Epidemiology of mental health disorders in the citizens of Tehran: a report from Tehran Cohort Study.

BACKGROUND: Mental health disorders (MHD) impose a considerable burden on public health systems. With an increasing worldwide trend in urbanization, urban mental health stressors are affecting a larger population. In this study, we evaluated the epidemiology of mental health disorders in the citizens of Tehran using the Tehran Cohort Study (TeCS) data. METHODS: We utilized data from the TeCS recruitment phase. A total of 10,247 permanent residents of Tehran metropolitan (aged 15 years and older) were enrolled in the study from March 2016 to 2019 via systematic random sampling from all 22 districts of Tehran. The participant's demographic, socioeconomic, and medical characteristics were evaluated by conducting comprehensive interviews. The standardized Persian version of the General Health Questionnaire version 28 was utilized to assess the mental status of the patients according to four central mental health disorders. RESULTS: Almost 37.1% of Tehran residents suffered mental health problems (45.0% of women and 28.0% of men). The greatest incidence of MHDs was seen in the 25-34 and over 75 age groups. The most common mental health disorders were depression (43%) and anxiety (40%), followed by somatization (30%) and social dysfunction (8.1%). Mental health disorders were more frequent in the southeast regions of the city. CONCLUSIONS: Tehran residents have a significantly higher rate of mental health disorders compared to nationwide studies, with an estimated 2.7 million citizens requiring mental health care services. Awareness of mental health disorders and identifying vulnerable groups are crucial in developing mental health care programs by public health authorities.

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals.

BACKGROUND: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer's disease (AD). The extraction of appropriate biomarkers to assess a subject's cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. METHODS: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study's feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. RESULTS: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. CONCLUSION: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers.

Evaluation of COVID-19 pandemic on components of social and mental health using machine learning, analysing United States data in 2020.

Background: COVID-19 was named a global pandemic by the World Health Organization in March 2020. Governments across the world issued various restrictions such as staying at home. These restrictions significantly influenced mental health worldwide. This study aims to document the prevalence of mental health problems and their relationship with the quality and quantity of social relationships affected by the pandemic during the United States national lockdown. Methods: Sample data was employed from the COVID-19 Impact Survey on April 20-26, 2020, May 4-10, 2020, and May 30-June 8, 2020 from United States Dataset. A total number of 8790, 8975, and 7506 adults participated in this study for April, May and June, respectively. Participants' mental health evaluations were compared clinically by looking at the quantity and quality of their social ties before and during the pandemic using machine learning techniques. To predict relationships between COVID-19 mental health and demographic and social factors, we employed random forest, support vector machine, Naive Bayes, and logistic regression. Results: The result for each contributing feature has been analyzed separately in detail. On the other hand, the influence of each feature was studied to evaluate the impact of COVID-19 on mental health. The overall result of our research indicates that people who had previously been diagnosed with any type of mental illness were most affected by the new constraints during the pandemic. These people were among the most vulnerable due to the imposed changes in lifestyle. Conclusion: This study estimates the occurrence of mental illness among adults with and without a history of mental disease during the COVID-19 preventative limitations. With the persistence of quarantine limitations, the prevalence of psychiatric issues grew. In the third survey, which was done under quarantine or house restrictions, mental health problems and acute stress reactions were substantially greater than in the prior two surveys. The findings of the study reveal that more focused messaging and support are needed for those with a history of mental illness throughout the implementation of restrictions.

The effect of training program of health promotion behaviors on geriatric general health components.

BACKGROUND: Increasing in elderly's population and their individual and social problems especially mental health problem in this group need special attention. The aim of this study was evaluation of health promotion behaviors training program efficacy on general health components in elderlies referring to health centers in Isfahan city. MATERIALS AND METHODS: This study was a tri phasic field trial in 2014 in which 72 elderlies allocated randomly in two case and control groups. Case group participated in 9 training sessions on stress management and interpersonal relationships and the control group participated in 2 sessions with a neutral discussion content. Data collection tools were demographic information questionnaire and general health questionnaire 28. Pretest, posttest, and 2-month follow-up were performed in two groups. Data were analyzed by t-test, analysis of variance with repeated measurement, least significant difference post hoc test, and SPSS 20 software. RESULTS: Findings of this study showed that the average of general health score in case and control groups had not significant difference before the intervention (P > 0.05). However, the mean difference of general health score after intervention and 2-month follow-up was statistically significant in two groups (P < 0.001). CONCLUSIONS: This study showed that interpersonal relationship and stress management training program are effective on promotion of mental health in elderlies. Hence, the findings of this study can be used in the field of treatment and care of the elderly by other health-care categories.

Citalopram and metacognitive therapy for depressive symptoms and cognitive emotion regulation in patients with major depressive disorder: A randomized controlled trial.

BACKGROUND: Metacognitive therapy (MCT) is a new psychotherapy for depression. This study was conducted to compare the effectiveness of citalopram and MCT on major depressive disorders (MDDs). MATERIALS AND METHODS: A total of 36 patients with MDD were randomly assigned into three groups of citalopram (n = 12), MCT (n = 16), and control (n = 8). MCT group received ten sessions of metacognition therapy. Citalopram group received 20-40 mg citalopram, and the control group did not receive any interventions. Outcomes were measured using the Beck Depression Inventory-II, Metacognition Questionnaire-30, and Cognitive-Emotion Regulation (CER) Questionnaire. Data were analyzed with ANCOVA using SPSS version 18. RESULTS: Depression score reduction was significant in both citalopram and metacognitive groups (P < 0.05). However, there was only a statistically significant difference between MCT and control group in CER and metacognition. CONCLUSION: MCT and citalopram both are effective in symptom reduction in MDD. Furthermore, MCT could lead to more improvement in metacognition, depression symptoms, and CER than citalopram, when treating MDDs.