Search

Anxiety in older individuals is understudied despite its prevalence. Investigating its occurrence can be challenging, yet understanding the factors influencing its recurrence is important. Gaining insights into these factors through an explainable, probabilistic approach can enhance improved management. A Bayesian network (BN) is well-suited for this purpose. This study aimed to model the recurrence of anxiety symptomatology in an older population within a five-month timeframe. Data included baseline socio-demographic and general health information for older adults aged 60 years or older with at least mild depressive symptoms. A BN model explored the relationship between baseline data and recurrent anxiety symptomatology. Model evaluation employed the Area Under the Receiver Operating Characteristic Curve (AUC). The BN model was also compared to four machine learning models. The model achieved an AUC of 0.821 on the test data, using a threshold of 0.367. The model demonstrated generalisation abilities while being less complex and more explainable than other machine learning models. Key factors associated with recurrence of anxiety symptomatology were: "Not being able to stop or control worrying"; "Becoming easily annoyed or irritable"; "Trouble relaxing"; and "depressive symptomatology severity". These findings indicate a prioritised sequence of predictors to identify individuals most likely to experience recurrent anxiety symptomatology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Depressive and subthreshold depressive symptomatology are common but often neglected in older adults. Objective: This study aimed to assess rates of depressive and subthreshold depressive symptomatology, and the characteristics associated, among older adults living in a socioeconomically deprived area of Brazil. Methods: This study is part of the PROACTIVE cluster randomised controlled trial. 3356 adults aged 60+ years and registered in 20 primary health clinics were screened for depressive symptomatology with the Patient Health Questionnaire‐9 (PHQ‐9). Depressive status was classified according to the total PHQ‐9 score and the presence of core depressive symptoms (depressed mood and anhedonia) as follows: no depressive symptomatology (PHQ‐9 score 0–4, or 5–9 but with no core depressive symptom); subthreshold depressive symptomatology (PHQ‐9 score 5–9 and at least one core depressive symptom); and depressive symptomatology (PHQ‐9 score ≥ 10). Sociodemographic information and self‐reported chronic conditions were collected. Relative risk ratios and 95% CIs were obtained using a multinomial regression model. Results: Depressive and subthreshold depressive symptomatology were present in 30% and 14% of the screened sample. Depressive symptomatology was associated with female gender, low socioeconomic conditions and presence of chronic conditions, whereas subthreshold depressive symptomatology was only associated with female gender and having hypertension. Conclusions: Depressive and subthreshold depressive symptomatology is highly prevalent in this population registered with primary care clinics. Strategies managed by primary care non‐mental health specialists can be a first step for improving this alarming and neglected situation among older adults. Key points: This is the first study to assess subthreshold depressive symptomatology in a large sample of older adults in Brazil.Individuals registered in primary care clinics and living in socioeconomically deprived areas were assessed for depressive and subthreshold depressive symptomatology.Depressive and subthreshold depressive symptomatology rates were high among this population.Higher levels of depressive symptomatology were associated with female gender, worse socioeconomic conditions and the presence of other chronic health conditions. [ABSTRACT FROM AUTHOR]

Background: The elderly population has been growing in most low- and middle-income countries (LMIC), and depression is a common condition among these populations. The lack of integration between mental health and primary healthcare services and the shortage of mental health specialists in the public health system contribute to underdiagnosis and undertreatment of depression. One of the strategies to reduce this gap is task shifting and collaborative care treatments. This study therefore aims to evaluate the effectiveness and cost-effectiveness of a collaborative care psychosocial intervention to improve the clinical management of depression among elderly people in poor neighbourhoods in Guarulhos, Brazil. Methods: Two-arm, cluster randomised controlled trial with Basic Health Units as the clusters and a 1:1 allocation ratio. Twenty Basic Health Units have been randomly selected and randomised to control or intervention arms. We aim to recruit 1440 adults (72 per cluster) aged 60 years or over identified with depression (9-item Patient Health Questionnaire (PHQ-9) score ≥ 10). The control arm participants will receive an enhanced usual care, while the intervention arm participants will receive an enhanced usual care and a 17-week psychosocial intervention programme delivered at home by community health workers with the help of an application installed on tablet computers. The primary outcome is the proportion with depression recovery (PHQ-9 < 10) at 8 months' follow-up. We will also assess the maintenance of any earlier clinical gains and the cost-effectiveness of the intervention at 12 months. Discussion: This is the first randomised trial to investigate a collaborative care intervention to treat depression among poor elderly in LMIC/Latin America. This is a major public health problem worldwide, but in these countries, there are no locally tested, evidence-based interventions available to date. Trial registration: International Standard Randomised Controlled Trial Number ISRCTN57805470. Registered on 25 April 2019 [ABSTRACT FROM AUTHOR]

This study aimed to investigate the relationship between sociodemographic characteristics, depressive symptomatology, mobile phone ownership, and different uses of WhatsApp among older adults enrolled in primary care clinics in Guarulhos, São Paulo State, Brazil. This is a secondary data analysis, using data collected in the screening of participants to be included in the PROACTIVE cluster randomized trial. Individuals aged ≥ 60 years, registered in primary care clinics in Guarulhos, were assessed for sociodemographic characteristics, depressive symptoms according to the PHQ-9, mobile phone ownership, and use of WhatsApp. We performed multiple logistic regression models to investigate characteristics of the potential users of digital interventions. Of 3,356 older adults screened for depression, 45.7% said they use WhatsApp to receive/send messages. In the subsample that presented depressive symptomatology (n = 1,020), 41.9% stated using WhatsApp. Younger older adults and those with better socioeconomic status used more WhatsApp and were more likely to own a mobile phone. Participants with higher levels of symptoms of depression were less likely to use WhatsApp. Gender, age, schooling level, income, and depressive symptomatology are variables associated with the possession of a cell phone and with the use of WhatsApp by the older adults of the sample. These findings can help to implement digital health programs better suited to disadvantaged populations in Brazil and other low- and middle-income countries through mental telehealth interventions using WhatsApp and mobile health services to the older people.