scholarly journals Geoinformation systems in population analysis of the distribution of depressive disorders in Khabarovsk

2020 ◽  
Vol 26 (3) ◽  
pp. 39-52
Author(s):  
Igor Loginov ◽  
Sergey Savin
Keyword(s):  
Risk Factors ◽  
Depressive Disorders ◽  
System Analysis ◽  
Population Analysis ◽  
Large City ◽  
Information Modeling ◽  
Medical Psychology ◽  
Geoinformation Systems ◽  
Geoinformation Technologies ◽  
Depressive Episodes

The research is devoted to the use of modern geoinformation technologies for the analysis of spatial medical and demographic data. On the example of the medical and ecological geoinformation system (MEGIS) of Khabarovsk the possibilities of geoinformation technologies in the study of the spread of depressive disorders in a large city and the analysis of cause-and-effect relationships between this disease and some socio-economic factors are shown. The features of mathematical support of MEGIS necessary for population epidemiological analysis are considered. The possibilities of using correlation-regression and cluster-discriminant analysis for these purposes are shown. At the stage of the initial manifestation of symptoms of depression, statistically significant binary risk factors for depressive disorders were established, which were used in the diagnostic questionnaire. Developed complex recognition, classification and predictive models on the individual level, to assess the risk of developing depressive disorders and to predict the potential severity of the disease. At the population level, risk groups for depressive disorders in patients who have suffered depressive episodes in childhood should be formed. Based on the results of multi-level GIS and classification and predictive modeling based on individual clinical dynamically, socio-psychological, transcultural and environmental health risk factors formulated practical recommendations to improve the prevention of depressive disorders. The study of clinical and pathodynamic, socio-psychological, transcultural and medico-ecological aspects of non-psychotic depressive disorders with the use of geoinformation systems was carried out for the prevention of depression and mental health stabilization of the region population. The results of this research can be used by specialists in geoinformation systems, medical demography, medical psychology and social psychiatry, sociology, psychiatry, psychology and family medicine, conflictology, information modeling and system analysis in health care.

scholarly journals Telemedicine technologies for organizing the work of mental health services to prevent disorders of the depressive spectrum during the COVID-19 pandemic

2020 ◽  
Vol 6 (4) ◽  
pp. 8-14
Author(s):  
S.Z. Savin ◽  
◽  
N.E. Kosykh ◽  
◽  
Keyword(s):  
Mental Health ◽  
Information Technologies ◽  
System Analysis ◽  
Critical Factor ◽  
Information Modeling ◽  
Panic Attacks ◽  
Medical Psychology ◽  
Young Generation ◽  
The North ◽  
Increased Risk

Introduction. The problems of telepsychiatry are analyzed from the point of view of the method of information modeling of complex conflict systems. Materials and methods. Methodological approaches to the use of information technologies for monitoring mental health and prevention of depressive spectrum disorders during the COVID-19 pandemic, including in the young generation of representatives of the indigenous peoples of the North and the Amur region, are considered. Results. The proposed telepsychiatric method of providing medical and psychiatric services is most effective in preventing depression in remote areas of the Khabarovsk territory where distance is a critical factor, during the COVID-19 pandemic. Discussion. Remote recording of cases of depression due to depression in patients with coronavirus, adequate teleconsultation and prevention of panic attacks and phobias will reduce the severity of the chronic course of depression and the risk of suicide. Conclusions. An actual solution to psychiatric problems associated with the COVID-19 pandemic is proposed for remote regions where, in addition to the existing problems of social and medical infrastructure, there is also a significantly increased risk of concomitant socially significant mental illness. It will be useful for specialists in early diagnosis and prevention of mental disorders, tele-medicine, mathematical modeling, system analysis in medical psychology, as well as for psychiatrists and psychotherapists.

scholarly journals Depressive episodes–evidence for a causal role of primary anxiety disorders?

2003 ◽  
Vol 18 (8) ◽  
pp. 384-393 ◽  
Author(s):  
Hans-Ulrich Wittchen ◽  
Katja Beesdo ◽  
Antje Bittner ◽  
Renee D. Goodwin
Keyword(s):  
Risk Factors ◽  
Anxiety Disorders ◽  
Depressive Disorders ◽  
Common Mental Disorders ◽  
Future Research ◽  
Severe Impairment ◽  
First Onset ◽  
Depressive Episodes ◽  
Course Of Depression

AbstractAnxiety and depressive disorders are common mental disorders in general population, imposing tremendous burden on both affected persons and society. Moreover, comorbidity between anxiety and depressive conditions is high, leading to substantial disability and functional impairment. Findings consistently suggest that anxiety disorders are primary to depression in the majority of comorbid cases. Yet, the question of whether anxiety disorders are risk factors for depression, and potentially even causal risk factors for the first onset of depression, remains unresolved. Recent results have shown that anxiety disorders increase the risk for subsequent depression, and also affect the course of depression, resulting in a poorer prognosis. Further, some results suggest a dose–response-relationship in revealing that a higher number of anxiety disorders and more severe impairment associated with anxiety disorders additionally increase the risk for subsequent depression. The goal of this paper is to review recent literature, summarize implications of previous findings, and suggest directions for future research regarding preventive and intervention strategies.

scholarly journals From depressive symptoms to depressive disorders: the relevance of thresholds

2010 ◽  
Vol 196 (5) ◽  
pp. 365-371 ◽  
Author(s):  
José L. Ayuso-Mateos ◽  
Roberto Nuevo ◽  
Emese Verdes ◽  
Nirmala Naidoo ◽  
Somnath Chatterji
Keyword(s):  
Risk Factors ◽  
Health Status ◽  
Depressive Episode ◽  
Depressive Disorders ◽  
World Health ◽  
World Health Survey ◽  
The World ◽  
The Unemployed ◽  
Depressive Episodes ◽  
The Impact

BackgroundNosological boundaries for depressive disorders as well as the prevalence and impact of ‘subsyndromal’ depression remain unclear.AimsTo examine the impact of subsyndromal depressive disorders on health status and to assess if depressive disorders lie on a continuum of severity.MethodThe sample was composed of randomly selected respondents from the general population in 68 countries from across the world participating in the World Health Organization's World Health Survey.ResultsThe pattern of risk factors for depressive disorders was consistent across all types of depression (subsyndromal, brief depressive episode and depressive episode): odds ratios for females ranged between 1.49 and 1.80, and for the unemployed from 1.19 to 1.25. All types of depression produced a significant decrement in health status compared with no depression after controlling for demographic variables, income and country.ConclusionsSubthreshold depressive disorders occur commonly all across the world and are associated with the same risk factors everywhere. They produce significant decrements in health and do not qualitatively differ from full-blown episodes of depression as currently defined, and lie on a continuum with more severe forms of depressive episodes but are distinct from normal mood changes.

scholarly journals The Rapid Mood Screener: A Novel and Pragmatic Screener Tool for Bipolar I Disorder

CNS Spectrums ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 167-168
Author(s):  
C. Brendan Montano ◽  
Mehul Patel ◽  
Rakesh Jain ◽  
Prakash S. Masand ◽  
Amanda Harrington ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Health Outcomes ◽  
Screening Tool ◽  
Clinical Interview ◽  
Clinical Practices ◽  
Predictive Values ◽  
Bipolar I Disorder ◽  
Manic Symptoms ◽  
Depressive Episodes

AbstractIntroductionApproximately 70% of patients with bipolar disorder (BPD) are initially misdiagnosed, resulting in significantly delayed diagnosis of 7–10 years on average. Misdiagnosis and diagnostic delay adversely affect health outcomes and lead to the use of inappropriate treatments. As depressive episodes and symptoms are the predominant symptom presentation in BPD, misdiagnosis as major depressive disorder (MDD) is common. Self-rated screening instruments for BPD exist but their length and reliance on past manic symptoms are barriers to implementation, especially in primary care settings where many of these patients initially present. We developed a brief, pragmatic bipolar I disorder (BPD-I) screening tool that not only screens for manic symptoms but also includes risk factors for BPD-I (eg, age of depression onset) to help clinicians reduce the misdiagnosis of BPD-I as MDD.MethodsExisting questionnaires and risk factors were identified through a targeted literature search; a multidisciplinary panel of experts participated in 2 modified Delphi panels to select concepts thought to differentiate BPD-I from MDD. Individuals with self-reported BPD-I or MDD participated in cognitive debriefing interviews (N=12) to test and refine item wording. A multisite, cross-sectional, observational study was conducted to evaluate the screening tool’s predictive validity. Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a draft 10-item screening tool and additional questionnaires/questions. Different combinations of item sets with various item permutations (eg, number of depressive episodes, age of onset) were simultaneously tested. The final combination of items and thresholds was selected based on multiple considerations including clinical validity, optimization of sensitivity and specificity, and pragmatism.ResultsA total of 160 clinical interviews were conducted; 139 patients had clinical interview-confirmed BPD-I (n=67) or MDD (n=72). The screening tool was reduced from 10 to 6 items based on item-level analysis. When 4 items or more were endorsed (yes) in this analysis sample, the sensitivity of this tool for identifying patients with BPD-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.ConclusionThis new 6-item BPD-I screening tool serves to differentiate BPD-I from MDD in patients with depressive symptoms. Use of this tool can provide real-world guidance to primary care practitioners on whether more comprehensive assessment for BPD-I is warranted. Use of a brief and valid tool provides an opportunity to reduce misdiagnosis, improve treatment selection, and enhance health outcomes in busy clinical practices.FundingAbbVie Inc.

Depression and tobacco smoking (analytical review). Part 2

2021 ◽  
pp. 44-56
Author(s):  
Sergey Zinovievich Savin ◽  
Evgeniya Valerievna Solodkaya
Keyword(s):  
Risk Factors ◽  
Suicidal Behavior ◽  
Tobacco Smoking ◽  
Depressive Disorders ◽  
Effective Means ◽  
Scientific Publications ◽  
Analytical Review ◽  
The Central Nervous System ◽  
Spectrum Disorders ◽  
Causes And Risk Factors

In the pathogenesis of depressive disorders and the consequent suicidal behavior, an important role belongs to the neurochemical processes and structures of the central nervous system. An analytical study of Russian and foreign literature was carried out to obtain information about the relationship between neurochemical factors in the development of depressive disorders and to elucidate the causes and risk factors of suicidal behavior due to depression and an unhealthy habit common among young people, i.e. tobacco smoking. A systematic search of scientific publications on the neurobiological aspects of the research into the causes and risk factors of depressive spectrum disorders was carried out. The analysis of the results of relevant neurobiological studies in the field of etiology and formation of depressive disorders with suicidal behavior contributes to the development of effective means of prevention and treatment of depressive spectrum disorders.

scholarly journals Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

2022 ◽  
Vol 12 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Fatty Acid ◽  
Depressive Disorders ◽  
Subjective Health ◽  
Bayesian Regression ◽  
Living Alone ◽  
Study Objective ◽  
Naive Bayesian ◽  
Naïve Bayesian

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.

scholarly journals Personalized Models of Internalizing Symptoms for Women with Social Anxiety Disorder and Depression

2020 ◽  
Author(s):  
Marilyn Piccirillo ◽  
Thomas Rodebaugh
Keyword(s):  
Risk Factors ◽  
Anxiety Disorder ◽  
Social Anxiety ◽  
Social Anxiety Disorder ◽  
Internalizing Symptoms ◽  
Emotional Experience ◽  
Group Level ◽  
Major Depressive ◽  
Individual Level ◽  
Depressive Episodes

Social anxiety disorder (SAD) constitutes an important risk factor for major depressive disorder (MDD) and women are at greater risk for both disorders and their comorbidity. Despite much research examining risk factors for MDD specifically, there is limited research evaluating how individuals with SAD transition into depressive episodes. Clinical and theoretical evidence suggests that each individual may exhibit a unique personalized pattern of risk factors. These idiographic patterns may contradict group-level findings. In this study, women (N = 35) with SAD and a current or past major depressive episode completed ecological sampling of their mood and emotional experience five times a day for a month via a smartphone application. These data were analyzed using idiographic analyses to construct individual-level models of each woman’s mood. A multilevel model was constructed to determine risk factors for group-level intra-daily sadness (i.e., depressed mood). Some group-level relationships were consistent with previous research; however, most women’s models demonstrated few, and differing, risk factors for intra-daily sadness. We also examined the spread of individual-level estimates taken from group and idiographic models to determine the extent to which multilevel models can estimate individual-level effects. Implications for integrating results from idiographic methodology into existing theoretical models of psychopathology and clinical practice are discussed.

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