This table contains 93984 series, with data for years 2002 - 2002 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Newfoundland and Labrador; Nova Scotia ...), Age group (4 items: 65 years and over;25 to 64 years;15 to 24 years; Total; 15 years and over ...), Sex (3 items: Both sexes; Females; Males ...), Mental health and well-being profile (89 items: Total population for the variable major depressive episode; Major depressive episode; all measured criteria are met; Major depressive episode; measured criteria not met; Major depressive episode; not stated ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons; High 95% confidence interval; number of persons ...).

BackgroundMental disorders are known to predict poverty, morbidity and mortality. In resource limited settings, low levels of mental health literacy (MHL) and high mental illness stigma (MIS) have been sighted as possible factors that may impede access to mental health care. However, little has been done to examine the association between mental disorders and these factors (MHL and MIS) in sub-Saharan Africa.MethodsWe assessed for the prevalence of major depressive disorders (MDD), substance use disorders (SUD), post-traumatic stress disorder (PTSD), generalized anxiety disorder (GAD), documented MHL and MIS among 814 participants from 24 villages in central Uganda. We conducted regression analyses to examine the association between the prevalence of mental disorders, demographic factors as well as MIS and MHL.ResultsOver two thirds of the participants 581 (70%) were female. The mean age of the participants was 38 years (SD± 13.5). The prevalence of mental disorders ranged from 6.8–32%. Participants who were older were less likely to screen positive for GAD (OR 0.98; 0.96–0.99), female gender was protective against SUD (OR 0.46; 0.3–0.68) and those with MDD had lower education level (OR 0.23; 0.1–0.53). The mean MIS score was 11.3 (SD± 5.4) with a range of 6–30 and the mean MHL score was 21.7 (SD ±3.0) with a range of 10–30. MIS was negatively associated with GAD [β = -1.211 (-2.382 to -0.040)]. There no statistically significant association between MHL and a mental disorder.ConclusionThere was a high prevalence of mental disorders in the community that we studied. Adequate resources should be allocated to address this burden.

This table shows the number of persons treated in specialized mental health care (GGZ), by mental disorder for which care was provided in the year under review. It only concerns care financed in the form of DBCs (Diagnosis Treatment Combinations). This concerns ambulatory care and care with a stay of up to one year, for persons aged 18 or older. The table shows persons for whom the diagnosis to be selected was the main (primary) diagnosis, as well as persons for whom this diagnosis was registered as a primary or secondary diagnosis. For example: the table counts both the number of people for whom a depressive disorder was the main diagnosis to be treated, and also people for whom a depressive disorder was an additional (secondary) diagnosis that was registered because it influenced the treatment. Data is presented by age, gender and income groups. It can also be seen whether a stay has taken place (one or more overnight stays in a mental health institution). The last few years are based on a less complete database and are therefore less reliable. Data available from: 2015 Status of the figures: The figures from 2015 to 2019 are final. 2020 and 2021 are provisional. Changes as of February 27, 2023: - 2019 has been made final. - Provisional figures for 2020 and 2021 have been added. When will new numbers come out? The DBC system in mental health care has been abolished as of 1 January 2022. The figures for 2020 and 2021 will be finalized at the end of 2023. After this, the table will be stopped.

BackgroundThe Occupational Depression Inventory (ODI) reflects a new approach to job-related distress centered on work-attributed depressive symptoms. The instrument was developed with reference to the characterization of major depression found in the Diagnostic and statistical manual of mental disorders, fifth edition. The ODI has been validated in English, French, and Spanish. This study (a) investigated the psychometric and structural properties of the ODI's Italian version and (b) inquired into the nomological network of occupational depression.MethodsA convenience sample of 963 employed individuals was recruited in Italy (69.9% female; mean age = 40.433). We notably relied on exploratory structural equation modeling bifactor analysis, common-practice confirmatory factor analysis, and Mokken scale analysis to examine our dataset.ResultsOur analyses indicated that the Italian version of the ODI meets the requirements for essential unidimensionality, thus justifying the use of the instrument's total score. The ODI's reliability was excellent. Measurement invariance held across sexes, age groups, and occupations. Occupational depression was negatively associated with general wellbeing and positively associated with a 12-month history of depressive disorder, current antidepressant intake, 12-month sick leave, 6-month physical assault at work, 6-month verbal abuse at work, lack of money for leisure activities, and financial strain in the household.ConclusionsThe ODI's Italian version exhibits robust psychometric and structural properties, suggesting that the instrument can be fruitfully used for addressing job-related distress in Italian-speaking populations. Furthermore, the present study relates occupational depression to important health, economic, and work-life characteristics, including past depressive episodes, antidepressant medication, sickness-related absenteeism, workplace violence, and economic stress.

Background: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. Methods: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. Results: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. Conclusion: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.