Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Adults included in this indicator are those who reported ever being diagnosed with depression AND either currently being treated for depression or currently having symptoms of depression.There is growing recognition that mental health is as essential to overall wellbeing as physical health. Individuals who are exposed to chronic stress from financial worry, work and family demands, job insecurity, unsafe living environments, social isolation, or discrimination are at a greater risk for developing mental health conditions, such as depression, anxiety, or post-traumatic stress disorder. Cities and communities can take an active role in fostering mental health by ensuring community safety, promoting equitable employment opportunities and economic security, expanding affordable housing, creating varied opportunities for residents to engage in community issues, reducing the stigma associated with mental health, and providing support services, particularly for seniors and other vulnerable community members.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

35694 rows

154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study

Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities

Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month

Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of depression in adults (aged 18+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to depression in adults (aged 18+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 18+) with depression was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with depression was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have depressionB) the NUMBER of people within that MSOA who are estimated to have depressionAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have depression, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from depression, and where those people make up a large percentage of the population, indicating there is a real issue with depression within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of depression, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of depression.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

A Collection of 120 Psychology Patients with 17 Essential Symptoms to Diagnose Mania Bipolar Disorder, Depressive Bipolar Disorder, Major Depressive Disorder, and Normal Individuals. The dataset contains the 17 essential symptoms psychiatrists use to diagnose the described disorders. The behavioral symptoms are considered the levels of patients Sadness, Exhaustness, Euphoric, Sleep disorder, Mood swings, Suicidal thoughts, Anorexia, Anxiety, Try-explaining, Nervous breakdown, Ignore & Move-on, Admitting mistakes, Overthinking, Aggressive response, Optimism, Sexual activity, and Concentration in a Comma Separated Value (CSV) format. The Normal category refer to the individuals using therapy time for specialized counseling, personal development, and life skill enrichments. While such individuals may also have minor mental problems, they differ from those suffering from Major Depressive Disorder and Bipolar Disorder.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/." This table displays the proportion of adults who were ever told they had a depressive disorder in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This indicator is based on the question: "“Has a doctor, nurse or other health professional EVER told you that you have a depressive disorder (including depression, major depression, dysthymia, or minor depression)?” NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Meta-analysed genome-wide association summary statistics for symptoms of major depressive disorder.Clin = Clinical and case-enriched cohorts: Psychiatric Genomics Consortium, , Australian Genetics of Depression Study and Generation Scotland,Comm = Community (not selected for diagnosis) cohorts: ALSPAC, Estonian Biobank, and UK Biobank.Symptoms analysed:MDD1: Depressed mood most of the day, nearly every dayMDD2: Markedly diminished interest or pleasure in all, or almost all, activities most of the day, nearly every dayMDD3: Significant change in weight or appetiteMDD3a: Significant weight loss or decrease in appetiteMDD3b: Significant weight gain or increase in appetiteMDD4: Sleeping too much or not sleeping enoughMDD4a: Insomnia nearly every dayMDD4b: Hypersomnia nearly every dayMDD5: Changes in speed/amount of moving or speakingMDD5a: Psychomotor agitation nearly every dayMDD5b: Psychomotor slowing nearly every dayMDD6: Fatigue or loss of energy nearly every dayMDD7: Feelings of worthlessness or excessive or inappropriate guiltMDD8: Diminished ability to think or concentrate, or indecisivenessMDD9: Recurrent thoughts of death or suicide or a suicide attempt or a specific plan for attempting suicide

A survey of college students in the United States in 2023-2024 found that around 38 percent had symptoms of depression. Symptoms of depression vary in severity and can include a loss of interest/pleasure in things once found enjoyable, feelings of sadness and hopelessness, fatigue, changes in sleep, and thoughts of death or suicide. Mental health among college students Due to the life changes and stress that often come with attending college, mental health problems are not unusual among college students. The most common mental health problems college students have been diagnosed with are anxiety disorders and depression. Fortunately, these are two of the most treatable forms of mental illness, with psychotherapy and/or medications the most frequent means of treatment. However, barriers to access mental health services persist, with around 22 percent of college students stating that in the past year financial reasons caused them to receive fewer services for their mental or emotional health than they would have otherwise received. Depression in the United States Depression is not only a problem among college students but affects people of all ages. In 2021, around ten percent of those aged 26 to 49 years in the United States reported a major depressive episode in the past year. Depression in the United States is more prevalent among females than males, but suicide is almost four times more common among males than females. Death rates due to suicide in the U.S. have increased for both genders in the past few years, highlighting the issue of depression and other mental health disorders and the need for easy access to mental health services.

PHQ-9 Depression Assessment

14-Days of Ambulatory Mood Dynamics in a General Population

By [source]

About this dataset

This dataset contains 14 days of ambulatory assessment (AA) data related to depression symptoms and mood ratings, as well as findings from a retrospective Patient Health Questionnaire (PHQ-9) designed for depression screening purposes. Furthermore, it contains demographic information about the participants such as their age and gender.

This dataset is composed of various fields including: phq1, phq2, phq3, phq4, phq5, phq6, phq7,ph q8 ,ph q9 ,age ,sex ,q10 ,e11 ,12 w13 w14 e16 e46 e47 happiness.score time period name start time Ph Q day The data gathered through this survey allows us to gain insight into the daily fluctuations in self-reported symptoms experienced by these individuals at different stages of their lives. In addition to providing important clues about possible causes or triggers associated with depressive episodes, this type of survey can also help identify interventions that may prove successful in reducing symptom severity and frequency. Our hope is that we can use this extensive collection of data to inform treatment decisions and ultimately improve outcomes for those affected by depression

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This dataset contains information about the Patient Health Questionnaire (PHQ-9) depression screening assessment, which is used to assess the severity of depressive symptoms over the past two weeks. This dataset can be used to gain insights into depression in a general population sample.

The data is broken down into several categories: PHQ Score (1-9), Age and Gender of participant, Questions 10-47 (Numeric Scores), Happiness score, Time/Period Name/Start Time, and PHQ Day.

In order to use this dataset effectively and accurately analyze your results it is important to understand how each column impacts your results. The PHQ Score column contains information on the severity of depressive symptoms in a scale from 1-9. The Age and Gender columns contain demographic information related to participants while Questions 10-47 represent a range of mental health subject including anhedonia, fatigue, sleep disturbance and changes in appetite or weight that are rated on a numeric scale from 0-4. The Happiness score reflects individual’s subjective ratings at time of assessment with higher scores reflecting greater positivity toward life as reported by participant during study period. Finally the Time/Period Name/Start Time columns provide date and time information related to study period while the PHQ Day represents total number of days elapsed since onset of clinical trial at beginning of assessment period.

By understanding how each category contributes as well as any relationships that may exist between variables researchers can use this data set effectively when analyzing their results for more detailed insights into depression in general population samples across different lengths of time or months scoring methodologies employed reflected by total PHQ scores attained over course on particular month interval included within scope defined for particular study group being considered for analysis by researcher during evaluation protocol being employed developed data research development team assigned project develop analysis offers potential obtainable from working current model designed herein designed incorporated iteration included questionnaires offer basis obtainable utilizing utilized platform outlined herethrough model presented currently established outcome metrics thereby providing tool required necessary review evaluate found current project implementation structure framework wherein needed result may provided evaluated research rationale procedures ultimately yielding findings potentially productive goals desired analytical outcomes original objective initial efforts made implement intended protocol design methodological measures prescribed evaluator's evaluation criteria reported therewith provide result assist uncovering needed research answers discoverable platform established herein presented purpose obviate further attempts previously reviewed limitations encountered earlier trials thus executing member's logbook objectives upgraded format allow corporate setting without interruption driven process overhaul project initiation iterative systemic component procedure triage session estimation techniques management applicable foundational principles

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• Developing an AI-driven screening tool that can rapidly identify and monitor symptom...

NIMH CAT-D

This dataset is a portion of National Institute of Mental Health Characterization and Treatment of Adolescent Depression (NIMH CAT-D) study's data. This preliminary release includes multi-echo resting state data from 190 sessions across 43 participants. The NIMH CAT-D study includes healthy volunteers (HVs) and adolescents with depression. Individuals with depression were scanned every 4 months and HVs were scanned yearly. Some of the depressed participants were enrolled in in-patient or out-patient treatment for a period of the longitudinal study. Please see Sadeghi et al., 2022. The full behavioral data from this study is available on github; this repository only includes behavioral data associated with a scanning session.

Session numbers

Session numbers indicate the study visit with v1-v4 representing the first year of longitudinal scans for depressed participants. Healthy participants only have v1 and v4 scans for their first year. During outpatient treatment, scan sessions are referred to as o1, o4, o8, and o12. During inpatient treatment, participants were scanned approximately weekly, the sessions are referred to as iX were X is the week of their inpatient stay. Some scanning session do double duty as both a regular longitudinal visit and outpatient or inpatient visits. These sessions are given both labels, i.e. v4o1 or v2i14.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness.

The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,

The PHQ-9 Student Depression Dataset contains responses from 400 students to the PHQ-9 questionnaire, a well-established tool for diagnosing depression. This dataset is designed to support the development of machine learning models aimed at automated depression detection by analyzing text responses to common depression-related questions.

The PHQ-9 questionnaire includes 9 questions that assess symptoms of depression over the past two weeks, covering areas like mood, energy levels, sleep, appetite, and thoughts of self-harm. The responses are scored on a scale from 0 (Not at all) to 3 (Nearly every day), with the total score ranging from 0 to 27. Based on this score, the depression severity is classified into one of the following categories: Minimal (0-4) Mild (5-9) Moderate (10-14) Moderately Severe (15-19) Severe (20-27)

This dataset is primarily designed for building models that can assist in automated depression detection. Some potential use cases include: Sentiment Analysis: Analyzing emotional tones in text responses to assess depression. Text Classification: Classifying responses into different depression severity levels. Predictive Modeling: Predicting depression severity based on textual responses. Feature Engineering: Extracting linguistic features (e.g., sentiment, keywords) to predict depression. The dataset is diverse, with synthetic responses across different levels of depression, providing a versatile foundation for machine learning applications. While the dataset does not contain personally identifiable information (PII), real-world applications should follow ethical guidelines regarding privacy, consent, and mental health resources. When working with real data or applying this dataset in clinical research, it is essential to adhere to ethical standards, including:

Data Privacy: Anonymizing personal information. Informed Consent: Ensuring participants give consent before data collection. Support Resources: Providing support for individuals who may exhibit serious mental health concerns.

Applications: Clinical Research: This dataset is valuable for studying depression detection using natural language processing and machine learning techniques. AI in Healthcare: It can be used in the development of tools for automated mental health assessment. Education: Training students or professionals in recognizing depression symptoms and analyzing responses.

(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

Estimates of adults with depressive symptoms with breakdowns by characteristic. Includes comparisons with earlier periods throughout and before the pandemic. Analysis is based on the Opinions and Lifestyle Survey.

This table contains 94080 series, with data for years 2003 - 2003 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (70 items: Newfoundland and Labrador; Health and Community Services Eastern Region; Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12 to 14 years; 12 to 19 years; 15 to 19 years ...) Sex (3 items: Both sexes; Females; Males ...) Probability of depression (4 items: Total population for the variable probability of depression; Probability of depression; 0.9 or greater; Probability of depression; less than 0.9 ...) Characteristics (8 items: Number of persons; High 95% confidence interval; number of persons; Coefficient of variation for number of persons; Low 95% confidence interval; number of persons ...).

Mental Health in Tech Survey

Understanding Employee Mental Health Needs in the Tech Industry

By Stephen Myers [source]

About this dataset

This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. Mental health is an issue that affects all people of all ages, genders and walks of life. The prevalence of these issues within the tech industry–one that places hard demands on those who work in it–is no exception. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector.–and what kinds of resources they rely upon to find help–so that more can be done to create a healthier working environment for all.

This dataset tracks key measures such as age, gender and country to determine overall prevalence, along with responses surrounding employee access to care options; whether mental health or physical illness are being taken as seriously by employers; whether or not anonymity is protected with regards to seeking help; and how coworkers may perceive those struggling with mental illness issues such as depression or anxiety. With an ever-evolving landscape due to new technology advancing faster than ever before – these statistics have never been more important for us to analyze if we hope remain true promoters of a healthy world inside and outside our office walls

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How to use the dataset

In this dataset you will find data on age, gender, country, and state of survey respondents in addition to numerous questions that assess an individual's mental state including: self-employment status, family history of mental illness, treatment status and access or lack thereof; how their mental health condition affects their work; number of employees at the company they work for; remote work status; tech company status; benefit information from employers such as mental health benefits and wellness program availability; anonymity protection if seeking treatment resources for substance abuse or mental health issues ; ease (or difficulty) for medical leave for a mental health condition ; whether discussing physical or medical matters with employers have negative consequences. You will also find comments from survey participants.

To use this dataset effectively: - Clean the data by removing invalid responses/duplicates/missing values - you can do this with basic Pandas commands like .dropna() , .drop_duplicates(), .replace(). - Utilize descriptive statistics such as mean and median to draw general conclusions about patterns of responses - you can do this with Pandas tools such as .groupby() and .describe(). - Run various types analyses such as mean comparisons on different kinds of variables(age vs gender), correlations between different features etc using appropriate statistical methods - use commands like Statsmodels' OLS models (.smf) , calculate z-scores , run hypothesis tests etc depending on what analysis is needed. Make sure you are aware any underlying assumptions your analysis requires beforehand !
- Visualize your results with plotting libraries like Matplotlib/Seaborn to easily interpret these findings! Use boxplots/histograms/heatmaps where appropriate depending on your question !

Research Ideas

• Using the results of this survey, you could develop targeted outreach campaigns directed at underrepresented groups that answer “No” to questions about their employers providing resources for mental health or discussing it as part of wellness programs.
• Analyzing the employee characteristics (e.g., age and gender) of those who reported negative consequences from discussing their mental health in the workplace could inform employer policies to support individuals with mental health conditions and reduce stigma and discrimination in the workplace.
• Correlating responses to questions about remote work, leave policies, and anonymity with whether or not individuals have sought treatment for a mental health condition may provide insight into which types of workplace resources are most beneficial for supporting employees dealing with these issues

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redi...

Genome-wide summary statistics of major depression from the Psychiatric Genomics Consortium (2025)Meta-analysed summary statistics for:Multi-ancestries, excluding 23andMe [DIV]European-ancestries, excluding 23andMe [EUR]European ancestries, excluding 23andMe and UK Biobank [EUR]European ancestries, top 10k SNPs [EUR]Clinical/interview phenotypes [EUR]Electronic health record / register phenotypes [EUR]Questionnaire symptom phenotypes [EUR]Questionnaire symptom phenotypes, excluding UK Biobank [EUR]Files:*.tsv.gz: summary statistics with rich header information*.txt: meta information for cohorts included in each meta-analysis (tab-separated)daner/daner_*.gz: summary statistics in daner formatssf/*_formatted.tsv.gz: summary statistics in GWAS-SSF

This publication contains the official statistics about uses of the Mental Health Act(1) ('the Act') in England during 2018-19. 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. However, some providers that make use of the Act are not yet submitting data to the MHSDS, or submitting incomplete data. Improvements in data quality have been made over the past year. NHS Digital is working with partners to ensure that all providers are submitting complete data and this publication includes guidance on interpreting these statistics. Footnotes (1) The Mental Health Act 1983 as amended by the Mental Health Act 2007 and other legislation.

In this project, we aimed to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research that is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. The main aim of the project was to help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children. We have used data from QResearch, a large consolidated database derived from anonymized health records from general practices in England matched with hospital administrative data, the Hospital Episode Statistics (HES). Further information is available under Related Resources.Problems relating to Maternal Depression and Anxiety Disorders (MDAD) are common and are known to affect child health and development. In the UK, the cost of perinatal mental health problems has been estimated at £8.1 billion for each birth cohort of children, and 72 percent of this cost is related to the direct impact on the children. The overarching aim of our proposed research is to examine the effect of MDAD on child health outcomes, with a special focus on the role that MDAD plays in the development of child depression and anxiety disorders (CDAD) in adolescence. In particular, this research will provide robust empirical evidence to understand how depression and anxiety disorders are transmitted from one generation to the next and to help design interventions aimed at reducing the negative consequences of poor maternal mental health for children. To achieve this aim, we will address the following research questions: 1) Are the negative effects of MDAD on children exclusively explained by genetic transmission and family background characteristics? Or are these negative effects also explained by changes in the child's home environment? If the transmission of mental and anxiety disorders is explained exclusively by genetic traits and family background characteristics, then interventions targeted at reducing the negative effect of MDAD on maternal behaviour, e.g. through cognitive behavioural therapy, would be ineffective. On the contrary, evidence on significant effects of MDAD after controlling for genetic and family background characteristics would suggest that MDAD can lead to changes in the child home environment, e.g. changes in maternal behaviour, harsher parenting style and lower time investments in the child, with negative consequences on children. 2) Do school policies and health practices have a role in attenuating the negative effect of maternal depression on children? We will answer this research question by focusing on whether starting school earlier harms or protects children who are exposed to MDAD, and on whether an early diagnosis of maternal depression can attenuate the negative effects suffered by children. We will develop and use state-of-the-art estimation methods in combination with a novel administrative dataset covering general practices and hospitals created by merging two population-based health databases from England - namely QResearch and Hospital Episode Statistics. Using this merged database, we will create a longitudinal household dataset that will allow us to study the mental health of mothers and their children at different stages of the children's lives up to adolescence. We are a multi-disciplinary team from the Universities of Oxford and York, consisting of experts in applied econometric methods, child and maternal mental health, psychology, general practice, and on the data that we plan to utilise. We will translate our research findings into advice for policy-makers to help them design new interventions aimed at achieving better outcomes for patients suffering from maternal mental health issues and their children. Our research will also have an impact on health practitioners, psychologists, academics and charities working with mothers and children. We will produce papers aimed at academics as well as non-technical outputs to engage with policy-makers and a non-academic audience. Furthermore, by sharing and explaining our data and estimation methods to academics, we will build capacity for further research based on large health datasets. The final central element of the project will be to build the capacity of early career researchers to undertake and lead large interdisciplinary projects. QResearch is a large, anonymised database of GP records from over 35 million patients with longitudinal data tracking back over 30 years & is linked to mortality, cancer registration & hospital data. In our analysis, we use individual-level information on general practice diagnostics, drug prescriptions, and maternity records from HES, which allows us to link children with their respective mothers. The QResearch linked database has high-quality data to support world-leading research to improve our understanding of disease and improve patient care. Our data includes all singletons born between 2002 and 2010.The mother-baby linkage in QResearch is done via maternal identifiers and year of birth.

This dataset provides comprehensive demographic and academic insights, encompassing a diverse range of variables relevant to student well-being, mental health, and academic success. Key demographic details include gender, age, and university affiliation, helping to create a foundation for understanding the student population. Academic factors are detailed through degree level, major, academic year, and current CGPA, offering a clear snapshot of the academic performance and trajectory of students across various fields of study.

In addition to these core academic variables, the dataset also explores students' residential status, providing information on whether they reside on or off-campus. This is particularly valuable in examining how different living arrangements may impact students' experiences and well-being. The dataset also captures important personal experiences related to discrimination, harassment, or bullying within the campus environment. These experiences are critical for researchers looking to understand how adverse social encounters may contribute to mental health challenges or affect academic success.

Lifestyle factors are carefully integrated into the dataset, offering insight into how often students engage in physical activities such as sports and the average number of sleep hours per night. These factors are known to significantly impact both physical and mental health, thus providing important context for understanding student well-being. Students’ satisfaction with their chosen field of study and their perception of the academic workload are also examined, giving researchers insight into how passion for a subject and perceived academic pressure influence students' overall happiness and engagement.

The dataset also addresses significant stressors faced by students, including academic pressure, financial concerns, and the quality of social relationships on campus. Financial worries can be a major burden, potentially affecting not only academic performance but also mental health. Similarly, the quality of social relationships—whether supportive or strained—plays a crucial role in students' emotional and psychological well-being.

One of the most important aspects of the dataset is its inclusion of mental health indicators. It records the frequency of students experiencing depression, anxiety, feelings of isolation, and insecurity about their future. These metrics are invaluable for understanding the mental health landscape of the student body, enabling policymakers and institutions to identify trends, vulnerable groups, and potential areas for intervention. Alongside these challenges, the dataset also highlights activities students engage in to relieve stress, offering insight into coping mechanisms and their effectiveness.

Overall, this dataset provides a rich source of information for researchers and policymakers interested in the intersection of student well-being, mental health, and academic success. The data can be used to inform interventions aimed at improving student support services, enhancing mental health resources, and promoting a healthier, more inclusive campus environment.

Analysis of ‘depression’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/diegobabativa/depression on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Hi,

The original Dataset wad published by Frankcc in the following link: Link Kaggle

The dataset is involved into the analysis of depression. The data was consists as a study about the life conditions of people who live in rurales zones. Because all the columns were not explicated in this challenge so We can´t understand them. We proceded to delete them or ignoring. Fhe final features or columns were the following:

Content

Survey_id Ville_id sex Age Married Number_children education_level total_members (in the family) gained_asset durable_asset save_asset living_expenses other_expenses incoming_salary incoming_own_farm incoming_business incoming_no_business incoming_agricultural farm_expenses labor_primary lasting_investment no_lasting_investmen depressed: [ Zero: No depressed] or One: depressed the main objective is to show statistic analysis and some data mining techniques.

The dataset has 23 columns or dimensions and a total of 1432 rows or objects.

Acknowledgements

The original attribution is to Frankcc i

Inspiration

--- Original source retains full ownership of the source dataset ---