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

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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 2021, it was estimated that over **** percent of the total global population suffered from an anxiety disorder. This statistic depicts the percentage of the global population with select mental health disorders in 2021.

Introduction. The analysis looks at mental and physical health data from 2000-2019 from various sources the main one being the World Health Organization (WHO).

Task: Analyze health data to gain insights into current consumers health patterns globally and in Kenya to be utilized to make data driven decisions.

Stakeholders: -Company founders and C-suite teams. -Human Resource and Mental Health Professionals. -Government policy makers.

Analysis Objectives: -What is the trend in global and local consumer mental and physical health? -How can these trends influence public and corporate strategies?

ROCCC of Data: A good data source is ROCCC which stands for Reliable, Original, Comprehensive, Current, and Cited.

-Reliablity — High — The data comes from global population sample data sources.

-Originality — LOW — Third party provider (WHO).

-Comprehensive — HIGH — There are several variables summarized into between 1,700-10,980 observations for a period of over 15 years which was fairly comprehensive.

-Current — MID — Data is 3 years old and may not be as relevant as there is no covid data updated to it.

-Cited — HIGH — Data collected from a reliable third party that comprehensively reports its data collection process publicly.

Overall, the dataset is good quality data however its recommended that an updated analysis be done on the health trends during and post-covid.

Key Insights

-There is a higher average suicide rate in men than women both globally and also in Kenya.

-Kenya has a higher average suicide rate for both genders compared to the global average as at 2019.

-The average probability of death between the age of 30 to 70 from from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in Kenya has been decreasing since 2008 however an increase has been observed since 2016.

-There has been a significant increase in the prevalence of alcohol and substance use disorder in Kenya, moreover, the prevalence in the country increases as the prevalence of anxiety disorders, eating disorders and schizophrenia increases according to the Kenyan correlation heat map.

-As evident on the correlation heat map the prevalence various mental health issues have an impact on each other.

-The global probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease has been falling significantly since the 2000s, however, its only been steadily decreasing in Kenya. Men are also at a higher risk of death from these diseases compared to women both globally and locally in Kenya.

-The probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in Kenya has been observed to be significantly inversely proportional to the prevalence of alcohol, substance use anxiety and eating disorders.

-Suicide rates have been observed to not have a significant direct relationship with any mental health disorders both globally and locally however the most significant correlation is the probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in the global analysis.

-Globally a significant inverse relationship between road traffic death rate and eating disorders has been observed however there is a slightly significant relationship between depressive disorders and road traffic death which should be an indicator for further research.

-In Kenya, its been observed that road traffic deaths are inversely proportional to the probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease but directly proportional to eating, anxiety, alcohol and substance use disorders.

-Depressive disorders is the most significant variable that has an impact on suicide rates in Kenya therefore further study can look into the impact of depression on attempted and reported suicide cases and other factors that may influence suicide as it has been on the rise in Kenya.

-Road traffic accidents have a significant impact of the mental health of several Kenyans.

Recommendations.

-There should be more education regarding suicide prevention for NGOs.

-Corporate firms should look into providing observed health insurance and mental health days off in addition to more sick days for the affected.

-The government can implement policies and programs that provide more efficient facilities for the handling of observed health issues.

-Insurance companies can restructure their products around the knowledge that mental health issues in Kenya have a significant direct relationship to each other and also that the prevalence of alcohol and substance use critically impacts the road traffic death rate in Kenya.

-The government should critically look at the increase in the prevalence of alcohol...

This statistic depicts the number of people worldwide with select mental health and substance use disorders as of 2021. According to the data, a total of 359.21 million people worldwide suffered from anxiety as of this time.

The Mental Health Depression Disorder dataset provides comprehensive information on the prevalence of various mental health disorders across different countries over multiple years. This dataset includes data on schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression, and alcohol use disorders. It is a valuable resource for analyzing trends in mental health disorders and understanding the global burden of mental illness.

**Usage: **

This dataset can be used for analyzing trends in mental health disorders, comparing the prevalence of different disorders across countries and years, and conducting epidemiological research. It is valuable for researchers, public health officials, and mental health professionals aiming to understand and address the global burden of mental illness.

**Acknowledgements: **

We acknowledge the contributions of international health organizations and research institutions that made this data publicly available for research and analysis.

BackgroundPopulation-based studies provide the understanding of health-need required for effective public health policy and service-planning. Mental disorders are an important but, until recently, neglected agenda in global health. This paper reviews the coverage and limitations in global epidemiological data for mental disorders and suggests strategies to strengthen the data.MethodsSystematic reviews were conducted for population-based epidemiological studies in mental disorders to inform new estimates for the global burden of disease study. Estimates of population coverage were calculated, adjusted for study parameters (age, gender and sampling frames) to quantify regional coverage.ResultsOf the 77,000 data sources identified, fewer than 1% could be used for deriving national estimates of prevalence, incidence, remission, and mortality in mental disorders. The two major limitations were (1) highly variable regional coverage, and (2) important methodological issues that prevented synthesis across studies, including the use of varying case definitions, the selection of samples not allowing generalization, lack of standardized indicators, and incomplete reporting. North America and Australasia had the most complete prevalence data for mental disorders while coverage was highly variable across Europe, Latin America, and Asia Pacific, and poor in other regions of Asia and Africa. Nationally-representative data for incidence, remission, and mortality were sparse across most of the world.DiscussionRecent calls to action for global mental health were predicated on the high prevalence and disability of mental disorders. However, the global picture of disorders is inadequate for planning. Global data coverage is not commensurate with other important health problems, and for most of the world's population, mental disorders are invisible and remain a low priority.

📘 Dataset Description

This dataset provides a realistic, synthetic simulation of global mental health survey responses from 10,000 individuals. It was created to reflect actual patterns seen in workplace mental health data while ensuring full anonymity and privacy.

🧠 Context & Purpose

Mental health issues affect people across all ages, countries, and industries. Understanding patterns in mental health at work, access to treatment, and stigma around disclosure is essential for shaping better workplace policies and interventions.

This dataset is ideal for:

• Training and evaluating machine learning models
• Practicing classification or clustering techniques
• Performing exploratory data analysis (EDA)
• Studying fairness and bias in mental health predictions
• Creating realistic dashboards for HR analytics or healthcare systems

📊 Dataset Highlights

• 10,000 rows representing anonymized individuals
• Diverse global coverage with country/state info
• Demographic attributes like age, gender, employment type
• Information about work environment and company support
• Responses about mental health history, treatment, and workplace stigma

💡 Example Use Cases

• Predicting the likelihood of an employee seeking mental health treatment
• Identifying factors most correlated with workplace stress
• Segmenting users by mental health risk using clustering
• Building fairness-aware models to reduce bias in mental health predictions

⚠️ Notes

• This dataset is entirely synthetic. No personally identifiable information (PII) or real user data is included.
• It was generated based on patterns observed in public mental health datasets and surveys.

As of August 2025, ** percent of adults surveyed worldwide believed that mental health was the biggest health problem in their country. This statistic illustrates the share of adults worldwide who believed that mental health was the biggest health concern in their country from 2018 to 2025.

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.

The share of respondents who reported their mental health in the lowest range had doubled, from 6.8 percent to 14.4 percent, since the COVID-19 outbreak. This statistic shows the percentage of workers who reported either perfectly healthy or nonfunctional mental health status in the year leading to COVID-19 and in the past week, globally as of April 2020. The survey was conducted among employees in select countries: Australia, France, Germany, New Zealand, Singapore, the United Kingdom and the United States.

Financial overview and grant giving statistics of American Friends of United for Global Mental Health

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...

AI in Mental Health Market Outlook

As per our latest research, the AI in Mental Health market size reached USD 2.3 billion globally in 2024, and is expected to grow at a robust CAGR of 32.7% from 2025 to 2033, reaching an estimated market value of USD 28.1 billion by the end of the forecast period. This remarkable growth trajectory is primarily driven by the increasing adoption of artificial intelligence technologies in mental health applications, the rising global prevalence of mental health disorders, and the urgent need for scalable, cost-effective, and personalized mental healthcare solutions.

The rapid expansion of the AI in Mental Health market is fundamentally underpinned by the growing awareness and destigmatization of mental health issues, which has led to increased demand for accessible and effective mental healthcare services. The integration of advanced AI technologies, such as machine learning, natural language processing, and computer vision, into mental health platforms and solutions is enabling early detection, continuous monitoring, and personalized intervention strategies. These advancements are significantly improving diagnostic accuracy, treatment outcomes, and patient engagement, thereby fueling market growth. Furthermore, the proliferation of digital health platforms and telemedicine services has made mental health support more accessible, particularly in remote and underserved regions, amplifying the market's expansion.

Another critical growth factor for the AI in Mental Health market is the substantial investment from both public and private sectors in digital health innovation and AI research. Governments and healthcare organizations worldwide are prioritizing mental health as a key component of overall well-being, leading to increased funding for AI-driven research and development initiatives. This has resulted in the emergence of a diverse ecosystem of AI-powered mental health solutions, ranging from chatbots and virtual therapists to predictive analytics platforms and wearable devices. These innovations are not only enhancing the efficiency and effectiveness of mental health services but are also reducing the burden on traditional healthcare systems by enabling early intervention and self-management of mental health conditions.

The evolving regulatory landscape and the increasing focus on data privacy and security are also shaping the growth dynamics of the AI in Mental Health market. Regulatory bodies in major markets such as North America and Europe are establishing guidelines and standards for the ethical use of AI in healthcare, which is fostering trust and encouraging wider adoption of AI-powered mental health solutions. Additionally, the integration of AI with electronic health records (EHRs) and other healthcare IT systems is streamlining care delivery and facilitating seamless communication between patients and providers. These factors, combined with the growing adoption of cloud-based deployment models and the increasing availability of high-quality mental health data, are expected to sustain the market's robust growth trajectory over the forecast period.

From a regional perspective, North America continues to lead the AI in Mental Health market in terms of adoption and innovation, driven by strong healthcare infrastructure, high digital literacy, and significant investments in AI research. Europe is also witnessing substantial growth, supported by favorable government initiatives and a growing emphasis on mental health awareness. The Asia Pacific region is emerging as a high-growth market, propelled by the rising prevalence of mental health disorders, increasing healthcare expenditure, and rapid digital transformation in countries such as China, India, and Japan. Latin America and the Middle East & Africa are gradually catching up, with growing investments in healthcare technology and increasing awareness of mental health issues contributing to market expansion in these regions.

Component Analysis

The AI in Mental Health market is segmented by component into s

As of August 2024, more than half of Gen Z women and ** percent of Gen Z men surveyed worldwide believed that mental health was the biggest health problem in their country. The gender gap was quite pronounced across all generations, but was the largest among the Gen Z population.

BackgroundA recent report by the Institute for Health Metrics and Evaluation (IHME) highlights that mental health receives little attention despite being a major cause of disease burden. This paper extends previous assessments of development assistance for mental health (DAMH) in two significant ways; first by contrasting DAMH against that for other disease categories, and second by benchmarking allocated development assistance against the core disease burden metric (disability-adjusted life year) as estimated by the Global Burden of Disease Studies.MethodsIn order to track DAH, IHME collates information from audited financial records, project level data, and budget information from the primary global health channels. The diverse set of data were standardised and put into a single inflation adjusted currency (2015 US dollars) and each dollar disbursed was assigned up to one health focus areas from 1990 through 2015. We tied these health financing estimates to disease burden estimates (DALYs) produced by the Global Burden of Disease 2015 Study to calculated a standardised measure across health focus areas—development assistance for health (in US Dollars) per DALY.FindingsDAMH increased from USD 18 million in 1995 to USD 132 million in 2015, which equates to 0.4% of total DAH in 2015. Over 1990 to 2015, private philanthropy was the most significant source (USD 435 million, 30% of DAMH), while the United States government provided USD 270 million of total DAMH. South and Southeast Asia received the largest proportion of funding for mental health in 2013 (34%). DAMH available per DALY in 2013 ranged from USD 0.27 in East Asia and the Pacific to USD 1.18 in the Middle East and North Africa. HIV/AIDS received the largest ratio of funds to burden—approximately USD150 per DALY in 2013. Mental and substance use disorders and its broader category of non-communicable disease received less than USD1 of DAH per DALY.InterpretationCombining estimates of disease burden and development assistance for health provides a valuable perspective on DAH resource allocation. The findings from this research point to several patterns of unproportioned distribution of DAH, none more apparent than the low levels of international investment in non-communicable diseases, and in particular, mental health. However, burden of disease estimates are only one input by which DAH should be determined.

The global mental health apps market size is forecast to expand steadily from USD 8.04 billion in 2025 to USD 33.1 billion by 2035, at a CAGR of 15.2%. Top players shaping the industry include Calm, Headspace, BetterHelp, Talkspace, Mindstrong, recognized for their significant market presence.

According to Cognitive Market Research, the global Mental Health Technology market size was USD 6824.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 15.80% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 2729.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.0% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 2047.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 1569.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.8% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 341.23 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.2% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 136.49 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.5% from 2024 to 2031.
The clinical is the fastest growing segment of the Mental Health Technology industry

Market Dynamics of Mental Health Technology Market

Key Drivers for Mental Health Technology Market

Rising Mental Health Awareness to Boost Market Growth

The growing attention to intellectual health problems has notably boosted the demand for available and low-cost mental healthcare solutions. As society becomes more open approximately discussing intellectual health situations, people are looking for powerful treatments and assistance systems. This shift is driving improvements in teletherapy, online resources, and network-based total programs, making mental health care more accessible for diverse populations. Additionally, employers and academic establishments are recognizing the importance of mental well being, integrating mental fitness offerings into their offerings. This heightened awareness is fostering a way of life of expertise and assistance, ultimately leading to advanced intellectual health results for lots of people.

Expansion of Technological Advancement to Drive Market Growth

Advancements in technology, including artificial intelligence (AI), systems gaining knowledge of, and virtual reality (VR), have revolutionized the panorama of intellectual fitness care. AI and device studying are being utilized to investigate sizable quantities of data, offering customized remedy plans and predictive analytics to enhance affected person consequences. Meanwhile, VR offers immersive healing studies, allowing people to confront and control anxiety, phobias, and PTSD in controlled environments. These revolutionary tools not only give growth accessibility to mental fitness sources but also interaction with customers in new methods, making therapy greater interactive and effective. This technological integration is paving the manner for more green and tailored intellectual fitness answers.

Restraint Factor for the Mental Health Technology Market

Data Privacy and Security Concerns, will Limit Market Growth

The growing use of Mental Health Technology increases good-sized concerns regarding information privacy and security, as this equipment frequently involves the collection and storage of sensitive private facts. Patients might also fear that their info, consisting of mental fitness histories and remedy plans, could be exposed via records breaches or mishandling with the aid of carriers. Additionally, the absence of more stringent regulations in a few regions can result in the unauthorized right of entry to personal statistics. Ensuring robust encryption, obvious records managing practices and compliance with privacy laws is critical to constructing, considering, and protecting the confidentiality of individuals looking for mental health guidance through the era.

Impact of Covid-19 on the Mental Health Technology Market

The COVID-19 pandemic considerably expanded the boom of the Mental Health Technology marketplace as individuals confronted multiplied stress, tension, and isolation. With traditional in-person offerings disrupted, teletherapy and mental health apps surged in reputation, offering handy options for assist. This shift highlighted the need for modern solutions to deal with intellectual fitness worries, driving investments in virtual...

Global mental health [GMH] scholarship and practice has typically focused on the unmet needs and barriers to mental health in communities, developing biomedical and psychosocial interventions for integration into formal health care platforms in response. In this article, we analyse four diverse settings to disrupt the emphasises on health system weaknesses, treatment gaps and barriers which can perpetuate harmful hierarchies and colonial and medical assumptions, or a ‘deficit model’. We draw on the experiential knowledge of community mental health practitioners and researchers working in Ghana, India, the Occupied Palestinian Territory and South Africa to describe key assets existing in ‘informal’ community mental health care systems and how these are shaped by socio-political contexts. These qualitative case studies emerged from an online mutual learning process convened between 39 academic and community-based collaborators working in 24 countries who interrogated key tenets to inform a social paradigm for global mental health. Bringing together diverse expertise gained from professional practice and research, our sub-group explored the role of Community Mental Health Systems in GMH through comparative country case studies describing the features of community care beyond the health and social care system. We found that the socio-political health determinants of global economic structures in all four countries exert significant influence on local community health systems. We identified that key assets across sites included: family and community care, and support from non-profit organisations and religious and faith-based organisations. Strengthening community assets may promote reciprocal relationships between the formal and informal sectors, providing resources for support and training for communities while communities collaborate in the design and delivery of interventions rooted in localised expertise. This paper highlights the value of informal care, the unique social structures of each local context, and resources within local communities as key existing assets for mental health.

Abstract

Explore global statistics on a subject that claims 800,000 lives each year.

About this dataset

Context

Suicide is a major cause of death in the world, claiming around 800,000 lives each year. It is ranked as the 14th leading cause of death worldwide as of 2017 and on average men are twice as likely to fall victim to it. It also one of the leading causes of death on young people and older people are at a higher risk as well. Source

Notes

This dataset contains data from 200+ countries on the topic of suicide and mental health infrastructure. It was created by extracting the latest data from WHO and combining it into a single dataset. Variables available range from Country, Sex, Mental health infrastructure and personnel and finally Suicide Rate (amount of suicides per 100k people). Note that the suicide rate is age-standardized, as to not bias comparisons between countries with different age compositions.

How to use

• Explore Suicide rates and their associated trends, as well as the effects of infrastructure and personnel on the suicide rates.
• Forecast suicide rates

Acknowledgements

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

Citation

@misc{Global Health Observatory data repository, title={Mental Health}, url={https://apps.who.int/gho/data/node.main.MENTALHEALTH?lang=en}, journal={WHO} }

License

CC BY NC SA IGO 3.0

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The global AI in Mental Health market is experiencing exponential growth, projected to expand from $733.7 million in 2021 to over $9.7 billion by 2033, driven by a remarkable CAGR of 24.1%. This surge is fueled by increasing mental health awareness, the urgent need for accessible care, and significant advancements in artificial intelligence, machine learning, and natural language processing. AI solutions, including chatbots, predictive analytics tools, and personalized therapy platforms, are becoming integral to modern mental healthcare. They offer scalable, confidential, and cost-effective support, helping to bridge the gap in traditional services. North America currently dominates the market, but the Asia Pacific region is emerging as a key growth hub due to its large population and rapid technological adoption. Despite the promising outlook, challenges related to data privacy, algorithmic bias, and regulatory hurdles must be addressed to ensure ethical and effective implementation.

Key strategic insights from our comprehensive analysis reveal:

  North America's Dominance and High-Growth Frontiers: North America, particularly the U.S., holds the largest market share due to high technology adoption and robust venture capital funding. However, the Asia Pacific region, led by China and India, is projected to witness the fastest growth, driven by a massive user base and increasing mobile health penetration.
  The Imperative of Ethical AI and Data Privacy: As AI systems handle highly sensitive personal data, building user trust is paramount. Companies must prioritize robust data security, transparency in algorithmic decision-making, and compliance with regulations like HIPAA and GDPR to mitigate risks of bias and privacy breaches.
  Personalization is the Future: The market is shifting from one-size-fits-all solutions to hyper-personalized care. AI's ability to analyze individual data from wearables, journals, and conversations allows for tailored interventions, predictive risk assessments, and dynamic treatment adjustments, significantly enhancing therapeutic outcomes.


Global Market Overview & Dynamics of AI in Mental Health Market Analysis
The global market for AI in Mental Health is on a steep upward trajectory, driven by a convergence of rising mental health concerns and technological innovation. AI is revolutionizing mental healthcare by providing accessible, 24/7 support through applications like chatbots, virtual therapists, and predictive analytics platforms for early diagnosis. This technology helps to overcome traditional barriers such as stigma, cost, and a shortage of mental health professionals. While North America leads in adoption, emerging economies are quickly catching up, signaling a global shift towards technology-assisted mental wellness and care delivery.

Global AI in Mental Health Market Drivers

  Increasing Prevalence of Mental Health Disorders: A growing global population is affected by mental health conditions like anxiety and depression, creating a massive demand for scalable and accessible solutions that AI technologies are uniquely positioned to provide.
  Advancements in AI and Machine Learning: Continuous improvements in Natural Language Processing (NLP), affective computing, and predictive analytics enable the development of more sophisticated and effective AI-powered mental health tools, from empathetic chatbots to early-warning systems.
  Growing Accessibility and Reduced Stigma: AI-driven platforms offer a confidential and non-judgmental space for individuals to seek help, encouraging early intervention. The convenience of accessing support via smartphones is breaking down long-standing barriers to mental healthcare.


Global AI in Mental Health Market Trends

  Integration with Wearable Technology: AI platforms are increasingly integrating with wearables (smartwatches, fitness trackers) to gather physiological data like heart rate and sleep patterns, enabling real-time monitoring and more accurate assessment of an individual's mental state.
  Rise of Hyper-Personalized Interventions: The market is moving towards highly personalized mental health support. AI algorithms analyze user data to deliver customized content, coping strategies, and therapeutic exercises tailored to individual needs and progress.
  Focus on Predictive Analytics for Early Detection: Companies ar...