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

Splash banner

Photo by Fernando on Unsplash

Splash icon

Icon by photo3idea_studio available on Flaticon.

More Datasets

Effect of suicide rates on life expectancy dataset

Abstract
In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy.
The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

Introduction Suicide is still one of the world's most important public health issues, with the World Health Organization (WHO) claiming that over 700,000 people die by suicide annually. Suicide is one of the main causes of death, with far-reaching consequences for people, families, and society. Understanding the global patterns and trends in suicide rates is critical for creating effective prevention methods and providing the required support to at-risk individuals. The purpose of this report is to visualize global data on suicides using the WHO dataset (who_suicide_statistics.csv). This dataset has statistics on the number of suicides in various countries, years, age categories, and sexes. By analyzing this data, it will guide us to learn about demographic and temporal patterns of suicide, show high-risk groups, and highlight regions facing significant challenges. The visualizations will employ various techniques such as graphs, charts, and maps to effectively convey the information and guide the viewer through the findings. Through these visualizations and insights, I suggested key points and recommendations needed to minimize suicide incidents in future. Description of the Dataset The dataset (who_suicide_statistics.csv) has extensive data on global suicide statistics collected by the World Health Organization. This dataset is an invaluable resource for analyzing the patterns and trends in suicide rates across countries, years, age groups, and genders. Below is a detailed description of the columns in the dataset and the kind of information each one provides. Columns in the Dataset • country: Description: The name of the country where the data was collected. Type: Categorical Example Values: 'United States', 'Japan', 'Germany' • year: Description: The year the data was recorded. Type: Numerical Example Values: 2000, 2005, 2010 - age: Description: The age group of the individuals whose suicide data is recorded. Type: Categorical Example Values: '15-24', '25-34', '35-44', '45-54', '55-64', '65-74', '75+' • sex: Description: The sex of the individuals whose suicide data is recorded. Type: Categorical Example Values: 'male', 'female' • suicide_no: Description: The number of suicide cases recorded for the specified country, year, age and sex. Type: Numerical Example Values: 15, 42, 108 • population: Description: The population of the specified age group and sex in the country for that year. Type: Numerical Example Values: 345633, 785042, 3356435 Additional Information • Suicide Rate Calculation: Using the suicide_no and population columns, we can calculate the suicide rate per 100,000 population, which normalizes the data and allows for fair comparisons across different countries and demographic groups. Formula: suicides_rate = (suicide_no / population) * 100000

Number of suicides and suicide rates by sex and age in England and Wales. Includes information on conclusion type, the proportion of suicides by method, and the median registration delay.

Data on death rates for suicide, by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. SOURCE: NCHS, National Vital Statistics System (NVSS); Grove RD, Hetzel AM. Vital statistics rates in the United States, 1940–1960. National Center for Health Statistics. 1968; numerator data from NVSS annual public-use Mortality Files; denominator data from U.S. Census Bureau national population estimates; and Murphy SL, Xu JQ, Kochanek KD, Arias E, Tejada-Vera B. Deaths: Final data for 2018. National Vital Statistics Reports; vol 69 no 13. Hyattsville, MD: National Center for Health Statistics. 2021. Available from: https://www.cdc.gov/nchs/products/nvsr.htm. For more information on the National Vital Statistics System, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

Impact of Social Media on Suicide Rates: Produced Results

Overview

This dataset explores the impact of social media usage on suicide rates, presenting an analysis based on social media platform data and WHO suicide rate statistics. It is an insightful resource for researchers, data scientists, and analysts looking to understand the correlation between increased social media activity and suicide rates across different regions and demographics.

Content

The dataset includes the following key sources:

WHO Suicide Rate Data (SDGSUICIDE): Retrieved from WHO data export, which tracks global suicide rates. Social Media Usage Data: Information from major social media platforms, sourced from Kaggle, supplemented with data from:

Facebook: Statista

Twitter: Twitter Investor Relations

Instagram: Facebook Investor Relations

Acknowledgements

We would like to acknowledge:

World Health Organization (WHO): For providing global suicide rate data, accessible under their data policy (WHO Data Policy). Kaggle Dataset Contributors: For social media usage data that played a crucial role in the analysis.

Usage

This dataset is useful for studying the potential social factors contributing to suicide rates, especially the role of social media. Analysts can explore correlations using time-series analysis, regression models, or other statistical tools to derive meaningful insights. Please ensure compliance with the Creative Commons Attribution Non-Commercial Share Alike 4.0 International License (CC BY-NC-SA 4.0).

Data Files

Impact-of-social-media-on-suicide-rates-results-1.1.0.zip (90.9 kB) Contains processed results and supplementary data.

Citations

If you use this dataset in your work, please cite:

Martin Winkler. (2021). Impact of social media on suicide rates: produced results (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4701587 https://zenodo.org/records/4701587

License

This dataset is released under the Creative Commons Attribution Non-Commercial Share Alike 4.0 International (CC BY-NC-SA 4.0) license. You are free to share and adapt the material, provided proper attribution is given, it's not used for commercial purposes, and any derivatives are distributed under the same license.

Columns

Year: The year of the recorded data. Sex: Demographic indicator (e.g., male, female). Suicide Rate % Change Since 2010: Percentage change in suicide rates compared to the year 2010. Twitter User Count % Change Since 2010: Percentage change in Twitter user counts compared to the year 2010. Facebook User Count % Change Since 2010: Percentage change in Facebook user counts compared to the year 2010.

Data Bins

The dataset includes categorized data ranges, allowing for analysis of trends within specified intervals. For example, ranges for suicide rates, Twitter user counts, and Facebook user counts are represented in bins for better granularity.

Count Summary

The dataset summarizes counts for various intervals, enabling researchers to identify trends and patterns over time, highlighting periods of significant change or stability in both suicide rates and social media usage.

Use Cases

This dataset can be used for:

Statistical analysis to understand correlations between social media usage and mental health outcomes. Academic research focused on public health, psychology, or sociology. Policy-making discussions aimed at addressing mental health concerns linked to social media.

Cautions

The dataset contains sensitive information regarding suicide rates. Users should handle this data with care and sensitivity, considering ethical implications when presenting findings.

Annual update of suicide deaths information (numbers and rates by sex), analysed at Scotland, NHS board and LA level and by deprivation decile at Scotland level. Source agency: ISD Scotland (part of NHS National Services Scotland) Designation: Official Statistics not designated as National Statistics Language: English Alternative title: Suicide Statistics

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 ...

This table contains the number of victims of suicide arranged by marital status, method, motives, age and sex. They represent the number deaths by suicide in the resident population of the Netherlands.

The figures in this table are equal to the suicide figures in the causes of death statistics, because they are based on the same files. The causes of death statistics do not contain information on the motive of suicide. For the years 1950-1995, this information is obtained from a historical data file on suicides. For the years 1996-now the motive is tasks from the external causes of death. Before the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), i.e. for the years 1950-1978, it was not possible to code “jumping in front of train/metro”. For these years 1950-1978 “jumping in front of train/metro” has been left empty, and it has been counted in the group “other method”.

Relative figures have been calculated per 100000 of the corresponding population group. The figures are calculated based on the average population of the corresponding year.

Data available from: 1950

Status of the figures: The figures up to and including 2022 are final.

Changes as of January 25th 2024: The provisional figures for 2022 have been made final unchanged.

Changes as of August 29th 2023: The provisional figures for 2022 have been added. Some final figures of 2021 were incorrect and have been revised. A small adjustment was made in the number of deceased women from 60 to 69 years.

When will new figures be published: In the third quarter of 2024 the provisional figures for 2023 will be published.

Crude death rate from suicide and intentional self-harm per 100 000 people, by age group. Suicide registration methods vary between countries and over time. Figures do not include deaths from events of undetermined intent (part of which should be considered as suicides) and attempted suicides which did not result in death.

This dataset is about countries per year in Mexico. It has 64 rows. It features 3 columns: country, and suicide mortality rate.

The included dataset contains 10,000 synthetic Veteran patient records generated by Synthea. The scope of the data includes over 500 clinical concepts across 90 disease modules, as well as additional social determinants of health (SDoH) data elements that are not traditionally tracked in electronic health records. Each synthetic patient conceptually represents one Veteran in the existing US population; each Veteran has a name, sociodemographic profile, a series of documented clinical encounters and diagnoses, as well as associated cost and payer data. To learn more about Synthea, please visit the Synthea wiki at https://github.com/synthetichealth/synthea/wiki. To find a description of how this dataset is organized by data type, please visit the Synthea CSV File Data Dictionary at https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary.The included dataset contains 10,000 synthetic Veteran patient records generated by Synthea. The scope of the data includes over 500 clinical concepts across 90 disease modules, as well as additional social determinants of health (SDoH) data elements that are not traditionally tracked in electronic health records. Each synthetic patient conceptually represents one Veteran in the existing US population; each Veteran has a name, sociodemographic profile, a series of documented clinical encounters and diagnoses, as well as associated cost and payer data. To learn more about Synthea, please visit the Synthea wiki at https://github.com/synthetichealth/synthea/wiki. To find a description of how this dataset is organized by data type, please visit the Synthea CSV File Data Dictionary at https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary.

Provisional rate and number of suicide deaths registered in England per quarter. Includes 2001 to 2023 registrations and provisional data for Quarter 1 (Jan to Mar) to Quarter 4 (Oct to Dec) 2024. These are official statistics in development.

Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.

This dataset shows the suicide rates for just over 100 countries. The data is compiled from the the World Health Organization from 2008 in which a country's rank is determined by its total rate deaths officially recorded as suicides. Rates are expressed as per 100,000 of population. Note - year is not consistant for all entries, please refer to the year column to determine what year the data represents. Data sourced from WHO website - Mental health. World Health Organization. 2009. http://www.who.int/mental_health/prevention/suicide/country_reports/en/index.html. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-01-31 and migrated to Edinburgh DataShare on 2017-02-21.

This report uses 2009 to 2014 NSDUH data, and 1999 and 2009 to 2014 data from the National Vital Statistics System to examine the percentages of suicidal thoughts and behaviors versus suicidal death rates among the middle-aged.

Suicide & Self-Inflicted Injury death rates by county, all races (includes Hispanic/Latino), all sexes, all ages, 2019-2023. Death data were provided by the National Vital Statistics System. Death rates (deaths per 100,000 population per year) are age-adjusted to the 2000 US standard population (20 age groups: <1, 1-4, 5-9, ... , 80-84, 85-89, 90+). Rates calculated using SEER*Stat. Population counts for denominators are based on Census populations as modified by the National Cancer Institute. The US Population Data File is used for mortality data. The Average Annual Percent Change is based onthe APCs calculated by the Joinpoint Regression Program (Version 4.9.0.0). Due to data availability issues, the time period used in the calculation of the joinpoint regression model may differ for selected counties. Counties with a (3) after their name may have their joinpoint regresssion model calculated using a different time period due to data availability issues.

Over *** thousand deaths due to suicides were recorded in India in 2022. Furthermore, majority of suicides were reported in the state of Tamil Nadu, followed by Rajasthan. The number of suicides that year had increased from the previous year. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders. Depressive disorders and suicide As of 2015, over ****** million people worldwide suffered from some kind of depressive disorder. Furthermore, over ** percent of the total population in India suffer from different forms of mental disorders as of 2017. There exists a positive correlation between the number of suicide mortality rates and people with select mental disorders as opposed to those without. Risk factors for mental disorders Every ******* person in India suffers from some form of mental disorder. Today, depressive disorders are regarded as the leading contributor not only to disease burden and morbidity worldwide, but even suicide if not addressed. In 2022, the leading cause for suicide deaths in India was due to family problems. The second leading cause was due to illness. Some of the risk factors, relative to developing mental disorders including depressive and anxiety disorders, include bullying victimization, poverty, unemployment, childhood sexual abuse and intimate partner violence.

Time series data for the statistic Suicide mortality rate, male (per 100,000 male population) and country Costa Rica. Indicator Definition:Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).The indicator "Suicide mortality rate, male (per 100,000 male population)" stands at 13.45 as of 12/31/2021, the highest value at least since 12/31/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 36.13 percent compared to the value the year prior.The 1 year change in percent is 36.13.The 3 year change in percent is 11.07.The 5 year change in percent is 16.96.The 10 year change in percent is 23.74.The Serie's long term average value is 10.83. It's latest available value, on 12/31/2021, is 24.15 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2001, to it's latest available value, on 12/31/2021, is +61.46%.The Serie's change in percent from it's maximum value, on 12/31/2021, to it's latest available value, on 12/31/2021, is 0.0%.

As the tagline of ‘American Association of Suicidology’ says I strongly believe that suicide prevention is everyone’s business. The act of ending one’s own life stating the reasons to be depression, alcoholism or any other mental disorders for that matter is not a considerable idea keeping in mind that anything can be overcome with reliable help and lifestyle. We can choose to stand together in the face of a society which may often feel like a lonely and disconnected place, and we can choose to make a difference by making lives more livable for those who struggle to cope. Through this project, I am hoping to identify the trends of suicidal rates by country, gender, age and ethnicity. And relate the trends to the possible reasons that leads to the drastic decision, which might help us to curb the thought in the very beginning.

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Data on suicides is deficient for two reasons, first of all, there is a problem with the frequency and reliability of vital registration data in many countries – an issue that undermine the quality of mortality estimates in general, not just suicide. Secondly, there are problems with the accuracy of the official figures made available, since suicide registration is a complicated process involving several responsible authorities with medical and legal concerns. Moreover, the illegality of suicidal behavior in some countries contributes to under reporting and misclassification. I was lucky enough to obtain enough data from different reliable resources. I will be starting off the project with the most reliable datasets available for us on suicide.

•World Health Organization (WHO) dataset which contains entity wise suicide rates, crude suicide rates per gender and country which are age standardized which has a geographical coverage of 198 countries. The time spanning from 1950-2011.

•Samaritans statistics report 2017 including data for 2013-2015, in order to reduce the time, it takes to register deaths, the maximum time between a death and registration is eight days.

•American Association of Suicidology facts and statistics which are categorized by age, gender, region and ethnicity.

Inspiration: To visualize the trends and patterns by merging different datasets available regarding the subject matter from different organizations, deriving the major causes for the drastic stride. And also observing the changes in patterns over the years by country, sex and ethnicity

Understanding the data: It is always tricky to understand the suicide statistics as they may not be so straight forward as they appear to be. Generally, the rate is per 100,000. It is done this way to adjust the underlying population size. ‘Age-standardized’ rates have been standardized to the world population to increase the confidence while making the comparisons. On the other hand, ‘Crude rates’ have not been standardized like the prior, so they are just the basic calculation of number of deaths divided by the population (x100,000). The size of the population and specific cohort is also to be taken into account as smaller groups often produce less reliable rates per 100,000. When examining the suicide trends over a period of time it is also important to look over a relatively long period. Increases and decreases for a year at a time should not be considered in isolation.