Depression is one of the most common health conditions globally. It's estimated that between two to six percent of people in the world have experienced depression in the past year.1

But what are the chances that people have depression at any time in their lives?

This question is difficult to answer because depression is not a constant condition – people tend to transition in and out of depressive episodes. The chances of ever having an episode of depression are therefore much higher than the figure of two to six percent.

Researchers estimate that around one in three women and one in five men in the United States have an episode of major depression by the time they are 65.2 Studies in other high-income countries suggest even higher figures. In the Netherlands and Australia, it's estimated that this affects 40% of women and 30% of men.3

In this post, I will explain why measuring the lifetime risk of depression can be challenging, and how researchers are able to address the challenges and estimate the risk of major depression over a person’s lifetime. One way to estimate the lifetime risk of depression is to ask elderly people whether they have ever had depression in their lives. This sounds straightforward, but it leads to several problems.

One is that it relies on self-reporting. Major depression is diagnosed based on the symptoms that people report to a professional. Since some are unwilling to share these symptoms, we would underestimate the risk of depression if we relied on this information alone.5

This is particularly important for older generations, who lived much of their lives at a time when recognition and acceptance of mental illness was lower. That relates to a second problem: people from different generations might be less willing to report symptoms.6

Another issue is that getting these estimates on a global level is difficult because this data is lacking across many countries. This is especially true for low-income countries.7 For example, the Global Burden of Disease study finds that only a quarter of countries and territories had direct data on the prevalence of major depression between 2005 and 2015.8

This means our findings mostly come from a small number of high-income countries where these studies have been done.

But even in countries where the data does exist, there is yet another major challenge. People often forget about previous episodes of depression – especially if they happened a long time ago. This is called ‘recall bias’, and it is one more problem that makes it hard to rely on people's self-report of symptoms of depression.

You can see this in the chart. This comes from a large study of people who were interviewed several times, years apart, about symptoms of mental and physical illness they had in their lives.9

Some people described having an episode of depression between one interview and the next. But some failed to recall episodes that they described in earlier interviews. This led to a more or less constant share who described lifetime depression at each interview.

As we might expect, older people were much more likely to forget previous symptoms. People older than 60 were around seven times more likely to forget past episodes than those under 50.

Global Trends in Mental Health Disorder

From Schizophrenia to Depression

By Amit [source]

About this dataset

This dataset contains informative data from countries across the globe about the prevalence of mental health disorders including schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression and alcohol use disorders. By providing this data in an easy to visualise format you can gain an insight into how these issues are impacting lives; allowing for a deeper understanding of these conditions and the implications. Through this reflection you may be able to answer some important questions: - What are the types of mental health disorder that people around the world suffer? - How many people in each country suffer mental health problems? - Are men or women more likely to have depression? - Is depression linked with suicide and what is the percentage rate? - In which age groups is depression more common?
From exploring patterns between prevalence rates through in-depth data visualisation you’ll be able to further understand these complex issues. The knowledge gained from this dataset can help bring valuable decision making skills such as research grants, policy making or preventative intervention plans across various countries. So if you wish to create meaningful data viz then start with this global prevalence of mental health disorder’s together with accompanying videos for extra context - Deepen your understanding about Mental Health Disorders today!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Using this dataset is quite straightforward. Each row of the table contains information about a certain country or region for a certain year. The following columns are provided: Entity (the country or region name), Code (the code for the country or region), Year (the year the data was collected) Schizophrenia (% - percentage of people with schizophrenia), Bipolar Disorder (%) - percentage of people with bipolar disorder) Eating Disorders (%) - Percentage of individuals with disordered eating patterns Anxiety Disorders (%) - Percentage of individuals with anxiety Drug Use Disorders (%) - Percentage figures for those struggling with substance abuse Depression (%) – Percentages relating to those struggling with depressive illness Alcohol Use Disorders (%) – Percentages relating to those battling alcoholism

Using this dataset requires no special skills; however it is best suited for those comfortable navigating spreadsheets and tables as well as analyzing numerical information quickly and accurately. Many software suites like excel are useful here but simple internet searches will reveal free alternatives if your preference is web-based solutions!

By piecing together these different columns’ values we can get an idea if prevalence rates across different types of mental illnesses increase or decrease over time. For example we could compare depression levels between 2015 and 2018 by creating two separate sets containing information filtered just within our parameters respectively only reading records from 2015 then 2018). From here we can see whether numbers changed very much or stayed stagnant supefying any sort of patterns that could exist

Research Ideas

• Visualizing the prevalence of mental health disorders - Create a data visualization that compares and contrasts the prevalence of depression, anxiety, bipolar disorder, schizophrenia, eating disorders, alcohol use disorder and drug use disorder across different countries. This could provide insight into global differences in mental health and potential causes of those differences.

• Mapping depression rates - Create an interactive map that shows both regional and national variations in depression rates within a specific country or region. This would allow people to easily identify areas with higher or lower than average prevalence of depression which could help inform decision-makers when it comes to policy-making related to mental healthcare services provisioning.

• Developing predictive models for mental health - Use the data from this dataset as part of a larger machine learning project to build predictive models for mental health across countries or regions based on various factors such as demographics, economic indicators etc., This can be helpful for researchers working on understanding populations’ susceptibility towards developing certain disorders so as to craft appropriate preventive strategies accordingly

Acknowledgements

If you use this dataset in your research, please credit the original aut...

Global Mental Health Disorders

Prevalence of Common Mental Health Conditions, 2005-2017

By Amit [source]

About this dataset

This dataset contains valuable information about the prevalence of mental health disorders including schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression, and alcohol use disorders from various countries across the globe. Mental health is a critical and complex issue which touches us all and this dataset allows a deeper dive into the quantitative understanding of its prevalence and geographical distribution. With this data at hand one can gain insight on questions such as: which countries have rates of mental illness that are higher or lower than average? Which regions are disproportionately dealing with certain types of mental health disruptions? Who is struggling with particular types of illnesses? This data provides answers to those inquiries as well as helping us gain a better understanding of how we can take action towards increasing global awareness, prevention efforts, and access to vital resources that help individuals become healed and empowered

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides information on the prevalence of mental health disorders globally, with data collected from various countries in a given year. It includes statistics on several types of mental health disorders, such as schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders and depression.

Using this dataset can provide useful insights into the prevalence of mental health conditions worldwide. This could be used to better understand how different countries are affected by mental health issues and to identify areas that may need more help or attention. The data is broken down by country or region and year to allow for a better understanding of trends over time.

To use this dataset effectively for research or data analysis purposes it is important to first familiarize yourself with the columns available in the dataset: Entity (country/region), Code (country code), Year (year in which the data was collected), Schizophrenia (%) , Bipolar Disorder (%) , Eating Disorders (%) , Anxiety Disorders (%) , Drug Use Disorders (%) , Depression (%) and Alcohol Use Disorders (%). Each column represents a specific type of mental health disorder and provides information on its prevalence rate in each country/region during that calendar year.

Once you have an understanding of these columns you can begin analyzing the data to gain further insights into global trends related to these mental health conditions. You might perform descriptive analyses such as finding average percentages across different groups (e.g., genders) or time periods, as well as performing inferential analyses like assessing relationships between different variables within your data set (e.g., correlation). Additionally you could create visualizations such as charts, maps or other graphics that help make sense out of large amounts of statistical information easily accessible to a wider audience

Research Ideas

• Creating age-group specific visualizations and infographics that compare the prevalence of mental health disorders in different countries or regions to better understand how the issue of depression or anxiety intersects with factors such as gender, culture, or socioeconomic status.
• Creating a global map visualization that shows the prevalence of different mental health disorders in different countries/regions to demonstrate disparities between places and provide a way for policy makers to better target areas most affected by these issues.
• Developing data visualizations exploring relationships between demographic variables (e.g., gender, age) and prevalence of mental health disorder types such as depression or anxiety disorders in order to gain insight into possible correlations between them

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Mental health Depression disorder Data.csv | Column name | Description | |:------------------------------|:--------------------------------------------------------------------------------------| | Entity | The name of the country or region. (String) | | Code ...

This dataset presents the prevalence of depression and anxiety among adults, serving as a key indicator of mental health within the population. It is intended to support monitoring and evaluation efforts aimed at improving mental health outcomes and reducing the burden of common mental disorders. The data is expressed as a percentage, reflecting the proportion of adults experiencing depression and/or anxiety.

Rationale

Mental health is a critical component of overall well-being. Monitoring the prevalence of depression and anxiety in adults helps inform public health strategies, allocate resources effectively, and evaluate the impact of mental health interventions. Reducing the prevalence of these conditions is a priority for improving quality of life and reducing associated social and economic costs.

Numerator

The numerator for this indicator is currently unspecified. It would typically represent the number of adults identified as experiencing depression and/or anxiety within a defined population and time period.

Denominator

The denominator is also unspecified in the current metadata. It would generally be the total number of adults in the population under study during the same time period.

Caveats

At present, the dataset lacks detailed definitions for both the numerator and denominator, as well as the data sources. This limits the interpretability and comparability of the indicator. Users should exercise caution when drawing conclusions or making comparisons based on this data.

External References

No external references have been provided. For further context or methodological guidance, users may refer to national health surveys or reports from organizations such as the World Health Organization.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

BackgroundDepression and suicide are responsible for a substantial burden of disease globally. Evidence suggests that intimate partner violence (IPV) experience is associated with increased risk of depression, but also that people with mental disorders are at increased risk of violence. We aimed to investigate the extent to which IPV experience is associated with incident depression and suicide attempts, and vice versa, in both women and men.Methods and FindingsWe conducted a systematic review and meta-analysis of longitudinal studies published before February 1, 2013. More than 22,000 records from 20 databases were searched for studies examining physical and/or sexual intimate partner or dating violence and symptoms of depression, diagnosed major depressive disorder, dysthymia, mild depression, or suicide attempts. Random effects meta-analyses were used to generate pooled odds ratios (ORs). Sixteen studies with 36,163 participants met our inclusion criteria. All studies included female participants; four studies also included male participants. Few controlled for key potential confounders other than demographics. All but one depression study measured only depressive symptoms. For women, there was clear evidence of an association between IPV and incident depressive symptoms, with 12 of 13 studies showing a positive direction of association and 11 reaching statistical significance; pooled OR from six studies = 1.97 (95% CI 1.56–2.48, I2 = 50.4%, pheterogeneity = 0.073). There was also evidence of an association in the reverse direction between depressive symptoms and incident IPV (pooled OR from four studies = 1.93, 95% CI 1.51–2.48, I2 = 0%, p = 0.481). IPV was also associated with incident suicide attempts. For men, evidence suggested that IPV was associated with incident depressive symptoms, but there was no clear evidence of an association between IPV and suicide attempts or depression and incident IPV.ConclusionsIn women, IPV was associated with incident depressive symptoms, and depressive symptoms with incident IPV. IPV was associated with incident suicide attempts. In men, few studies were conducted, but evidence suggested IPV was associated with incident depressive symptoms. There was no clear evidence of association with suicide attempts.Please see later in the article for the Editors' Summary

PurposeSocial restrictions and government-mandated lockdowns implemented worldwide to kerb the SARS-CoV-2 virus disrupted our social interactions, behaviours, and routines. While many studies have examined how the pandemic influenced loneliness and poor mental health, such as depression, almost none have focussed on social anxiety. Further, how the change in social restrictions affected change in mental-health and well-being has not yet been explored.MethodsThis is a longitudinal cohort study in community dwellers who were surveyed across three timepoints in the first six months of the pandemic. We measured loneliness, social anxiety, depression, and social restrictions severity that were objectively coded in a sample from Australia, United States, and United Kingdom (n = 1562) at each time point. Longitudinal data were analysed using a multivariate latent growth curve model.ResultsLoneliness reduced, depression marginally reduced, and social anxiety symptoms increased as social restrictions eased. Specific demographic factors (e.g., younger age, unemployment, lower wealth, and living alone) all influenced loneliness, depression, and social anxiety at baseline. No demographic factors influenced changes for loneliness; we found that those aged over 25 years reduced faster on depression, while those younger than 25 years and unemployed increased faster on social anxiety over time.ConclusionWe found evidence that easing social restrictions brought about additional burden to people who experienced higher social anxiety symptoms. As country-mandated lockdown and social restrictions eased, people are more likely report higher social anxiety as they readjust into their social environment. Mental health practitioners are likely to see higher levels of social anxiety in vulnerable communities even as social restrictions ease.

BackgroundAnxiety disorders, depression and schizophrenia are the focus of global mental health attention, resulting in a significant number of disability-adjusted life years and a considerable social and economic burden. It’s can affect the socioeconomic landscape as a result of experiencing a global epidemic. And rarely, different Socio-demographic Index (SDI) levels and Age-Period-Cohort (APC) have been used to evaluate the prevalence of mental disorders worldwide.MethodsUsing data from the Global Burden of Disease 2021 (GBD) database, this study assessed trends in the incidence and prevalence of anxiety disorders, depression, and schizophrenia in countries with different SDI levels from 1990 to 2021. Joinpoint and periodic cohort (APC) models were used to sort out the effects of age, period and cohort on incidence. Data were categorized into 5-year age groups and 95% uncertainty intervals (UI) were calculated to account for data variability.ResultsIn countries with different SDI levels, the age-standardized average annual percentage change (AAPC) in the incidence of anxiety were all shown to be increasing, and there were large gender differences between the different SDI levels, with a maximum of 0.97 (0.76–1.18) for females in countries with a high SDI level, Age-standardized more rates per 100,000 people in high SDI countries, from 658.87 in 1990 to 841.56 in 2021, and the largest gender differences in countries with a low to moderate SDI level, with AAPCs for males and females of 0.04 (0.04–0.05), 0.86 (0.63–1.09); for depression, only the countries with medium-high SDI levels were statistically significant compared to the countries with medium-low SDI levels, with AAPCs of 0.05 (0.04–0.07), 0.04 (0.04–0.05); for schizophrenia in addition to the AAPCs of the countries with medium-high SDI levels showed an increase of 0.16 (0.13–0.18); the rest decreased.ConclusionThis study highlights the current status of global incidence and prevalence of mental disorders and examines the complex interactions between the period of onset and cohort of onset of mental disorders using APC modeling, with differences in gender differences in mental disorders in countries with different SDIs, and significant differences in countries with low to medium SDI levels, requiring further exploration of the mechanisms by which socio-economic development influences gender-specific mental health. Countries with different SDI levels have responded to unique trends within their specific socioeconomic, cultural, and historical contexts, suggesting the need for contextualized public health strategies to effectively respond to and manage the incidence and prevalence of mental disorders in these different settings. Prevalence of mental disorders. This points the way to more in-depth future research on treatments and interventions for mental disorders.

The World Health Organization estimates that almost 300 million people suffer from depression worldwide. Depression is the most common mental health disorder and shows racial disparities in disease prevalence, age of onset, severity of symptoms, frequency of diagnosis, and treatment utilization across the United States. Since depression has both social and genetic risk factors, we propose a conceptual model wherein social stressors are primary risk factors for depression, but genetic variants increase or decrease individual susceptibility to the effects of the social stressors. Our research strategy incorporates both social and genetic data to investigate variation in symptoms of depression (CES-D scores). We collected data on financial strain (difficulty paying bills) and personal social networks (a model of an individual’s social environment), and we genotyped genetic variants in five genes involved in stress reactivity (HTR1a, BDNF, GNB3, SLC6A4, and FKBP5) in 135 African Americans residing in Tallahassee, Florida. We found that high financial strain and a high percentage of people in one’s social network who are a source of stress or worry were significantly associated with higher CES-D scores and explained more variation in CES-D scores than did genetic factors. Only one genetic variant (rs1360780 in FKBP5) was significantly associated with CES-D scores and only when the social stressors were included in the model. Interestingly, the effect of FKPB5 appeared to be strongest in individuals with high financial strain such that participants with a T allele at rs1360780 in FKBP5 and high financial strain had the highest mean CES-D scores in our study population. These results suggest that material disadvantage and a stressful social environment increases the risk of depression, but that individual-level genetic variation may increase susceptibility to the adverse health consequences of social stressors.

This is a data collections is related to the ESRC 'Living with SAD' research, an interdisciplinary project featuring cultural geography, psychiatry and arts practice.

The purpose for the study was to understand more about the lived experience of SAD with participants who worked with the research team via semi-structured seasonal interviews; seasonal diaries; workshops over one winter; and survey. All this data is deposited in the collection in anonymized form.

In the project and working experimentally with creative practice and across the interdisciplinary expertise, a Wintering Well workshop programme was developed that sought to make an intervention in winter experience for those experiencing seasonal low mood and depression. The data set combines 'before' and 'after' interviews and artistic extracts from creative journals completed as part of the Wintering Well programme.

The data set combines different data: questionnaire and mood survey returns; transcribed interview data, artistic drawings and photographs by research participants.

The findings from the data tell us that seasonal depression affects many aspect of social life and feelings about physical and mental health. The Wintering Well Workshop data combine to suggest that creative interventions in winter experience are meaningful for those people who find themselves on a SAD spectrum in ways that make a positive difference to living in winter and thinking about SAD.

Picture the scene. It's one we can all identity with, at some level: late October. The clocks have gone back. The mornings are colder and certainly looking darker. Light itself feels like a precious and endangered thing. A shift is underway. Marked by a downturn in energy and mood, it's tougher to get up for work, feels harder to think clearly, or to muster much in the way of enthusiasm. During the worst spells, there's an unmistakable feeling of sinking ...

Feelings associated with the changing seasons, and moods that seem to be governed by the nature of the weather overhead and related qualities of natural light, are a phenomenon known to us all. Seasonal affective disorder (SAD) is an intensified form of this lived experience that, for considerable numbers of people in the UK, can be debilitating and limiting, resulting in emotional challenges, lowered mood, and feelings of anxiety. This research project enters the lived experience of SAD, seeking to examine its occurrence and impacts in individuals' life-worlds. Working closely with people who self-identify as experiencing depression on a SAD spectrum, the research team will develop narrative, creative and therapeutic-educational resources more fully to examine and reflect SAD experiences, and to build a self-help programme to be hosted by the NHS-approved website, 'Living Life to the Full', to which over 40,000 people register annually. The programme will offer a range of well-being interventions to mitigate against negative experiences of lightness-darkness and changing seasons, in both urban and rural environments.

The research team combines differing skills and approaches, suited to interdisciplinary practice and public engagement. It is comprised of cultural geographers and a creative arts-health practitioner, jointly working with a cognitive behavioural therapy (CBT) expert. By focusing attention on SAD as a widely experienced, but poorly understood, affective phenomenon, the research project will have considerable public impact, initiating national conversations about addressing questions of how to live well through altered seasonal patterns and envisaging the sorts of adaptive life skills and cultural tools required for the mental health challenges now associated with global climate change. A network of project partnerships held with national-level organisations will leverage our finding to create meaningful 'national conversations' on mental health, sustainability and climate resilience in the public sector. Our partners - an expert advisory group - will ensure strategic input to the project, and have already helped us identify clear pathways to generate research impact. In future times, we all might be at risk of feeling SAD in relation to changing climate conditions (stormier weather and smoke-filled darker skies) and this project offers targeted resources to help mitigate these affects, as well as offering guided ways to increase creative and embodied connections between people and outdoor environments.

Background: Depression is one of the most prevalent psychological disorders worldwide. Although psychotherapy for depression is effective, there are barriers to its implementation in primary care in Spain. The use of the Internet has been shown to be a feasible solution. However, the acceptability of Internet-based interventions has not been studied sufficiently.Objective: To assess the acceptability of an Internet-based intervention (IBI) for depression in primary care, and explore the relationship between expectations and satisfaction and the improvement in the clinical variables in primary care patients receiving this intervention. Furthermore, it offers data about the effects of some sociodemographic characteristics on these acceptability variables and analyzes whether the expectations are related to finalizing the intervention.Methods: Data were based on depressive patients who were participants in a randomized controlled trial. In the present study, we present the data from all the participants in the Internet intervention groups (N = 198). All the participants filled out the expectation and satisfaction scales (six-item scales regarding treatment logic, satisfaction, recommending, usefulness for other disorders, usefulness for the patient, and unpleasantness), the Beck Depression Inventory-II, and the secondary outcome measures: depression and anxiety impairment, and positive and negative affect.Results: Results showed that participants’ expectations and satisfaction with the program were both high and differences in expectations and satisfaction depended on some sociodemographic variables (age: older people have higher expectations; sex: women have greater satisfaction). A positive relationship between these variables and intervention efficacy was found: expectations related to “usefulness for the patient” were a statistically related predictor to the results on the BDI-II (Beta = 0.364), and the perception of how logical the treatment is (Beta = 0.528) was associated with change in the clinical variable. Furthermore, the higher the expectations, the higher the improvements exhibited by the patients in all measures evaluated during the ten intervention modules. High expectations were also directly related to finalizing the intervention.Conclusions: This is the first study in Spain to address this issue in the field of IBIs for depression in primary care. The IBI showed high acceptance related to the intervention’s efficacy and completion. Research on IBI acceptability could help to implement the treatment offered.Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT01611818.

This publication contains the official statistics about uses of the Mental Health Act ('the Act') in England during 2023-24. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. People may be detained in secure psychiatric hospitals, other NHS Trusts or at Independent Service Providers (ISPs). All organisations that detain people under the Act must be registered with the Care Quality Commission (CQC). In recent years, the number of detentions under the Act have been rising. An independent review has examined how the Act is used and has made recommendations for improving the Mental Health Act legislation. In responding to the review, the government said it would introduce a new Mental Health Bill to reform practice. This publication does not cover: 1. People in hospital voluntarily for mental health treatment, as they have not been detained under the Act (see the Mental Health Bulletin). 2. Uses of section 136 where the place of safety was a police station; these are published by the Home Office.

BackgroundHumanitarian crises and disasters affect millions of people worldwide. Humanitarian aid workers are civilians or professionals who respond to disasters and provide humanitarian assistance. In doing so, they face several stressors and traumatic exposures. Humanitarian aid workers also face unique challenges associated with working in unfamiliar settings.ObjectiveTo determine the occurrence of and factors associated with mental ill-health among humanitarian aid workers.Search strategyCINAHL plus, Cochrane library, Global Health, Medline, PubMed, Web of Science were searched from 2005–2020. Grey literature was searched on Google Scholar.Selection criteriaPRISMA guidelines were followed and after double screening, studies reporting occurrence of mental ill-health were included. Individual narratives and case studies were excluded, as were studies that reported outcomes in non-humanitarian aid workers.Data analysisData on occurrence of mental ill-health and associated factors were independently extracted and combined in a narrative summary. A random effects logistic regression model was used for the meta-analysis.Main resultsNine studies were included with a total of 3619 participants, reporting on five types of mental ill-health (% occurrence) including psychological distress (6.5%-52.8%); burnout (8.5%-32%); anxiety (3.8%-38.5%); depression (10.4%-39.0%) and post-traumatic stress disorder (0% to 25%). Hazardous drinking of alcohol ranged from 16.2%-50.0%. Meta-analysis reporting OR (95% CI) among humanitarian aid workers, for psychological distress was 0.45 (0.12–1.64); burnout 0.34 (0.27–0.44); anxiety 0.22 (0.10–0.51); depression 0.32 (0.18–0.57) and PTSD 0.11 (0.03–0.39). Associated factors included young age, being female and pre-existing mental ill-health.ConclusionsMental ill-health is common among humanitarian aid workers, has a negative impact on personal well-being, and on a larger scale reduces the efficacy of humanitarian organisations with delivery of aid and retention of staff. It is imperative that mental ill-health is screened for, detected and treated in humanitarian aid workers, before, during and after their placements. It is essential to implement psychologically protective measures for individuals working in stressful and traumatic crises.

These Census Tract-level datasets described here provide de-identified diagnosis data for customers of three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) that have filed a claim for depression medications in 2015 and 2016. The data also includes the number of enrolled members in the three participating managed care organizations in 2015 and 2016.

Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

(Note: Part of the content of this post was adapted from the original DIRECT Psychoradiology paper (https://academic.oup.com/psyrad/article/2/1/32/6604754) and REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)Major Depressive Disorder (MDD) is the second leading cause of health burden worldwide (1). Unfortunately, objective biomarkers to assist in diagnosis are still lacking, and current first-line treatments are only modestly effective (2, 3), reflecting our incomplete understanding of the pathophysiology of MDD. Characterizing the neurobiological basis of MDD promises to support developing more effective diagnostic approaches and treatments.An increasingly used approach to reveal neurobiological substrates of clinical conditions is termed resting-state functional magnetic resonance imaging (R-fMRI) (4). Despite intensive efforts to characterize the pathophysiology of MDD with R-fMRI, clinical imaging markers of diagnosis and predictors of treatment outcomes have yet to be identified. Previous reports have been inconsistent, sometimes contradictory, impeding the endeavor to translate them into clinical practice (5). One reason for inconsistent results is low statistical power from small sample size studies (6). Low-powered studies are more prone to produce false positive results, reducing the reproducibility of findings in a given field (7, 8). Of note, one recent study demonstrate that sample size of thousands of subjects may be needed to identify reproducible brain-wide association findings (9), calling for larger datasets to boost effect size. Another reason could be the high analytic flexibility (10). Recently, Botvinik-Nezer and colleagues (11) demonstrated the divergence in results when independent research teams applied different workflows to process an identical fMRI dataset, highlighting the effects of “researcher degrees of freedom” (i.e., heterogeneity in (pre-)processing methods) in producing disparate fMRI findings.To address these critical issues, we initiated the Depression Imaging REsearch ConsorTium (DIRECT) in 2017. Through a series of meetings, a group of 17 participating hospitals in China agreed to establish the first project of the DIRECT consortium, the REST-meta-MDD Project, and share 25 study cohorts, including R-fMRI data from 1300 MDD patients and 1128 normal controls. Based on prior work, a standardized preprocessing pipeline adapted from Data Processing Assistant for Resting-State fMRI (DPARSF) (12, 13) was implemented at each local participating site to minimize heterogeneity in preprocessing methods. R-fMRI metrics can be vulnerable to physiological confounds such as head motion (14, 15). Based on our previous work examination of head motion impact on R-fMRI FC connectomes (16) and other recent benchmarking studies (15, 17), DPARSF implements a regression model (Friston-24 model) on the participant-level and group-level correction for mean frame displacements (FD) as the default setting.In the REST-meta-MDD Project of the DIRECT consortium, 25 research groups from 17 hospitals in China agreed to share final R-fMRI indices from patients with MDD and matched normal controls (see Supplementary Table; henceforth “site” refers to each cohort for convenience) from studies approved by local Institutional Review Boards. The consortium contributed 2428 previously collected datasets (1300 MDDs and 1128 NCs). On average, each site contributed 52.0±52.4 patients with MDD (range 13-282) and 45.1±46.9 NCs (range 6-251). Most MDD patients were female (826 vs. 474 males), as expected. The 562 patients with first episode MDD included 318 first episode drug-naïve (FEDN) MDD and 160 scanned while receiving antidepressants (medication status unavailable for 84). Of 282 with recurrent MDD, 121 were scanned while receiving antidepressants and 76 were not being treated with medication (medication status unavailable for 85). Episodicity (first or recurrent) and medication status were unavailable for 456 patients.To improve transparency and reproducibility, our analysis code has been openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Yan_2019_PNAS. In addition, we would like to share the R-fMRI indices of the 1300 MDD patients and 1128 NCs through the R-fMRI Maps Project (http://rfmri.org/REST-meta-MDD). These data derivatives will allow replication, secondary analyses and discovery efforts while protecting participant privacy and confidentiality.According to the agreement of the REST-meta-MDD consortium, there would be 2 phases for sharing the brain imaging data and phenotypic data of the 1300 MDD patients and 1128 NCs. 1) Phase 1: coordinated sharing, before January 1, 2020. To reduce conflict of the researchers, the consortium will review and coordinate the proposals submitted by interested researchers. The interested researchers first send a letter of intent to rfmrilab@gmail.com. Then the consortium will send all the approved proposals to the applicant. The applicant should submit a new innovative proposal while avoiding conflict with approved proposals. This proposal would be reviewed and approved by the consortium if no conflict. Once approved, this proposal would enter the pool of approved proposals and prevent future conflict. 2) Phase 2: unrestricted sharing, after January 1, 2020. The researchers can perform any analyses of interest while not violating ethics.The REST-meta-MDD data entered unrestricted sharing phase since January 1, 2020. The researchers can perform any analyses of interest while not violating ethics. Please visit Psychological Science Data Bank to download the data, and then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information. ACKNOWLEDGEMENTSThis work was supported by the National Key R&D Program of China (2017YFC1309902), the National Natural Science Foundation of China (81671774, 81630031, 81471740 and 81371488), the Hundred Talents Program and the 13th Five-year Informatization Plan (XXH13505) of Chinese Academy of Sciences, Beijing Municipal Science & Technology Commission (Z161100000216152, Z171100000117016, Z161100002616023 and Z171100000117012), Department of Science and Technology, Zhejiang Province (2015C03037) and the National Basic Research (973) Program (2015CB351702). REFERENCES1. A. J. Ferrari et al., Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLOS Medicine 10, e1001547 (2013).2. L. M. Williams et al., International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4 (2011).3. S. J. Borowsky et al., Who is at risk of nondetection of mental health problems in primary care? J Gen Intern Med 15, 381-388 (2000).4. B. B. Biswal, Resting state fMRI: a personal history. Neuroimage 62, 938-944 (2012).5. C. G. Yan et al., Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 116, 9078-9083 (2019).6. K. S. Button et al., Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013).7. J. P. A. Ioannidis, Why Most Published Research Findings Are False. PLOS Medicine 2, e124 (2005).8. R. A. Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 10.1038/nrn.2016.167 (2017).9. S. Marek et al., Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654-660 (2022).10. J. Carp, On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments. Frontiers in Neuroscience 6, 149 (2012).11. R. Botvinik-Nezer et al., Variability in the analysis of a single neuroimaging dataset by many teams. Nature 10.1038/s41586-020-2314-9 (2020).12. C.-G. Yan, X.-D. Wang, X.-N. Zuo, Y.-F. Zang, DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339-351 (2016).13. C.-G. Yan, Y.-F. Zang, DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in systems neuroscience 4, 13 (2010).14. R. Ciric et al., Mitigating head motion artifact in functional connectivity MRI. Nature protocols 13, 2801-2826 (2018).15. R. Ciric et al., Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174-187 (2017).16. C.-G. Yan et al., A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183-201 (2013).17. L. Parkes, B. Fulcher, M. Yücel, A. Fornito, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415-436 (2018).18. L. Wang et al., Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. PLoS One 8, e60191 (2013).19. L. Wang et al., The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 36, 768-778 (2015).20. Y. Liu et al., Regional homogeneity associated with overgeneral autobiographical memory of first-episode treatment-naive patients with major depressive disorder in the orbitofrontal cortex: A resting-state fMRI study. J Affect Disord 209, 163-168 (2017).21. X. Zhu et al., Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological psychiatry 71, 611-617 (2012).22. W. Guo et al., Abnormal default-mode

BackgroundClinical depression ranks as a leading cause of disease and disability in young people worldwide, but it is widely stigmatized. The aim of this qualitative research was to gather young people’s experiences of depression stigma and its impact on loneliness, social isolation, and mental health disclosure and secrecy. This novel information can then be used to guide psychosocial interventions for young people with depression.MethodsThis qualitative study included N = 28 young people aged 18–25 years (Mage = 21.30). Participants were recruited from the community who had high symptoms of depression (assessed through a pre-screen using the Mood and Feelings Questionnaire (MFQ) with a benchmark score > 27) or had been recently diagnosed with depression by a medical professional. Semi-structured interviews were based on conceptual model drawings created by participants and analyzed using thematic analysis.ResultsFour main themes emerged: 1) Depression secrecy: positive and negative aspects; 2) Depression disclosure: positive and negative aspects; 3) The solution is selective disclosure; and 4) Participants’ recommendations do not align with personal preferences. In particular, the young people described non-disclosure as a way to be in control, but that secrecy prevented authentic engagement with others. Young people also described disclosure as eliciting more stigma but as necessary to gain help. Finally, the young people described struggling with knowing how much to disclose in relation to their mental health and with whom they could disclose.ConclusionsThis study provides new evidence of how young people with depression experience stigma and its effects on disclosure and mental health secrecy. Knowing how young people struggle with these issues can allow us to develop interventions to encourage them to come forward and discuss their mental health in order to receive appropriate support and treatment. We recommend young people be signposted and have access to mental health champions or nominated teachers in their schools or universities.

BackgroundThe mental health impacts of the COVID-19 pandemic remain a public health concern. High quality synthesis of extensive global literature is needed to quantify this impact and identify factors associated with adverse outcomes.MethodsWe conducted a rigorous umbrella review with meta-review and present (a) pooled prevalence of probable depression, anxiety, stress, psychological distress, and post-traumatic stress, (b) standardised mean difference in probable depression and anxiety pre-versus-during the pandemic period, and (c) comprehensive narrative synthesis of factors associated with poorer outcomes. Databases searched included Scopus, Embase, PsycINFO, and MEDLINE dated to March 2022. Eligibility criteria included systematic reviews and/or meta-analyses, published post-November 2019, reporting data in English on mental health outcomes during the COVID-19 pandemic.FindingsThree hundred and thirty-eight systematic reviews were included, 158 of which incorporated meta-analyses. Meta-review prevalence of anxiety symptoms ranged from 24.4% (95%CI: 18–31%, I2: 99.98%) for general populations to 41.1% (95%CI: 23–61%, I2: 99.65%) in vulnerable populations. Prevalence of depressive symptoms ranged from 22.9% (95%CI: 17–30%, I2: 99.99%) for general populations to 32.5% (95%CI: 17–52%, I2: 99.35) in vulnerable populations. Prevalence of stress, psychological distress and PTSD/PTSS symptoms were 39.1% (95%CI: 34–44%; I2: 99.91%), 44.2% (95%CI: 32–58%; I2: 99.95%), and 18.8% (95%CI: 15–23%; I2: 99.87%), respectively. Meta-review comparing pre-COVID-19 to during COVID-19 prevalence of probable depression and probable anxiety revealed standard mean differences of 0.20 (95%CI = 0.07–0.33) and 0.29 (95%CI = 0.12–0.45), respectively.ConclusionThis is the first meta-review to synthesise the longitudinal mental health impacts of the pandemic. Findings show that probable depression and anxiety were significantly higher than pre-COVID-19, and provide some evidence that that adolescents, pregnant and postpartum people, and those hospitalised with COVID-19 experienced heightened adverse mental health. Policymakers can modify future pandemic responses accordingly to mitigate the impact of such measures on public mental health.

BackgroundPoststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge.AimThis study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims.MethodsA cross-sectional study was carried out with 725 stroke victims who were receiving medical care at three designated tertiary care hospitals in Sylhet from January to December 2023. Depression and disability were measured using the Patient Health Questionnaire-9 and the Modified Rankin Scale. Logistic regression analysis was employed to examine the predictors linked to depression.ResultsAccording to the study, 80.8% of individuals had moderate to severe disability, and 58.1% of them experienced a moderate to severe level of depression. Individuals who had hemorrhagic stroke (AOR 1.31, 95% CI: 0.77–2.25), repeated episodes (AOR 3.41, 95% CI: 1.89–6.14), tobacco use (AOR 1.76, 95% CI: 1.16–2.67), or coexisting health conditions (AOR 1.68, 95% CI: 1.00–2.82) exhibited elevated levels of depression. Participants whose medical expenses covered by relatives or others were six times more likely to experience depressive symptoms (AOR 6.32, 95% CI: 1.61–24.76). Individuals who did not receive rehabilitation services had two times greater odds of being depressed (OR 1.85, 95% CI: 1.23–2.77, p = 0.003). Consequently, individuals with low functional status had eleven times greater levels of depression (AOR 11.03, 95% CI: 7.14–17.04).ConclusionMore than half of the participants in this present study reported moderate to extreme levels of depression which is a serious health issue among Bangladeshi stroke survivors. Understanding the predictors of depression linked to stroke could enhance the effectiveness of therapeutic interventions for this condition. In addition, multidisciplinary teams should work collaboratively to address this serious issue.

Objective: To evaluate whether having general insight into bipolar disorder and its symptoms is affected by the mood state of the patient, using the Insight Scale for Affective Disorders, a hetero-application scale for people with mood disorders.Methods: Ninety-five patients with bipolar disorder were evaluated and divided into different groups according to the mood state presented during assessment (i.e., euthymia, mania and depression). Sociodemographic and clinical data (Hamilton Depression Scale, Young Mania Rating Scale, and Clinical Global Impressions Scale) were recorded. Insight was evaluated using the Insight Scale for Affective Disorders.Results: Patients with bipolar disorder in mania show less insight about their condition than patients in depression or euthymia, and less insight about their symptoms than patients with depression, with the exception of awareness of weight change.Conclusions: Loss of insight during mania may have important implications for treatment compliance and adherence and needs to be taken into account in the clinical management of people with bipolar disorder.

As the COVID-19 pandemic restricted individuals to their houses for a substantial amount of time, people took to the virtual world to stay connected with their peers, family and friends. Likewise, news channels and other forms of electronic media also witnessed a steep rise in viewership all across the globe. That being said, social media has led to adverse impacts on the mental health of individuals through addiction, stress, anxiety, depression and post-traumatic stress syndromes.

The primary objective of this data is to analyze both the positive and negative effects of social media usage on individuals during an unprecedented global lockdown. Existing literature has found significant connections between the use of social media and mental health during extensive periods of lockdown (Swarnam. S., 2021; Pragholapat, A., 2020., Hong, W. et al., 2020). This dataset is used to understand the extent of depression and anxiety experienced by persons restricted to stay-at-home confinements ...

More than 300 million people suffer from depressive disorders globally. People under early-life stress (ELS) are reportedly vulnerable to depression in their adulthood, and synaptic plasticity can be the molecular mechanism underlying such depression. Herein, we simulated ELS by using a maternal separation (MS) model and evaluated the behavior of Sprague–Dawley (SD) rats in adulthood through behavioral examination, including sucrose preference, forced swimming, and open-field tests. The behavior tests showed that SD rats in the MS group were more susceptible to depression- and anxiety-like behaviors than did the non-MS (NMS) group. Nissl staining analysis indicated a significant reduction in the number of neurons at the prefrontal cortex and hippocampus, including the CA1, CA2, CA3, and DG regions of SD rats in the MS group. Immunohistochemistry results showed that the percentages of synaptophysin-positive area in the prefrontal cortex and hippocampus (including the CA1, CA2, CA3, and DG regions) slice of the MS group significantly decreased compared with those of the NMS group. Western blot analysis was used to assess synaptic-plasticity protein markers, including postsynaptic density 95, synaptophysin, and growth-associated binding protein 43 protein expression in the cortex and hippocampus. Results showed that the expression levels of these three proteins in the MS group were significantly lower than those in the NMS group. LC–MS/MS analysis revealed no significant differences in the peak areas of sex hormones and their metabolites, including estradiol, testosterone, androstenedione, estrone, estriol, and 5β-dihydrotestosterone. Through the application of nontargeted metabolomics to the overall analysis of differential metabolites, pathway-enrichment results showed the importance of arginine and proline metabolism; pantothenate and CoA biosyntheses; glutathione metabolism; and the phenylalanine, tyrosine, and tryptophan biosynthesis pathways. In summary, the MS model caused adult SD rats to be susceptible to depression, which may regulate synaptic plasticity through arginine and proline metabolism; pantothenate and CoA biosyntheses; glutathione metabolism; and phenylalanine, tyrosine, and tryptophan biosyntheses.