In 2023, around 9.4 percent of males and 27.3 percent of females in the United States aged 12 to 17 years reported that they had a major depressive episode in the past year. This statistic depicts the percentage of U.S. youths with a major depressive episode in the past year from 2004 to 2023, by gender.

In 2022, approximately 22 percent of U.S. adults were diagnosed with depression, an increase from the previous year. The share of U.S. adults who had depression has gradually increased in the provided time interval. This statistic depicts the share of adults in the U.S. who had depression from 2011 to 2022

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.

In 2022, roughly 58 percent of U.S. adults with cognitive difficulty had depression. The prevalence of depression was higher in those with a disability than those without a disability, it also varied significantly by disability type. This statistic depicts the share of U.S. adults who had depression in 2022, by disability type.

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 ...).

BackgroundThe mental well-being of physicians is increasingly recognized as vital, both for their personal health and the quality of care they provide to patients. Physicians face a variety of mental health challenges, including depression, anxiety, and burnout, which have become prevalent issues globally. These mental health concerns are like those found in the general population but are particularly significant in the demanding healthcare setting.ObjectiveThis review aims to explore the prevalence and correlates of depression, anxiety, and burnout among physicians and residents in training.MethodsA comprehensive literature review was conducted, searching databases such as Medline, PubMed, Scopus, CINAHL, and PsycINFO. The review focused on studies published from 2021 to 2024 that addressed the prevalence of these mental health conditions in physicians and residents. The findings, in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, were summarized in detailed tables.ResultsFollowing titles and abstracts screening, 196 publications were selected for full-text review, with 92 articles ultimately included in the analysis. The results revealed significant variability in the prevalence of burnout, depression, and anxiety. Burnout rates among physicians ranged from 4.7 to 90.1% and from 18.3 to 94% among residents. Depression prevalence ranged from 4.8 to 66.5% in physicians and from 7.7 to 93% in residents. Anxiety rates were between 8 and 78.9% in physicians and 10 to 63.9% in residents. Notably, women reported higher rates of all three conditions compared to men. Key factors influencing these mental health conditions included demographics (age, gender, education, financial status, family situation, occupation), psychological conditions, social factors (stigma, family life), work organization (workload, work conditions), and COVID-19-related issues (caring for COVID-19 patients, fear of infection, working in high-risk areas, concerns about personal protective equipment (PPE), and testing positive).ConclusionThis review indicates a high prevalence of burnout, depression, and anxiety among physicians and residents, with female participants consistently showing higher rates than males. These findings can guide policymakers and healthcare administrators in designing targeted programs and interventions to help reduce these mental health issues in these groups.

In early 2020, characterized by the outbreak of COVID-19, across select OECD countries there was an increase in the prevalence of depression or symptoms of depression. In the United States, for example, around 23.5 percent of repondents reported suffering from depression or had symptoms of depression in 2020, while only 6.6 percent reported depression or depression symptoms in the year prior. The graph shows the results of different national surveys measuring the prevalence of depression or symptoms of depression in early 2020 and in the year before.

This table contains 2016 series, with data for years 1994 - 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 (14 items: Canada; Newfoundland and Labrador; Nova Scotia; Prince Edward Island ...) Sex (3 items: Both sexes; Females; Males ...) Probability of depression (4 items: Total population for the variable probability of depression; Probability of depression; less than 0.9;Probability of depression; 0.9 or greater ...) Characteristics (12 items: Number of persons; Low 95% confidence interval; number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons ...).

This table contains 18480 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Age group (14 items: Total; 12 years and over; 15-19 years; 12-19 years; 12-14 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Probable risk of depression; No risk of depression; Possible risk of depression ...), 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 ...).

This table contains 8400 series, with data for years 1994 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Northwest Territories; Yukon; Northwest Territories including Nunavut ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; No risk of depression; Probable risk of depression; Possible risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

In 2023, it was estimated that nearly 21 percent of 18 to 20 year old women experienced a major depressive episode in the past year. This statistic depicts the percentage of U.S. women with a major depressive episode in the past year as of 2023, by age.

This table contains 334320 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H) ...), Age group (14 items: Total; 12 years and over; 12-14 years; 15-19 years; 12-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Risk level of depression (5 items: Total population for the variable risk of depression; Possible risk of depression; No risk of depression; Probable risk of depression ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

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

ObjectiveTo understand the global trends in depression and identify potential early risk factors for its detection.MethodsThis study is the first to integrate the 2021 Global Burden of Disease (GBD) data with machine learning techniques to explore the risk factors of adolescent depression. A machine learning-based model was constructed, and SHAP (SHapley Additive exPlanations) plots were utilized for interpretive analysis.ResultsFrom 1990 to 2021, the incidence and disability-adjusted life years (DALYs) of depression continued to rise globally among the 10–24 age group, particularly in high socio-demographic index(SDI) regions. Greenland, the United States of America, and Palestine had the highest rates of depression globally. Among the eight machine learning models evaluated, random forest (RF) proved to be the most reliable. SHAP analysis revealed that elevated levels of S100β (0.330), NSE (0.060), and PLT (0.031) significantly increased the risk of depression.ConclusionOur study shows an increasing trend of depression in the global 10–24 age group. Additionally, elevated S100β, NSE, and PLT are identified as key risk factors for depression.

Suicide is one of the leading causes of mortality worldwide. Various factors could lead to suicidal ideation (SI), while depression is the predominant cause among all mental disorders. Studies have shown that alterations in brain structures and networks may be highly associated with suicidality. This study investigated both neurological structural variations and network alterations in depressed patients with suicidal ideation by using generalized q-sampling imaging (GQI) and Graph Theoretical Analysis (GTA). This study recruited 155 participants and divided them into three groups: 44 depressed patients with suicidal ideation (SI+; 20 males and 24 females with mean age = 42, SD = 12), 56 depressed patients without suicidal ideation (Depressed; 24 males and 32 females with mean age = 45, SD = 11) and 55 healthy controls (HC; nine males and 46 females with mean age = 39, SD = 11). Both the generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) values were evaluated in a voxel-based statistical analysis by GQI. We analyzed different topological parameters in the graph theoretical analysis and the subnetwork interconnections in the Network-based Statistical (NBS) analysis. In the voxel-based statistical analysis, both the GFA and NQA values in the SI+ group were generally lower than those in the Depressed and HC groups in the corpus callosum and cingulate gyrus. Furthermore, we found that the SI+ group demonstrated higher global integration and lower local segregation among the three groups of participants. In the network-based statistical analysis, we discovered that the SI+ group had stronger connections of subnetworks in the frontal lobe than the HC group. We found significant structural differences in depressed patients with suicidal ideation compared to depressed patients without suicidal ideation and healthy controls and we also found several network alterations among these groups of participants, which indicated that white matter integrity and network alterations are associated with patients with depression as well as suicidal ideation.

BackgroundDepression is a significant mental health concern among women. The objective of this study was to explore the relationship between reproductive factors such as parity and the frequency of different modes of delivery and depression.MethodsThe analysis was conducted based on the National Health and Nutrition Examination Survey (NHANES) 2005-2014, involving 5,401 non-pregnant women aged 20 years or older. Depression was evaluated using the Patient Health Questionnaire-9 (PHQ-9), while information on parity and delivery modes was self-reported. Multivariable logistic regression models were employed to investigate the association between parity, the frequency of vaginal and cesarean deliveries, and depression. Additionally, smooth curve fitting and subgroup analysis were performed.ResultsAfter adjusting for all covariates, higher parity (OR: 1.12, 95% CI: 1.06-1.19) and an increased frequency of vaginal deliveries (OR: 1.12, 95% CI: 1.06-1.18) were both associated with a higher prevalence of depression. Women with four or more total births exhibited a 1.78-fold greater prevalence of depression relative to those with no births. Similarly, compared to women with no vaginal deliveries or cesarean sections, the prevalence was 1.81 times higher in those with four or more vaginal deliveries and 2.03 times higher in those with four or more cesarean deliveries.ConclusionsGreater parity, particularly a higher frequency vaginal deliveries, is significantly associated to an elevated prevalence of depression among women. The findings highlight the need to consider reproductive history in mental screening for women, especially those with multiple vaginal deliveries.

In 2024, approximately ** percent of 18 to 20-year-old men experienced a major depressive episode in the past year. This statistic depicts the percentage of U.S. men with a major depressive episode in the past year in 2024, by age.

ME/CFS vs Depression Dataset

Welcome to a synthetic dataset designed for classification tasks between Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) and Depression.
This is the first dataset of its kind created specifically to help beginners and researchers explore complex cases of differential diagnosis in mental and chronic health conditions.

🎯 Objective

Predict whether a patient has: - ME/CFS - Depression - Or both (Both)

Based on behavioral, clinical, and symptomatic features.

📋 Features

⚠️ Key Characteristics

• Contains missing values (NaN) in most features (1–5%), simulating real-world data collection issues.
• All numeric features contain controlled noise to prevent perfect class separation.
• Diagnosis logic is based on clinical-like heuristics, making it suitable for training models that could support real-world decisions.

🛠 Suggested Use Cases

• Binary classification: ME/CFS vs Depression
• Multiclass classification: ME/CFS, Depression, Both
• EDA and feature engineering practice
• Missing data imputation techniques
• Medical ML modeling and interpretability

📦 Format

• CSV file
• ~1,000 rows
• UTF-8 encoding

🙌 Author

Created with ❤️ for the Kaggle community.

If you like this dataset — please upvote!
If you have any suggestions or improvements — feel free to comment.

(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