In 2024, it was estimated that nearly 32 percent of men aged 26 to 29 suffered from some mental illness, and 7.1 percent of those in this age group suffered from serious mental illness. This statistic shows the percentage of U.S. men with any or serious mental illness in the past year in 2024, by age.

Global Mental Health Disorders

Prevalence of Common Mental Health Conditions, 2005-2017

By Amit [source]

About this dataset

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

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

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

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

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

Research Ideas

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

Acknowledgements

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

License

See the dataset description for more information.

Columns

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

In 2024, it was estimated that 20 percent of men in the U.S. had some type of mental illness in the past year. This statistic shows the percentage of U.S. men who had any mental illness in the past year from 2008 to 2024.

In 2024, it was estimated that 26.7 percent of women in the U.S. had some type of mental illness in the past year. This statistic shows the percentage of U.S. women who had any mental illness in the past year from 2008 to 2024.

Mental Illness Prevalence Across the US

Substate Level Estimates

By Substance Abuse and Mental Health Services Organization [source]

About this dataset

This dataset contains estimates of serious mental illness in the US by state and substate region from 2012-2014. This data helps to understand better the mental health disparities that exist between states and different regions within states. By looking at this data, researchers can identify the parts of the country with particularly high or low rates of serious mental illness, which can help prioritize resources for affected areas.

The dataset includes estimates along with 95% confidence intervals based on a survey-weighted hierarchical Bayes estimation approach and are generated by Markov Chain Monte Carlo techniques. Columns labeled Map Group can be used to distinguish substate regions included in corresponding maps as well as numerical order for sorting original sort order. For definitions in Substate Region, refer to the National Survey on Drug Use and Health's Substate Region Definitions found here: https://www.samhsa.gov/data/sites/default/files/NSDUHsubstateRegionDefs2014/NSDUHsubstateRegionDefs2014.pdf

This reliable information is provided by SAMHSA, Center for Behavioral Health Statistics and Quality through their National Survey on Drug Use and Health from 2012-2014; helping us gain insights into America’s overall mental health picture – revealing more about where help is needed most urgently so that we can take steps towards a healthier future for all Americans!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Welcome to this dataset! This dataset contains estimates of Serious Mental Illnesses in the United States by state and substate region from 2012 to 2014. It is designed for researchers, analysts, and data scientists looking for information about the prevalence of Serious Mental Illnesses across the US.

Research Ideas

• Performing a trend analysis to identify changes in the estimates of serious mental illnesses over time and across different geographic regions.
• Exploring disparities in serious mental illnesses among certain minority groups or deprived socio-economic subgroups by comparing estimates at the substate level.
• Developing targeted public health strategies and interventions for states with higher than average rates of serious mental illness prevalence

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 redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2012-2014_Substate_SAE_Table_24.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------------------------------------------------------------------------| | Order | A numerical order that can be used to sort the data back to its original order. (Numeric) | | State | The US state associated with the data. (String) | | Substate Region | The substate region associated with the data. (String) | | 95% CI (Lower) | The lower bound of the 95 percent confidence interval for the estimated number of people with serious mental illness in the region. (Numeric) | | 95% CI (Upper) | The upper bound of the 95 percent confidence interval for the estimated number of people with serious mental illness in the region. (Numeric) | | Map Group | A numerical value which can distinguish between different substate regions included in the maps. (Numeric) |

...

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.

This dataset was originally collected for a data science and machine learning project that aimed at investigating the potential correlation between the amount of time an individual spends on social media and the impact it has on their mental health.

The project involves conducting a survey to collect data, organizing the data, and using machine learning techniques to create a predictive model that can determine whether a person should seek professional help based on their answers to the survey questions.

This project was completed as part of a Statistics course at a university, and the team is currently in the process of writing a report and completing a paper that summarizes and discusses the findings in relation to other research on the topic.

The following is the Google Colab link to the project, done on Jupyter Notebook -

https://colab.research.google.com/drive/1p7P6lL1QUw1TtyUD1odNR4M6TVJK7IYN

The following is the GitHub Repository of the project -

https://github.com/daerkns/social-media-and-mental-health

Libraries used for the Project -

Pandas
Numpy
Matplotlib
Seaborn
Sci-kit Learn

Introduction

Mental Health Statistics: Mental health is vital to well-being, influencing how people think, feel, and act. In recent years, there has been increasing recognition of its significance as societies become more aware of the far-reaching effects mental health disorders have on individuals, families, and communities.

Mental health statistics provide crucial insights into these conditions' prevalence, causes, and consequences, enabling policymakers, healthcare providers, and researchers to understand emerging trends better. This data supports effective resource allocation and the development of targeted interventions to tackle mental health issues.

We can pinpoint high-risk groups and regions that require additional support by examining these trends. Additionally, these insights help inform public health initiatives focused on reducing stigma and promoting mental health awareness. Accurate statistics are essential for shaping evidence-based policies emphasizing prevention, early intervention, and improving access to mental health services. As mental health continues to gain attention, continuous data collection and research will be key to addressing the global mental health crisis effectively.

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.

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.

Percentage of persons aged 15 years and over by perceived mental health, by gender, for Canada, regions and provinces.

Graph and download economic data for Medical Services Expenditures by Disease: Mental Illness , MEPS Account Basis (MNINEIEXPMEPS) from 2000 to 2021 about mental health, disease, physicians, healthcare, medical, health, expenditures, services, and USA.

In 2014, 29% of Black women had experienced a common mental disorder in the week before being surveyed, a higher rate than for White women.

Social media plays a central role in today’s lives, both in the U.S. and around the world, and this intersection of constant connectivity and daily use is having measurable effects on mental health. In real-world settings, mental health clinics are increasingly treating young adults whose symptoms correlate with excessive social...

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

VAMC-level statistics on the prevalence, mental health utilization, non-mental health utilization, mental health workload, and psychological testing of Veterans with a possible or confirmed diagnosis of mental illness. Information prepared by the VA Northeast Program Evaluation Center (NEPEC) for fiscal year 2015. This dataset is no longer supported and is provided as-is. Any historical knowledge regarding meta data or it's creation is no longer available. All known information is proved as part of this data set.

Number and percentage of persons based on the perception of their mental health status, by age group and sex.

Mental Health Treatment Access (Over Time)

Tracking Progress from 2014

By Oklahoma [source]

About this dataset

This dataset examines the increase of adult mental health treatment from 2014 to 2018. Through our analysis, we seek to observe patterns and trends that capture the progress made in providing effective services for those in need. Specifically, this dataset contains details on the number of eligible adults receiving mental health treatment and a target for increasing that number by 2018. Using these figures, we can ascertain how successful efforts have been in addressing issues related to mental health among vulnerable populations. With this data set, we can identify areas where such efforts may have been hindered or benefited by external forces to inform future initiatives towards improved access and provision of beneficial services for those experiencing psychological distress. We hope our insights can be used as a starting point for creating sustainable solutions which support individuals’ well being via quality treatments methods with tangible results

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset offers insight into the number of mental health treatment options available for eligible adults in each year from 2014 to 2018. It includes historical data on the number of individuals receiving treatment as well as a target for what should be achieved by 2018.

For this dataset, one can use the following steps to analyze and understand the data: - Identify what is included in each column – years and historical data on eligible adults receiving mental health treatment, as well as a target figure set by 2018. - Review changes in numbers over time – plot a graph or chart to compare results over different years; look out for any unusual dips or highs that may warrant further investigation. - Compare figures against <18 age groups – is there an increase or decrease? How do 18+-year-olds’ access compare with those younger than 18? Are there any correlations between adult mental health treatment access and other social factors such as poverty levels, population distribution etc.? - Set specific goals using the ‘Target’ column – do you want to reach a certain number of users making use of available services by 2018? Set yourself achievable targets and compare your progress against baseline figures reviews your progress up until that point in time Remember: accurate benchmarking isn’t just about achieving your long term learning objectives but also understanding why you did / didn’t succeed along the way!

Research Ideas

• Establishing baseline data of and designing mental health treatment interventions to increase the access to care for eligible adults.
• Identifying disparities in the receipt of mental health services among different population groups based on age, gender, race etc.
• Determining best practices for advertising or outreach campaigns targeted at increasing awareness about mental health resources for eligible adults

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: res_adult_mental_health_treatment_u84c-zt9p.csv | Column name | Description | |:--------------------|:-----------------------------------------------------------------------------------------------------------------| | Years | The year in which the data was collected. (Integer) | | Historical Data | The total number of eligible adults receiving mental health treatment in the corresponding year. (Integer) | | Target | The target set for increasing the number of eligible adults receiving mental health treatment in 2018. (Integer) |

File: res_adult_mental_health_treatement_-_line_chart_72fs-gbb3.csv | Column name | Description | |:--------------------|:-----------------------------------------------------------------------------------------------------------------| | Years | The year in which the data was collected. (Integer) | | Historical Data | The total number of eligible adults receiving mental health treatment in ...

Graph and download economic data for Medical Services Expenditures per Capita by Disease: Mental Illness , MEPS Account Basis (MNINEIPCMEPS) from 2000 to 2021 about mental health, disease, physicians, healthcare, medical, health, expenditures, per capita, services, and USA.

This application provided a way for the public to explore and analyze VA Mental Health Statistics (FY2015 Annual Datasheet).