In 2021, the highest suicide rate in the U.S. for working males was among musicians, singers, and related workers with around 139 suicide deaths per 100,000 population. This statistic shows the suicide rate among working male civilians in the U.S. in 2021, by detailed occupation group.

In the United States, the suicide rate among males working in construction and extraction was **** per 100,000 population. In contrast, the suicide rate among males working in education, training, and library occupations was **** per 100,000 population. This statistic shows the suicide rate among working male civilians in the U.S. in 2021, by occupation.

State-wise Suicides by Profession in India

Investigating National Level Trends and Patterns

By Rajanand Ilangovan [source]

About this dataset

This dataset contains data on suicides in India by state, year, profession and gender. Through this dataset, we can gain an understanding of the factors that influence suicide rates across different states, professions and genders. By examining this data we can better understand how to reduce these tragedies in India which are of great concern to citizens, families and the government alike. The columns include the State in India where the suicides occurred; Year in which the suicides occurred; Type_code of the profession of the person who committed suicide; Gender of the person who committed suicide; Age_group of such person; and Total number of suicides for a given State-Year-Typecode-Type-Gender-Agegroup combination. With this insightful data set at our disposal, we can gather valuable insights into why certain types people are more likely to take their own lives than others and look for solutions which would have meaningful implications for society at large

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How to use the dataset

This dataset contains information about the number of suicides in India by state, year, type of profession, gender, and age group. It is an important resource for understanding the trends and patterns in suicides in India. This guide will explain how to use this dataset to gain insights into suicide rates across India.

Exploring the Data

The first step to exploring this data is to examine its structure. There are 8 columns that contain information about each suicide: State (the Indian state where the suicide occurred), Year (the year of occurrence), Type_code (the code for the type of profession or activity engaged in at time of death), Gender (male or female), Age_group (groups based on age-range), Total (total number of suicides for given state/year/type_code/type/gender/age group). In addition, there are other useful descriptive stats such as aggregate totals by year and aggregate totals by state as well as null values indicating missing data points that should be accounted for during analysis.

Analyzing Trends

Once you have a good understanding of the data structure, you can begin analyzing it for patterns and trends. You can look at overall trends across all states or focus on individual states to see if certain decades witness higher suicide rates than others due to specific socioeconomic factors within those states. Similarly, you may identify distinct patterns when examining activity related causes across genders or age groups both generally and within individual states – e.g., self-immolation witnessed significantly more amongst females than males within a given decade etc.. Alternatively you could find out what types occupations had higher incidences during certain years thus ruling out otherwise unlikely ways people chose ‘suicide’!

Finally it may also be useful window shop; use this data set as research material before further framing hypotheses related too changes over time i historical events that directly caused shifts in societal norms like wars / pandemics etc.. And then corroborate results against timelines ascertained through secondary sources such newspapers / anecdotal reports or primary sources like census records summaries published by official agencies etc.. As a index towards which other activities were attempted within scope!

Overall these analyses can help policy makers understand better how best resources can be allocated while developing interventions aimed at reducing suicidal tendencies amongst different demographic segments including males & females , adolescents & elderly people respectively!

Research Ideas

• Analyzing trends in suicides across different states in India over time to identify regional disparities and support the implementation of targeted policies and interventions.
• Mapping out the suicide hotspots across age groups, genders, and profession types to better target prevention efforts in those areas.
• Examining differences by profession type among populations with higher suicide rates in order to suggest preventative measures or resources tailored specifically for such populations

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: Suicides_in_India.csv | Column name | Description ...

In 2021, the highest suicide rate in the U.S. for working females was among artists and related workers with 45.3 deaths per 100,000 population, followed by a rate of around 39 per 100,000 population for female construction laborers. This statistic shows the suicide rate among working female civilians in the U.S. in 2021, by detailed occupation group.

In 2021, construction and extraction was the occupation with the highest suicide rate among U.S. females with **** suicide deaths per 100,000 population. At that time, the protective services occupation had a female suicide rate of **** per 100,000 population. This statistic shows the suicide rate among working female civilians in the U.S. in 2021, by occupation.

Occupational suicide analysis, providing numbers of deaths and the standardised mortality ratio for people aged 20 to 64 years, deaths registered in England.

In 2022, more than ***** students, homemakers, and unemployed people committed suicide in South Korea, representing the highest share of all occupational groups at about ** percent. Suicide remains the leading cause of death among young people.

In 2022, over ** thousand daily wage earners committed suicide in India. Followed by over ** thousand home makers who committed suicide. Family problem was the leading cause of suicides in the country that year.

With approximately *******cases, unemployed people in Japan showed the highest number of committed suicides in 2024. That same year, over ******employed people in the country committed suicide.

The Distribution of Confidence Ratings by Profession and Gender.

Occupational suicide analysis which provides the number of deaths and the standardised mortality ratio for people aged 20 to 64 years in Wales.

AI Suicide Risk Stratification Market Outlook

According to our latest research, the global AI Suicide Risk Stratification market size reached USD 1.18 billion in 2024, driven by the increasing adoption of artificial intelligence in mental health care and the urgent need for advanced predictive analytics in suicide prevention. The market is experiencing robust growth, registering a CAGR of 18.9% from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 6.15 billion, reflecting significant investments in healthcare digitization, growing awareness of mental health issues, and the integration of AI-powered solutions across various healthcare settings. The expanding role of AI in early risk detection and intervention is a primary growth catalyst, as per our comprehensive analysis.

One of the key growth factors propelling the AI Suicide Risk Stratification market is the escalating global mental health crisis, marked by rising suicide rates and the pressing need for timely intervention. Traditional methods of suicide risk assessment often rely on subjective clinical judgment, which can be inconsistent and prone to bias. AI-driven stratification tools, leveraging machine learning and natural language processing, enable healthcare professionals to identify at-risk individuals with higher accuracy and efficiency. These platforms analyze vast datasets, including electronic health records, social media activity, and behavioral patterns, to generate actionable insights. As governments and healthcare organizations increasingly prioritize suicide prevention strategies, the demand for advanced, data-driven risk assessment solutions is expected to surge, further accelerating market expansion.

Another substantial driver for the AI Suicide Risk Stratification market is the rapid advancement in AI technology and its integration into healthcare systems. Recent breakthroughs in deep learning, predictive analytics, and big data have revolutionized the way mental health professionals approach suicide risk assessment. AI-based solutions can continuously learn and adapt, offering real-time risk predictions and personalized intervention recommendations. This technological evolution is complemented by the growing availability of high-quality mental health data, improved interoperability of health IT systems, and rising investments from both public and private sectors. As healthcare providers seek to enhance patient outcomes and reduce the burden of suicide, the adoption of AI-powered risk stratification tools is becoming a standard practice.

The increasing acceptance of digital health solutions and telemedicine is also fueling the growth of the AI Suicide Risk Stratification market. The COVID-19 pandemic has accelerated the shift towards remote healthcare delivery, highlighting the need for scalable and accessible mental health assessment tools. AI-driven platforms can be seamlessly integrated into telehealth services, enabling clinicians to monitor patients remotely and intervene proactively. Furthermore, the growing emphasis on preventive care and population health management is prompting healthcare systems to invest in predictive analytics for early identification of high-risk individuals. This trend is particularly pronounced in developed regions, where digital infrastructure and regulatory support for AI in healthcare are more advanced.

From a regional perspective, North America currently dominates the AI Suicide Risk Stratification market, accounting for the largest share due to its advanced healthcare infrastructure, high awareness levels, and strong presence of leading AI solution providers. However, the Asia Pacific region is anticipated to witness the fastest growth over the forecast period, driven by increasing mental health challenges, government initiatives to improve healthcare access, and rapid digital transformation in emerging economies. Europe is also a significant contributor, benefiting from supportive regulatory frameworks and rising investments in mental health research. Meanwhile, Latin America and the Middle East & Africa are gradually embracing AI-driven mental health solutions, although market penetration remains relatively lower due to infrastructural and economic constraints.

Component Analysis

The Component segment of the AI Suicide Risk Stratification market is categorized into Software, Hardware, and Services, each playing a pivotal role in the ecosystem. Softwa

‘Suicide’ and ‘Non-suicide’ responses by condition.

In 2023, the rate of suicides among both men and women in England was at their highest recorded rates. The rate of among males was 17.4 per 100,000 population and among females it was 5.7 per 100,000. Recent years have seen an increase again for both genders, however, the rate of suicide for men has remained significantly higher than for women. Individuals seeking help for mental health issuesIn Great Britain, almost 70 percent have never visited a mental health professional, while eighteen percent consult with one at least once a year. Additionally, almost 60 percent of those with a psychiatric condition do not take any medication to control their condition. Mental health of young peopleThe COVID-19 pandemic had a huge impact of the mental health of many people, particularly young people. The share of all adults reporting to having experienced symptoms of depression doubled during the pandemic compared to before. Although for those in the age group 16 to 39 years, depression prevalence tripled. Among young people that had mental health concerns prior to the pandemic, a significant majority of those surveyed reported that their life had become worse due to the impact of the pandemic and subsequent restrictions.

Confidence ratings by condition and by response.

This statistic indicates the number of suicides per 100,000 employed men in France from 1997 to 2002, according to the socio-professional class. During this period, the suicide rate among workers was about ** per 100,000.

In 2024, around ***** people in Japan committed suicide due to problems related to their working situation in Japan. “Karojisatsu," or suicide brought on by excessive work or stress at work, is a well-known phenomenon in Japan.

Numbers and rates of CB-related online searches and publicized CB suicides across 31 provinces and provincial-level municipalities in mainland China (2011–2014).

In 2024, over 20,300 people in Japan died of suicide. The overall number of suicides decreased after the unexpected upward trend, likely connected to the COVID-19 pandemic. Why Japanese men are more likely to die by suicide  When looking at suicide numbers by gender, Japanese men are more likely to commit suicide compared to women. Attitudes on traditional gender roles in Japan may have shifted in recent decades, but social change has since been slow. Men are still expected to focus on their careers and provide for the family. Hence, economic slumps are typically reflected in rising suicide figures among men, as failure to fulfill social expectations can lead to mental health issues, which in turn might trigger suicidal thoughts. As an example, the suicide figures increased only for men in 2009 as a result of the global banking crisis. Suicide resulting from work-related issues is also more common among men than among women. Stress and pressure at work pose health risks It has been determined over the past few decades that one of the primary issues facing Japanese workers that leads to self-harm is exhaustion. Occupational sudden mortality, known as "karoshi (death by overwork)" is a well-known phenomenon in Japanese society. Besides physical pressure, mental stress from the employment may cause karoshi. Suicide due to occupational stress or overwork is called "karojisatsu (overwork suicide)" in Japan.