-19 reinfection in patients with rheumatic diseases.
Methods: We developed four prediction models using explainable machine learning to assess the risk of short-term COVID-19 reinfection in 543 patients with rheumatic diseases.
BMC Med Inform Decis Mak
December 2024
Department of Cardiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
Background: Psychological disorders, such as anxiety and depression, are considered to be one of the causes of noncardiac chest pain (NCCP). And these patients can be challenging to differentiate from coronary artery disease (CAD), leading to a considerable number of patients still undergoing angiography. We aim to develop a practical prediction model and nomogram using cardiopulmonary exercise testing (CPET), to help identify these patients.
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