ABSTRACT
Objective: To assess the prevalence of common mental disorders (CMD) and the association of CMD with sociodemographic characteristics in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) cohort. Methods: We analyzed data from the cross-sectional baseline assessment of the ELSA-Brasil, a cohort study of 15,105 civil servants from six Brazilian cities. The Clinical Interview Schedule-Revised (CIS-R) was used to investigate the presence of CMD, with a score ≥ 12 indicating a current CMD (last week). Specific diagnostic algorithms for each disorder were based on the ICD-10 diagnostic criteria. Prevalence ratios (PR) of the association between CMD and sociodemographic characteristics were estimated by Poisson regression. Results: CMD (CIS-R score ≥ 12) was found in 26.8% (95% confidence intervals [95%CI] 26.1-27.5). The highest burden occurred among women (PR 1.9; 95%CI 1.8-2.0), the youngest (PR 1.7; 95%CI 1.5-1.9), non-white individuals, and those without a university degree. The most frequent diagnostic category was anxiety disorders (16.2%), followed by depressive episodes (4.2%). Conclusion: The burden of CMD was high, particularly among the more socially vulnerable groups. These findings highlight the need to strengthen public policies aimed to address health inequities related to mental disorders.
Subject(s)
Humans , Male , Female , Adult , Aged , Anxiety Disorders/epidemiology , Sociological Factors , Mental Disorders/epidemiology , Sleep Wake Disorders/epidemiology , Brazil/epidemiology , Sex Factors , Prevalence , Cross-Sectional Studies , Age Distribution , Educational Status , Fatigue/epidemiology , Interview, Psychological , Middle AgedABSTRACT
Current theoretical models of emotion highlight the importance of distinguishing depression and anxiety. The present article critically evaluates a number of these models and provides a practical framework that could be applied in future studies to better understand the neural substrates that contribute to variation in anxiety and depressed mood. One influential model, the tripartite model, suggests that depression and anxiety can be distinguished on the basis of anhedonia and hyperarousal. Yet this model is based predominantly on questionnaire data. A more direct and powerful method to test this model is to identify biological markers of arousal and anhedonia. Other influential models, such as the approach-withdrawal and valence-arousal models, are based on biological measures and integrate the concept of arousal - but have generally restricted empirical enquiry into resting state paradigms, without an integrative approach to explore concurrent physiological arousal using autonomic measures, or to extend into emotion processing paradigms. The authors propose a practical framework that will have significant implications for theoretical models of depression and anxiety including integration of influential models of emotion and advancement of the knowledge base, clarification of the neurobiological specificity of depression and anxiety and identification of overlapping and distinctive features of these disorders.