Depression Severity
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2021 ◽  
Vol 2021 ◽  
pp. 1-8
Jia Li ◽  
Leijun Li ◽  
Yan Lv ◽  
Yanhai Kang ◽  
Mingjin Zhu ◽  

Objective. To investigate the effect of the interaction between depression and sleep disorders on the stroke occurrence based on the data from the National Health and Nutritional Examination Survey (NHANES). Methods. Seven cycles of 2-year NHANES data (2005-2018) were analyzed in this study. Univariate analysis was first performed between the stroke and nonstroke patients, and then, multivariate logistic regression models were conducted to analyze the association of depression, sleep disorders, and their interactions with stroke occurrence. Results. A total of 30473 eligible participants were included in this study, including 1138 (3.73%) with stroke and 29335 (96.27%) with nonstroke. Except sex, the differences were all significant between the stroke and nonstroke patients in baseline information (all P < 0.001 ). Depression (odds ratio (OR): 2.494, 95% confidence interval (CI): 2.098-2.964), depression severity (moderate, OR: 2.013, 95% CI: 1.612-2.514; moderately severe, OR: 2.598, 95% CI: 1.930-3.496; severe, OR: 5.588, 95% CI: 3.883-8.043), and sleep disorders (OR: 1.677, 95% CI: 1.472-1.910) were presented to be associated with an increased risk of stroke after correcting all the confounders. The logistic regression analysis showed that there was a synergic, additive interaction between depression and sleep disorders on the stroke occurrence, and the proportion of stroke patients caused by this interaction accounted for 27.1% of all the stroke patients. Conclusion. Depression, depression severity, and sleep disorders are all independently associated with a high risk of stroke. The interaction between depression and sleep disorders can synergistically increase the stroke occurrence.

Murali Krishna ◽  
Sumanth Mallikarjuna Majgi ◽  
Sudeep Pradeep Kumar ◽  
Rajagopal Rajendra ◽  
Narendra Heggere ◽  

Background: In high-income countries, dedicated self-harm surveillance systems are regarded as a key component in suicide prevention strategies, which suggests they may be important in low- and middle-income countries where rates of suicide are higher and risk factors for self-harm are different, provided they can be shown to be feasible in those settings.Methods: We established a hospital based self-harm register in Mysore, South India. A subset of participants was followed-up after two years. Results: Of the 453 who were examined at baseline, the vast majority (80%) were from rural areas, nearly a quarter were illiterate and 65 (14%) were diagnosed with depression. Compared to men, women tended to be younger, single, from rural areas, unemployed, with lower levels of educational attainment and higher levels of disability. Of the 453, 371 (80%) were successfully contacted by cellphone at 2 years. There were no significant differences in baseline variables between those followed-up and those who were not, including sociodemographic features, rates of depression, severity of disability and severity of suicidal intent. All participants reported that psychosocial assessment offered at baseline was helpful and that they would recommend assessment to othersConclusions: Findings from this study indicate that our self-harm register was a feasible and useful resource, and that contact and follow up are acceptable and feasible.

2021 ◽  
Gabriela Lunansky ◽  
Ria H. A. Hoekstra ◽  
Tessa Blanken

Background. Why does adversity lead to mental health complaints in some, but not others? Individual differences in the development of depressive complaints are related to the regulation of affect states. The COVID-19 pandemic has caused a prolonged period of perturbations to the daily lives of people across the globe, providing an unparalleled opportunity to investigate how fluctuations in positive and negative affect relate to the evolution of mood complaints.Methods. 228 participants from the Boston College daily sleep and well-being survey completed at least 20 assessments of positive and negative affect and depression complaints between March 20th 2020 and June 26th 2020. We explored affect trajectories throughout this period and estimated longitudinal multilevel network models. Furthermore, we investigated how individual network structures relate to changes in depression severity over time.Results. On average, positive affect was reported somewhat higher than negative affect. However, when separating affect trajectories based on the individuals’ depressive complaints, we identified that individuals consistently experiencing depressive complaints report higher levels of negative affect compared with positive affect. Contrary, individuals consistently reporting no depressive complaints show opposite results. Furthermore, we found many and strong associations in the multilevel network between the distinct affect states and depressive complaints. Lastly, we established that the higher the connectivity of an individual’s network, the larger their change in depressive complaints is.Conclusions. We conclude that affect fluctuations are directly related to the development of depressive complaints, both within- and across individuals, and both within a single measurement moment and over time.

2021 ◽  
Vol 7 (1) ◽  
pp. 23
Jorge Gabín ◽  
Anxo Pérez ◽  
Javier Parapar

Depression is one of the most prevalent mental health diseases. Although there are effective treatments, the main problem relies on providing early and effective risk detection. Medical experts use self-reporting questionnaires to elaborate their diagnosis, but these questionnaires have some limitations. Social stigmas and the lack of awareness often negatively affect the success of these self-report questionnaires. This article aims to describe techniques to automatically estimate the depression severity from users on social media. We explored the use of pre-trained language models over the subject’s writings. We addressed the task “Measuring the Severity of the Signs of Depression” of eRisk 2020, an initiative in the CLEF Conference. In this task, participants have to fill the Beck Depression Questionnaire (BDI-II). Our proposal explores the application of pre-trained Multiple-Choice Question Answering (MCQA) models to predict user’s answers to the BDI-II questionnaire using their posts on social media. These MCQA models are built over the BERT (Bidirectional Encoder Representations from Transformers) architecture. Our results showed that multiple-choice question answering models could be a suitable alternative for estimating the depression degree, even when small amounts of training data are available (20 users).

Alexandra Schosser ◽  
Birgit Senft ◽  
Marion Rauner

AbstractWe investigated the benefit of a 6-week ambulant psychiatric rehabilitation program in an ambulant psychiatric rehabilitation clinic in Vienna, Austria, from January 2014 to December 2016 by an uncontrolled repeated measures study. The potential of this intervention program was assessed by effectiveness and cost measures using suitable statistical analyses. We compared the effectiveness and cost measures of this ambulant psychiatric rehabilitation program on patients for the period of up to 12 months after discharge to the period of 12 months before admission to the intervention program based on self-reported catamnesis questionnaires. For the program’s effectiveness measures, we accounted for both psychological indices for measuring depression severity, symptom burden, and functioning to document the health improvement of patients and economy-related indices such as the number of sick leave days for patients. For the program’s cost measures, both direct tangible treatment and medication costs and indirect tangible costs based on the productivity loss measured in non-working days of the patients were considered. The results significantly demonstrated that all psychological effectiveness measures for the patients highly improved by the 6-weeks rehabilitation program and remained rather stable 12 months after discharge. We found that costs for the 6-week ambulant psychiatric rehabilitation program could be easily covered within 12 months after discharge once a total societal cost perspective was considered. Even additional total cost savings of up to over 5000 Euro could be achieved which were highest for employed patients, followed by unemployed patients receiving rehabilitation allowance due to both their high direct medication and treatment costs as well as high indirect costs for productivity loss. The most important finding was that this treatment program was especially beneficial for rehabilitation patients in earlier stages of psychiatric diseases who were still employed, indicating the need for early intervention in mental disorder.

2021 ◽  
Mariko Makhmutova ◽  
Raghu Kainkaryam ◽  
Marta Ferreira ◽  
Jae Min ◽  
Martin Jaggi ◽  

BACKGROUND In 2017, an estimated 17.3 million adults in the US experienced at least one major depressive episode, with 35% of them not receiving any treatment. Under-diagnosis of depression has been attributed to many reasons including stigma surrounding mental health, limited access to medical care or barriers due to cost. OBJECTIVE To determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes. METHODS Here we present the development of PSYCHE-D (Prediction of SeveritY CHange - Depression), a predictive model developed using PGHD from more than 4000 individuals, that forecasts long-term increase in depression severity. PSYCHE-D uses a two-phase approach: the first phase supplements self-reports with intermediate generated labels; the second phase predicts changing status over a 3 month period, up to 2 months in advance. The two phases are implemented as a single pipeline in order to eliminate data leakage, and ensure results are generalizable. RESULTS PSYCHE-D is composed of two Light Gradient Boosting Machine (LightGBM) algorithm-based classifiers that use a range of PGHD input features, including objective activity and sleep, self reported changes in lifestyle and medication, as well as generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect increase in depression severity over a 3-month interval with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity, while maintaining specificity, versus a random model. CONCLUSIONS These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual's mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals suffering from depression. CLINICALTRIAL Data used to develop the model was derived from the Digital Signals in Chronic Pain (DiSCover) Project ( identifier: NCT03421223)

2021 ◽  
Vol 70 (40) ◽  
pp. 1427-1432
Haomiao Jia ◽  
Rebecca J. Guerin ◽  
John P. Barile ◽  
Andrea H. Okun ◽  
Lela McKnight-Eily ◽  

2021 ◽  
Vol 118 (40) ◽  
pp. e2105730118
Dipanjan Ray ◽  
Dmitry Bezmaternykh ◽  
Mikhail Mel’nikov ◽  
Karl J. Friston ◽  
Moumita Das

Functional neuroimaging research on depression has traditionally targeted neural networks associated with the psychological aspects of depression. In this study, instead, we focus on alterations of sensorimotor function in depression. We used resting-state functional MRI data and dynamic causal modeling (DCM) to assess the hypothesis that depression is associated with aberrant effective connectivity within and between key regions in the sensorimotor hierarchy. Using hierarchical modeling of between-subject effects in DCM with parametric empirical Bayes we first established the architecture of effective connectivity in sensorimotor cortices. We found that in (interoceptive and exteroceptive) sensory cortices across participants, the backward connections are predominantly inhibitory, whereas the forward connections are mainly excitatory in nature. In motor cortices these parities were reversed. With increasing depression severity, these patterns are depreciated in exteroceptive and motor cortices and augmented in the interoceptive cortex, an observation that speaks to depressive symptomatology. We established the robustness of these results in a leave-one-out cross-validation analysis and by reproducing the main results in a follow-up dataset. Interestingly, with (nonpharmacological) treatment, depression-associated changes in backward and forward effective connectivity partially reverted to group mean levels. Overall, altered effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of depression severity and treatment response.

Jan Sandora ◽  
Lukas Novak ◽  
Robert Brnka ◽  
Jitse P. van Dijk ◽  
Peter Tavel ◽  

Short and effective tools for measuring depression, anxiety and their resulting impairments are lacking in the Czech language. The abbreviated versions of the Overall Anxiety Severity and Impairment Scale (OASIS) and the Overall Depression Severity and Impairment Scale (ODSIS) show very good psychometric properties in English and other languages, and can be used in different settings for research or clinical purposes. The aim of this study was the psychometric evaluation and validation of the Czech versions of the abbreviated forms of both tools in the general population. A nationally representative sample of 2912 participants (age = 48.88, SD = 15.56; 55% female) was used. The non-parametric testing of the differences between sociodemographic groups revealed a higher level of anxiety and depression in students, females and religious respondents. Confirmatory Factor Analysis suggested a good fit for the unidimensional model of the OASIS: x²(4) = 38.28; p < 0.001; TLI = 0.999; CFI = 0.997; RMSEA = 0.078; SRMR = 0.027 and the ODSIS: x²(4) = 36.54; p < 0.001; TLI = 0.999; CFI = 0.999; RMSEA = 0.076; SRMR = 0.021 with the data. Both scales had an excellent internal consistency (OASIS: Cronbach’s alpha = 0.95, McDonald’s omega = 0.95 and ODSIS: Cronbach’s alpha = 0.95, McDonald’s omega = 0.95). A clinical cut-off of 15 was identified for the OASIS and a cut-off of 12 for the ODSIS. The study showed good validity for both scales. The Czech versions of the abbreviated OASIS and ODSIS were short and valid instruments for measuring anxiety and depression.

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