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BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

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Objective: To investigate whether having a higher number of depressive symptoms is associated with negative self-rated health (SRH) even in the absence of illness. Methods: This is a secondary analysis of baseline data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil), conducted in 2015-2016, using a national sample of 9,412 people aged 50 or over. SRH was dichotomized into poor or very poor and very good or excellent, good, or average. Depressive symptoms were assessed through the eight-item Center for Epidemiologic Studies Depression Scale (CES-D8). Sociodemographic variables, information about unhealthy behaviors, and the number of chronic conditions were also analyzed. Results: Having depressive symptoms was strongly associated with poor or very poor SRH both in the unadjusted and adjusted analyses. The magnitude of the association was reduced when the number of chronic illnesses was included in the multivariate analysis, along with the other sociodemographic variables and unhealthy behaviors (OR 1.35, 95%CI 1.31-1.39). Conclusion: Having depressive symptoms may contribute towards having a poorer perception of health, even in the absence of health conditions. SRH is a multidimensional construct that can accurately reflect a person's state of general mental health.

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OBJECTIVE: This study investigates whether having a higher number of depressive symptoms is associated with negative self-rated health even in the presence of illnesses. METHODS: This is a secondary analysis of the baseline data from the Longitudinal Study of the Health of Elderly Brazilians (ELSI - Brazil), conducted in 2015-2016, using a national sample of 9,412 people aged 50 or over. Self-rated health was dichotomized into "bad or very bad" and "very good or excellent, good or average". Depressive symptoms were assessed through the eight-item Center for Epidemiologic Studies Depression (CES-D8). Sociodemographic variables, information about unhealthy behaviours, and the number of chronic conditions were also analysed. RESULTS: Having depressive symptoms was strongly associated with bad or very bad self-rated health both in the unadjusted and adjusted analyses. There was a reduction in the magnitude of the association when the number of chronic illnesses was included in the multivariate analysis, along with the other sociodemographic variables and unhealthy behaviours (OR 1,35 95% CI 1,31 - 1,39). CONCLUSION: Having depressive symptoms may contribute towards having a poorer perception of health, even in the absence of health conditions. Self-rated health is a multidimensional construct that can accurately reflect a person's state of general mental health.

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BACKGROUND: Precarious employment conditions can influence the worker's mental health; however, there is no consensus regarding the definition of precarious employment or the way it is measured. OBJECTIVE: The objective is to identify existing research of the conceptual framework, the ways to measure precarious employment and its impact on the mental health of workers. METHODS: A systematic review with the strategic search for observational-empirical and qualitative studies published between 2007 and 2020 in Embase, Scopus and PubMed, full text, in English and Spanish. Studies were selected and excluded, according to eligibility criteria. Two independent reviewers and one arbitrator evaluated the quality of selected papers with the STROBE guidelines for observational studies and SRQR for qualitative ones. RESULTS: 408 studies were obtained, 21 met inclusion criteria, in 14 studies precarious employment was measured one-dimensionally. Of these, 11 with the dimension of temporality and three with insecurity. Four studies it was measured in a multidimensional way, with the Employment Precariousness Scale (EPRES) and in three qualitative designs, with different categories of analysis. Mental health was measured with SF-36 (n = 4), GHQ-12 (n = 3) and CES-D (n = 3). CONCLUSIONS: The concept of precarious employment requires a multidimensional construct, although the tendency to measure precarious employment is one-dimensional. Regardless of how is measured, this has a negative impact on the mental health of workers. The outcomes related to this social determinant included depression, depressive symptoms, psychological distress, stress, and suicidal thoughts. Youths, women, people with low levels of education and immigrants are the groups that show the major precariousness.

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Background: Despite numerous efforts to assess the impact of the COVID-19 pandemic on mental health, there are few longitudinal studies that examine the change in the burden of psychological distress over time and its associated factors, especially in developing countries. Objective: The primary aim of this study was to assess the levels of psychological distress at two time points during the COVID-19 outbreak based on a representative community sample in Chile. The secondary aim was to identify groups that are more vulnerable to psychological distress during the pandemic. Methods: A nationally representative, longitudinal telephone survey of Chilean adults was conducted. This study analyses panel data from two waves in 2020: May 30 to June 10 and September 15 to October 9. A total of 823 people participated in both surveys. Changes in mental health outcomes (anxiety and depressive symptoms) were assessed, estimating the effect of demographic characteristics, psychosocial and economic factors, household conditions, and health status. Results: There was a significant increase in psychological distress (PHQ-4 ≥ 6) between Waves 1 (22.6%) and 2 (27.0%), especially among younger participants. Overall, the results of this study show that being female, living in or near the capital, living in overcrowded households and having a perceived lack of space in the home, loneliness or perceived social isolation, and having received mental health treatment within the last year are significantly associated with psychological distress over time (p < 0.05). Conclusion: This study highlights the need to implement psychosocial programs to protect people's psychological well-being, as well as social policies to improve household living conditions and levels of social connectedness during the COVID-19 outbreak.

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Objectives: Over the last decade, an increasing number of empirical studies have examined long-term patterns of depression among adults around retirement age and identified employment status as a crucial determinant. However, most research has examined associations between cross-sectional measures of employment and prospective depression patterns, overlooking the changing nature of employment statuses, particularly close to retirement age. Furthermore, most knowledge in this field comes from studies conducted in developed countries in Western Europe and North America. To address these gaps, this study examined simultaneous trajectories in the employment and depressive symptom domains among two age groups of Chileans before and after the standard retirement age. Method: Using population-representative data and longitudinal statistical methods, we identified different trajectory types among two age groups (one aged 56-65 and another aged 66-75, at baseline) and characterized them according to social and health characteristics.Results: We found that trajectories defined by permanent employment were accompanied by lower depressive symptoms than trajectories indicating either retirement or inactivity. However, trajectories combining employment and the absence of depressive symptoms were primarily followed by individuals with advantaged health and social statuses at the baseline. Conclusion: Public policies aimed at promoting the mental health of older adults through their labor market integration risk forcing individuals who have accumulated social and health disadvantages across the life course to work longer.

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This study examined the association between widowhood and depressive symptoms and the extent to which the association is contingent upon risk and resiliency, including immigration status, functional limitations, financial strains, and intergenerational support, among older Mexican Americans. The sample included 344 parent-child pairs reported by 83 respondents. Clustered regression analysis showed that widowhood elevated risks for depressive symptoms. We found that having some functional limitations, having more children and living in the same city with children exacerbated the adverse effects of widowhood on depressive symptoms. We also found that living in the same city with children increased the detrimental effects of widowhood on the depressive symptoms in men, whereas we did not find this pattern in women. The findings highlight the heterogeneity within the widowed Mexican American older adults. Implications for future research and practice are discussed.

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Alterations in the monoaminergic neurotransmission systems are suspected to be involved in the etiology of neuropsychiatric disorders, including depression. The role of these pathways in the risk of developing depressive symptoms during childhood or adolescence is still not completely clear. This study sought to identify putative genetic factors in genes of serotonergic and dopaminergic systems modulating the level of manifestation of depressive symptoms in children and adolescents. We analyzed 170 single nucleotide polymorphisms (SNPs) in 21 candidate dopaminergic and serotonergic genes in a non-clinical sample of 410 Costa Rican participants of ages between 7 and 18 years, assessing the severity of depressive symptoms through the Child Depression Inventory (CDI). Genotypic and haplotypic associations, as well as epistatic effects, were examined. A significant interaction effect was detected between rs1039089 in conjunction with rs877138 located upstream of the dopamine D1 receptor (DRD1) and the dopamine D2 receptor (DRD2) genes respectively, although no evidence was found for any single variant or haplotype related to a differential liability. This newly described genetic interaction among putative regulatory regions of dopamine receptors could affect the level of manifestation of depressive symptoms through an imbalance of D1-D2 heteromers and modulation of cognitive processes.

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