This statistic shows the percentage of U.S. adults that have received treatment for a mental health condition as of April 2018, by gender. According to the results, among those that identified as male, 14 percent were in current treatment for a mental health issue.

In 2022, about ****** suicides were linked to mental health. Out of which, men accounted for the most mental health-related suicides at over ****** in India.

In the United States, the prevalence of mental illness in the past year is more common among females than males and more common among the young than the old. As of 2024, some 26.7 percent of females reported some type of mental illness in the past year, compared to 20 percent of males. Common forms of mental illness include depression, anxiety disorders, and mood disorders. Depression Depression is one of the most common mental illnesses in the United States. Depression is defined by prolonged feelings of sadness, hopelessness, and despair leading to a loss of interest in activities once enjoyed, a loss of energy, trouble sleeping, and thoughts of death or suicide. It is estimated that around five percent of the U.S. population suffers from depression. Depression is more common among women with around six percent of women suffering from depression compared to four percent of men. Mental illness and substance abuse Data has shown that those who suffer from mental illness are more likely to suffer from substance abuse than those without mental illness. Those with mental illness are more likely to use illicit drugs such as heroin and cocaine, and to abuse prescription drugs than those without mental illness. As of 2023, around 7.9 percent of adults in the United States suffered from co-occuring mental illness and substance use disorder.

The Adult Psychiatric Morbidity Survey (APMS) series provides data on the prevalence of both treated and untreated psychiatric disorder in the English adult population (aged 16 and over). This survey is the fourth in a series and was conducted by NatCen Social Research, in collaboration with the University of Leicester, for NHS Digital. The previous surveys were conducted in 1993 (16-64 year olds) and 2000 (16-74 year olds) by the Office for National Statistics, which covered England, Scotland and Wales. The 2007 Survey included people aged over 16 and covered England only. The survey used a robust stratified, multi-stage probability sample of households and assesses psychiatric disorder to actual diagnostic criteria for several disorders. The report features chapters on: common mental disorders, mental health treatment and service use, post-traumatic stress disorder, psychotic disorder, autism, personality disorder, attention-deficit/hyperactivity disorder, bipolar disorder, alcohol, drugs, suicidal thoughts, suicide attempts and self-harm, and comorbidity. All the APMS surveys have used largely consistent methods. They have been designed so that the survey samples can be combined. This is particularly useful for examination of low prevalence population groups and disorders. For example, in the APMS 2014 survey report, analyses of psychotic disorder (Chapter 5) and autism (Chapter 6) have been run using the 2007 and 2014 samples combined. Due to the larger sample size, we consider estimates based on the combined sample to be the more robust. Further notes on the Autism chapter can be found with that chapter and in the 'Additional notes on autism' document below. NHS Digital carried out a consultation exercise to obtain feedback from users on the APMS publication and statistics. The consultation will inform the design, content and reporting of any future survey. The consultation closed 30 December 2016, findings will be made available by April 2017. You can access the results of consultation when available in the Related Links below. A correction has been made to this publication in September 2017. This correction applies to all statistics relating to people receiving medication for a mental health condition and more widely to people accessing mental health treatment. This correction increases the proportion of adults (aged 16-74) with a common mental disorder accessing mental health treatment in 2014 from 37 per cent to 39 per cent. Overall the proportion of all people receiving mental health treatment in 2014 increases from 12 per cent to 13 per cent. Logistic regression models used in chapter 3 have not been corrected due to the change not being large enough to change the findings of this analysis. A further correction has been made to this publication in February 2018. This correction applies to statistics for Asian/Asian British men and all adults in Table 10.5 - Harmful and dependent drinking in the past year (observed and age-standardised), by ethnic group and sex. Statistics for the number of respondents with an AUDIT score of 16 or over previously incorrectly included only those with an AUDIT score between 16 and 19. This has now been corrected to include respondents with an AUDIT score of 20 or more. NHS Digital apologies for any inconvenience caused.

Demographic characteristics and sexual stigma item endorsement among HIV-negative men who have sex with men who responded to the American Men’s Internet Survey (AMIS), AMIS 2018–2019.

Crude and adjusted associations between sexual behavior stigma and mental health outcomes among gay, bisexual and other men who have sex with men, AMIS 2018–2019.

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!

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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...

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.

It was estimated that in 2018 roughly 31 million men in the United States suffered from an anxiety disorder. The statistic illustrates the number of lifetime prevalent cases of anxiety disorders among adults in select countries worldwide in 2018, by gender.

This data is a one-time criminal mental state status data by gender provided by the National Police Agency, and details the mental states of male and female criminals by crime type in 2018. The data consists of the categories of classification, male normal, male mental disorder, male mental retardation, male other mental disorder, male drunk, male unknown, female normal, female mental disorder, female mental retardation, female other mental disorder, female drunk, female menstrual abnormality, and female unknown. Through this, crime occurrence trends by criminal mental state and gender can be identified, and it can be usefully utilized for the police's case analysis and response strategy establishment. In particular, it is important as basic data that can increase the efficiency of case processing by analyzing crime trends by mental disorder, intoxication, and other conditions, and contribute to crime prevention and securing national safety. This data is essential data that can be utilized by the police and related organizations to establish case analysis, preventive measures, and criminal support policies.

This dataset, released June 2020, contains data relating to male hospital admissions during 2017-2018 by a principal diagnosis of Parasitic disease, cancer, Blood-organ disease, metabolic disease, mental health disease, nervous system disease, eye disease, ear disease, circulatory system disease, respiratory system disease, digestive system disease, skin disease, musculoskeletal disease, genitourinary system disease, perinatal condition, congenital deformations, pregnancies and external causes. The data is by Population Health Area (PHA) 2016 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). Population Health Areas, developed by PHIDU, are comprised of a combination of whole SA2s and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure. For more information please see the data source notes on the data. Source: Compiled by PHIDU using data from the Australian Institute of Health and Welfare, supplied on behalf of Stateand Territory health departments for 2017/18; and the average of the ABS Estimated Resident Population, 30 June 2017 and 30 June 2018. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Research on the bidirectional relationship between social connectedness and health/mental health in adolescents is scarce, with most studies on adults. Some of the existing studies exploited the availability of longitudinal data to provide evidence of the existence of a causal relationship, either from social connectedness to health or establish a bidirectional relationship. There are at least two weaknesses associated with earlier research to assess the size of the effects. As acknowledged in the literature, one relates to attributing causality to empirical findings, due to well-known but inadequately addressed endogeneity biases. The other relates to failure to account for potentially important covariates, sometimes due to data limitations, or because such variables are not frequently used in sociological research. Existing research predominantly finds that the strongest path is from social connectedness to health/mental health, with effect estimates modest in size. I followed a quasi-experimental strategy by modelling adolescent students’ perceptions of social connectedness and mental health perceptions as potentially endogenous variables when estimating bidirectional effects. An instrumental variables (IV) modelling approach was followed, supplemented with a recently developed alternative approach to testing the exclusion restrictions of individual instruments. I exploited the rich information available in the PISA 2018 multi-country dataset, which allows for conditioning for a wide array of information on adolescent students’ personal circumstances, self-reported personality-related attributes, relationships with parents; and school characteristics. I found that (1) accounting for endogeneity biases is important; and (2) as opposed to findings reported in the literature, the dominant effect is from mental health perceptions to social connectedness for both male and female participants. The policy relevance of the findings is that adolescent mental health should be the primary focus of interventions, i.e., identifying and treating mental health symptoms as a primary intervention and as a precursor to improving the social connectedness of adolescents.

Background and Data SourceThis dataset is derived from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018, with mortality data linked to the National Death Index, providing comprehensive follow-up until 2019. The study focuses on understanding the complex relationship between depressive symptoms and mortality in older adult populations.Study ObjectiveThe primary aim was to investigate gender-specific nonlinear associations between depressive symptoms, measured by the Patient Health Questionnaire-9 (PHQ-9), and all-cause mortality in adults aged 65 and older.Data Collection and ProcessingPopulation: 8,098 U.S. adults aged ≥65 yearsData Collection Period: 2005-2018 (baseline), mortality follow-up until 2019Median Follow-up: 73 monthsKey VariablesDependent Variable:All-cause mortality statusSurvival timePrimary Independent Variable:PHQ-9 Depressive Symptom ScoresCovariates:Demographic Factors:AgeSexRaceEducation LevelMarital StatusHealth-Related Factors:Body Mass Index (BMI)Smoking StatusAntidepressant UseMajor Depressive Disorder (MDD) StatusAnalytical ApproachWeighted Cox regression modelsPiecewise regression analysisAdditive Cox model with smoothed curve fittingKey FindingsMales: J-shaped mortality risk curveThreshold at PHQ-9 = 6Sharp mortality risk increase for scores ≤6No significant risk increase for scores >6Females: Linear dose-response relationshipConsistent mortality risk increase across PHQ-9 scoresUsage NotesDesigned for research on geriatric mental health and mortalityWeights provided for national representativenessConsult original publication for full methodological detailsLimitationsObservational study designSelf-reported depressive symptomsPotential unmeasured confounding factorsLicensing and AttributionPlease cite the original study when using this dataset in research or publications.

This paper analyzes data from the 2018 Sex in Canada survey (n = 1,015 cisgender men) to examine the association between feminist identification and reported use of prescription ED medication (EDM) during men’s last sexual encounter. Feminist-identified men were substantially more likely to report EDM use than non-feminist men, even after controlling for alcohol use before sex, erection difficulties, sexual arousal, sexual health, mental health, and physical health. One explanation is that feminist men may use EDM to bolster their masculinity when it is otherwise threatened by their identification as feminist. Another is that non-feminist men may be less likely to use prescription EDM because they view accessing healthcare services as a threat to their masculinity. It is also possible that feminist men are more likely to use EDM because they wish to maintain an erection to better please their partner. Lastly, feminist men may be more honest about EDM use than non-feminist men, even though rates are similar. Regardless of the exact reason, therapists can use these results to tailor sexual health messages to clients based on feminist identification. Future work could employ qualitative methods to understand why feminist men report higher rates of EDM use than non-feminist men.

Background: Prior studies comparing the mental healthcare utilisation (MHU) of Danish formerly deployed military personnel (FDP) with the general population have not included data on psychotherapy through the Defence or talking therapy with the general practitioner. This study included these and several other data sources in a comprehensive comparison of MHU between Danish FDP and civilians. Methods: First-time deployed military personnel (N = 10,971) who had returned from a mission to Kosovo, Afghanistan, Iraq or Lebanon between January 2005 and July 2017 were included. A sex and birth-year-matched civilian reference group was randomly drawn from the entire Danish non-deployed population (N = 253,714). Furthermore, a sub-cohort, including male FDP and civilians deemed eligible for military service, was defined. These cohorts were followed up in military medical records and registers covering the primary and secondary civilian health sectors from 2005 to 2018, and the rates of MHU were compared. Results: Approximately half of the initial help-seeking for FDP took place through the Defence (49.4%), and the remainder through the civilian healthcare system. When help-seeking through the Defence was not included, MHU was significantly lower among FDP in the main cohort during the first two years (IRR = 0.84, 95% CI: [0.77, 0.92]) compared to civilians. When help-seeking through the Defence was included, MHU was significantly higher among FDP compared to civilians both in the first two years of follow-up (IRR = 2.01, 95% CI: [1.89, 2.13]) and thereafter (IRR = 1.18, 95% CI: [1.13, 1.23]). In the sub-cohort, these differences were even more pronounced both in the first two years of follow-up and thereafter. Conclusions: MHU was higher among Danish FDP compared to civilians only when data from the Defence was included. The inclusion of data on both civilian and military healthcare services is necessary to evaluate the full impact of deployment on MHU among Danish FDP. This study compared mental healthcare utilisation among Danish deployed military personnel and civilians.Most personnel sought help first through the Defence.When all data sources were included, mental healthcare utilisation was significantly higher among military personnel. This study compared mental healthcare utilisation among Danish deployed military personnel and civilians. Most personnel sought help first through the Defence. When all data sources were included, mental healthcare utilisation was significantly higher among military personnel.

This table represents five variants of health expectancies: -life expectancy in perceived good health. -life expectancy without physical limitations. -life expectancy without chronic morbidity. -life expectancy in good mental health. -life expectancy without GALI-limitations In addition, figures of 'normal' life expectancy are included, so the figures of health expectancy can be related to them. In the table, the data on health expectancy can be split into the following characteristics: -sex (starting from the data of 2018, the category ‘total, men + women’ is added). -age.

Using this table one can see the developments over time of health expectancies. For example it can be seen that morbidity free life expectancy of women shortened during the eighties and nineties. In the same period the life expectancy free of moderate and severe limitations of men increased.

Data available from: 1981

Status of the figures: The figures in this table are definitive.

Changes as of 15 July 2021: The figures of 2020 are added.

When will new figures be published? Third quarter of 2022.

In 2023, around 60 million adults in the United States received treatment or counseling for their mental health within the past year. Such treatment included inpatient or outpatient treatment or counseling, or the use of prescription medication. Anxiety and depression are two common reasons for seeking mental health treatment. Who most often receives mental health treatment? In the United States, women are almost twice as likely than men to have received mental health treatment in the past year, with around 21 percent of adult women receiving some form of mental health treatment in the past year, as of 2021. Considering age, those between 18 and 44 years are more likely to receive counseling or therapy than older adults, however older adults are more likely to take medication to treat their mental health issues. Furthermore, mental health treatment in general is far more common among white adults in the U.S. than among other races or ethnicities. In 2020, around 24.4 percent of white adults received some form of mental health treatment in the past year compared to 15.3 percent of black adults and 12.6 percent of Hispanics. Reasons for not receiving mental health treatment Although stigma surrounding mental health treatment has declined over the last few decades and access to such services has greatly improved, many people in the United States who want or need treatment for mental health issues still do not get it. For example, it is estimated that almost half of women with some form of mental illness did not receive any treatment in the past year, as of 2022. Sadly, the most common reason for U.S. adults to not receive mental health treatment is that they thought they could handle the problem without treatment. Other common reasons for not receiving mental health treatment include not knowing where to go for services or could not afford the costs.

In 2017/18, there were more than ************ females aged between 10 and 24 years in England who were admitted to hospital with a primary diagnosis of an eating disorder, while *** male admissions for eating disorders also occurred in this age group. For both genders, 2017/18 had the highest amount of hospital admissions for eating disorders in the provided time interval.

Can you use brainwave data to discern whether someone is feeling good?

Please cite the following if you are using this data

https://www.researchgate.net/publication/329403546_Mental_Emotional_Sentiment_Classification_with_an_EEG-based_Brain-machine_Interface

https://www.researchgate.net/publication/335173767_A_Deep_Evolutionary_Approach_to_Bioinspired_Classifier_Optimisation_for_Brain-Machine_Interaction

This is a dataset of EEG brainwave data that has been processed with our original strategy of statistical extraction (paper below)

The data was collected from two people (1 male, 1 female) for 3 minutes per state - positive, neutral, negative. We used a Muse EEG headband which recorded the TP9, AF7, AF8 and TP10 EEG placements via dry electrodes. Six minutes of resting neutral data is also recorded, the stimuli used to evoke the emotions are below

1 . Marley and Me - Negative (Twentieth Century Fox) Death Scene 2. Up - Negative (Walt Disney Pictures) Opening Death Scene 3. My Girl - Negative (Imagine Entertainment) Funeral Scene 4. La La Land - Positive (Summit Entertainment) Opening musical number 5. Slow Life - Positive (BioQuest Studios) Nature timelapse 6. Funny Dogs - Positive (MashupZone) Funny dog clips

Our method of statistical extraction resampled the data since waves must be mathematically described in a temporal fashion.

If you would like to use the data in research projects, please cite the following:

J. J. Bird, L. J. Manso, E. P. Ribiero, A. Ekart, and D. R. Faria, “A study on mental state classification using eeg-based brain-machine interface,”in 9th International Conference on Intelligent Systems, IEEE, 2018.

J. J. Bird, A. Ekart, C. D. Buckingham, and D. R. Faria, “Mental emotional sentiment classification with an eeg-based brain-machine interface,” in The International Conference on Digital Image and Signal Processing (DISP’19), Springer, 2019.

This research was part supported by the EIT Health GRaCE-AGE grant number 18429 awarded to C.D. Buckingham.