Recurrence of major depressive disorder and its predictors in the general population: results from The Netherlands Mental Health Survey and Incidence Study (NEMESIS)

2012 ◽  
Vol 43 (1) ◽  
pp. 39-48 ◽  
Author(s):  
F. Hardeveld ◽  
J. Spijker ◽  
R. De Graaf ◽  
W. A. Nolen ◽  
A. T. F. Beekman
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
General Population ◽  
Depressive Disorder ◽  
Depressive Episode ◽  
Major Depressive ◽  
Time To Recurrence ◽  
Younger Age ◽  
Mental Health Survey

BackgroundKnowledge of the risk of recurrence after recovery from major depressive disorder (MDD) in the general population is scarce.MethodData were derived from 687 subjects in the general population with a lifetime DSM-III-R diagnosis of MDD but without a current major depressive episode (MDE) or dysthymia. Participants had to be at least 6 months in remission, and were recruited from The Netherlands Mental Health Survey and Incidence Study (NEMESIS), using the Composite International Diagnostic Interview (CIDI). Recency and severity of the last MDE were assessed retrospectively at baseline. Recurrence of MDD was measured prospectively during the 3-year follow-up. Kaplan–Meier survival curves were used to measure time to recurrence. Determinants of time to recurrence were analyzed using proportional hazard models.ResultsThe estimated cumulative recurrence of MDD was 13.2% at 5 years, 23.2% at 10 years and 42.0% at 20 years. In bivariate analysis, the following variables predicted a shorter time to recurrence: younger age, younger age of onset, higher number of previous episodes, a severe last depressive episode, negative youth experiences, ongoing difficulties before recurrence and high neuroticism. Multivariably, younger age, a higher number of previous episodes, a severe last depressive episode, negative youth experiences and ongoing difficulties remained significant.ConclusionsIn this community sample, the long-term risk of recurrence was high, but lower than that found in clinical samples. Subjects who had had an MDE had a long-term vulnerability for recurrence. Factors predicting recurrence included illness- and stress-related factors.

scholarly journals Prevalence of Mental Disorders in Nepal: Findings from the Pilot Study

2019 ◽  
Vol 17 (2) ◽  
pp. 141-147 ◽  
Author(s):  
Anjani Kumar Jha ◽  
Saroj Prasad Ojha ◽  
Sushma Dahal ◽  
Pawan Sharma ◽  
Sagun Ballav Pant ◽  
...  
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Pilot Study ◽  
Mental Disorders ◽  
Depressive Disorder ◽  
Health Survey ◽  
Conversion Disorder ◽  
Major Depressive ◽  
Mental Health Survey ◽  
National Mental Health

Background: The global prevalence of mental disorders is high and has an increasing trend. In Nepal, there is dearth in literature on prevalence of mental disorders based on national representative sample. In this study, we aim to present the findings on the prevalence of mental disorders from the pilot study of National Mental Health Survey, Nepal. Methods: A cross-sectional study was conducted among 1647 participants aged 13 years and above in three districts of Nepal: Dhanusha, Bhaktapur and Dolakha each representing three ecological regions. Mini International Neuropsychiatric Interview (MINI) standard version 7.0.2 for DSM-5 was used for adults (aged 18 years and above), and kid version of the same tool was used for children (aged 13-17 years) in Nepali language. Separate sets of questions were added for epilepsy and dissociative conversion disorder that were not in the Mini International Neuropsychiatric Interview tool. Prevalence of assessed mental disorders was reported separately for adults and children.Results: The current prevalence of mental disorders among adults and children were 13.2% and 11.2% respectively. Substance use disorder, dissociative conversion disorder, major depressive disorder, alcohol use disorder and psychotic disorder were common among adults. Similarly, psychotic disorder, agoraphobia, major depressive disorder, and anxiety disorders were common among children. Current suicidality was present among 10.9% adults and 8.7% children.Conclusions: Our findings from the pilot study have given insight into the prevalence of different mental disorders in the survey areas. These findings can be utilized for planning the National Mental Health Survey, Nepal. Keywords: Mental disorders; mental health survey; MINI; Nepal; pilot study.

scholarly journals Knowledge of and attitudes to major depressive disorder and its treatment in a sample of the general population in Serbia

2020 ◽  
Vol 148 (1-2) ◽  
pp. 94-99
Author(s):  
Ivona Milacic-Vidojevic ◽  
Marija Colic ◽  
Branka Draskovic
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Major Depressive Disorder ◽  
Health Literacy ◽  
General Population ◽  
Depressive Disorder ◽  
Mental Health Literacy ◽  
Structured Interview ◽  
Major Depressive ◽  
People With Mental Illness

Introduction/Objective. The objective of this paper was to examine the mental health literacy of the general population in Serbia and their attitudes towards persons with a mental illness. Methods. This was a cross-sectional study with structured interview using the vignette of a person with major depressive disorder (MDD). The attitudes towards people with mental illness were assessed by the Department of Health Attitudes to Mental Illness Questionnaire. A convenient sample consisted of 504 participants. Results. A total of 72% of the sample recognized the presence of some sort of mental health problem, of which 40.9% correctly labeled the symptoms as MDD. The majority of participants believed that MDD was caused more by stress than by biological factors. A psychologist, a close friend, and a psychiatrist were often rated as helpful for the person described by the vignette. Vitamins and healing herbs were rated as the most helpful remedy. Antidepressants were considered both helpful and harmful. The attitudes towards people with mental illness were moderately positive. Conclusion. Mental health literacy in Serbia is moderate. Risk factors for negative attitudes included older age and lower education.

Bipolar features in major depressive disorder: Results from the Iranian mental health survey (IranMHS)

2018 ◽  
Vol 241 ◽  
pp. 319-324 ◽  
Author(s):  
Masoumeh Amin-Esmaeili ◽  
Abbas Motevalian ◽  
Afarin Rahimi-Movaghar ◽  
Ahmad Hajebi ◽  
Vandad Sharifi ◽  
...  
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Health Survey ◽  
Major Depressive ◽  
Mental Health Survey

scholarly journals Long term life dissatisfaction and subsequent major depressive disorder and poor mental health

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Teemu Rissanen ◽  
Heimo Viinamäki ◽  
Kirsi Honkalampi ◽  
Soili M Lehto ◽  
Jukka Hintikka ◽  
...  
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Poor Mental Health ◽  
Major Depressive ◽  
Life Dissatisfaction

STUDY ON NEWER PSYCHIATRIC CASES VISITING OPD AFTER ONSET OF PANDEMIC COVID-19

2021 ◽  
pp. 11-12
Author(s):  
Krishna Mishra ◽  
Priyash Jain
Keyword(s):  
Mental Health ◽  
Major Depressive Disorder ◽  
Health Status ◽  
General Population ◽  
Depressive Disorder ◽  
Mental Hospital ◽  
Major Depressive ◽  
Classi Cation ◽  
The Impact

Introduction : It is predicted by the experts that there will be a steep rise in the number of psychiatric cases post covid-19 pandemic. It is important to gauge the situation of mental health in population to be able to provide mental health services in a better way. Objective: The aim of this study is to determine the impact of the pandemic and other factors contingent to the pandemic on the mental health status of general population. Materials and method: The study was done over a period of 1 month after the lockdown. Study was done on 160 patients visiting Psychiatry OPD th in Mental Hospital, Indore for the rst time. Patients were diagnosed using International classication of diseases- 10 edition. Results: In our study we found that a majority of 45 of the 160 patients were suffering from Major Depressive Disorder followed by 24 patients of 160 suffering from nonorganic unspecied psychosis. Conclusion: It is important to assess the number of psychiatry patients visiting OPD to be able to cater to the needs of general population. It is also important to assess any role of psychosocial factors operating during pandemic being responsible for any rise in number of psychiatric cases.

scholarly journals A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jakub Tomasik ◽  
Sung Yeon Sarah Han ◽  
Giles Barton-Owen ◽  
Dan-Mircea Mirea ◽  
Nayra A. Martin-Key ◽  
...  
Keyword(s):  
Mental Health ◽  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Diagnostic Algorithm ◽  
Health Questionnaire ◽  
Major Depressive ◽  
Blood Biomarker ◽  
Mental Health Questionnaire ◽  
Biomarker Data

AbstractThe vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18–45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86–0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86–0.91) and 0.90 (0.87–0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57–0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

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