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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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

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.

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.

India IN: Suicide Mortality Rate: Male data was reported at 17.800 NA in 2016. This records a decrease from the previous number of 18.000 NA for 2015. India IN: Suicide Mortality Rate: Male data is updated yearly, averaging 18.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 18.600 NA in 2000 and a record low of 17.700 NA in 2010. India IN: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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 contains annual suicide rates from 2000 to 2019 for various countries and regions around the world. The data was collected from the World Health Organization (WHO) Mortality Database, which provides information on deaths and mortality rates from various causes, including suicide. The dataset includes information on the country, location, year, sex, and suicide rates (with upper and lower bounds) for each year.

Variables:

ParentLocation: The name of the parent location, such as a region or subregion. Location: The name of the location, such as a country or territory. Period: The year the suicide rate was recorded. Sex: The gender of the individual (male or female). FactValueNumeric: The age-standardized suicide rate (per 100,000 population) for the given sex, age, and year. FactValueNumericLow: The lower bound of the confidence interval for the suicide rate. FactValueNumericHigh: The upper bound of the confidence interval for the suicide rate.

Potential uses:

This dataset can be used to explore patterns and trends in suicide rates over time and across different regions of the world. Researchers and policymakers can use this data to identify risk factors and develop interventions to prevent suicides. The dataset can also be used to investigate the impact of economic, social, and cultural factors on suicide rates.

Caveats:

It is important to note that suicide is a sensitive and complex issue, and the data in this dataset may be subject to reporting biases, cultural differences in suicide rates, and other limitations. Additionally, the dataset does not include information on the causes or circumstances surrounding the suicides. Therefore, any analyses based on this dataset should be interpreted with caution and with an understanding of the limitations of the data.

Japan JP: Suicide Mortality Rate: Female data was reported at 11.400 NA in 2016. This records a decrease from the previous number of 11.800 NA for 2015. Japan JP: Suicide Mortality Rate: Female data is updated yearly, averaging 13.600 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 14.100 NA in 2010 and a record low of 11.400 NA in 2016. Japan JP: Suicide Mortality Rate: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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.

United States US: Suicide Mortality Rate: per 100,000 Population data was reported at 15.300 Number in 2016. This records an increase from the previous number of 15.000 Number for 2015. United States US: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 13.200 Number from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 15.300 Number in 2016 and a record low of 11.300 Number in 2000. United States US: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

Indonesia ID: Suicide Mortality Rate: Male data was reported at 4.800 NA in 2016. This stayed constant from the previous number of 4.800 NA for 2015. Indonesia ID: Suicide Mortality Rate: Male data is updated yearly, averaging 5.100 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 5.300 NA in 2005 and a record low of 4.800 NA in 2016. Indonesia ID: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Panama PA: Suicide Mortality Rate: Male data was reported at 7.400 NA in 2016. This records an increase from the previous number of 6.500 NA for 2015. Panama PA: Suicide Mortality Rate: Male data is updated yearly, averaging 8.600 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 10.700 NA in 2005 and a record low of 6.500 NA in 2015. Panama PA: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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 dataset is about countries per year in Malaysia. It has 64 rows. It features 4 columns: country, suicide mortality rate, and death rate.

this project to realized in Power Bi:

Suicide rates vary around the world Suicide rates vary widely between countries. The map shows this.

For some countries in Southern Africa and Eastern Europe, the estimated rates of suicide are high, with over 15 annual deaths per 100,000 people.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F0ad0b0509ea3e47fa4fef0c0df5a7650%2Fgraph1.jpg?generation=1709580346519280&alt=media" alt="">

Meanwhile for other countries in Europe, South America and Asia, the estimated rates of suicide are lower, with under 10 annual deaths per 100,000 people.

The wide range in suicide rates around the world is likely the result of many factors. This includes differences in underlying mental health and treatment, personal and financial stress, restrictions on the means of suicide, recognition and awareness of suicide, and other factors.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F4852c4fa7dd9966750113f508fa189ea%2Fgraph2_page-0001.jpg?generation=1709580403955230&alt=media" alt="">

WHAT YOU SHOULD KNOW ABOUT THIS DATA Suicide estimates come from death certificate data, using deaths that were classified under death codes for 'intentional self-harm' in the International Classification of Diseases (ICD). This includes people who had self-harmed but had not intended to die, and they may not be considered suicides by the country's particular legal definition. In many countries, deaths due to self-harm are highly underreported due to social stigma, cultural and legal concerns. Instead, these deaths are often misclassified in reported data, especially as deaths due to "events of undetermined intent", accidents, homicides, or unknown causes. To account for this, the WHO's Global Health Observatory reclassifies a proportion of deaths reported with those causes as suicides, according to the fraction that are estimated to be deaths by suicide. As a result, data on suicide rates represent a better estimate of how many people die from suicide.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F70c55821265e5e2e25f693d3bb0d6520%2Fgraph3_page-0001.jpg?generation=1709580432933739&alt=media" alt="">

Suicides may still be underestimated after this adjustment, especially if they are misclassified as other types of deaths.2 This can also be why some countries appear to have rising suicide rates, if the rates of misclassification decline.

China Suicide Mortality Rate: Female data was reported at 10.300 NA in 2016. This stayed constant from the previous number of 10.300 NA for 2015. China Suicide Mortality Rate: Female data is updated yearly, averaging 12.500 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 15.400 NA in 2000 and a record low of 10.300 NA in 2016. China Suicide Mortality Rate: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Context

In light of Suicide Awareness Month, relationships between overall health investment (as a mental health proxy) and suicide rates are a key index to watch.

We expect a positive effect of more expenditure in health topics, giving away less rates of suicides in countries worldwide.

The hope of less suicides brings us a light of answers of where to put our efforts, even though this job does not consider other relevant factors such as socioeconomical development, private and public actions, and individuals characteristics.

Content

This data was downloaded from the WHO Mortality stats published and ICD10 death classification codes, then edited for format and condensation of ICD (more than 5 million rows) in SQL (Query attached). Finally, a tableau visualization was also developed. The following links are the primary content sources: 1. https://www.who.int/data/data-collection-tools/who-mortality-database 2. https://www.who.int/data/gho/data/indicators/indicator-details/GHO/current-health-expenditure-(che)-as-percentage-of-gross-domestic-product-(gdp)-(-) 3. https://health.mo.gov/data/documentation/death/death-icd10.php

"The data available for download from this web site are official national statistics in the sense that they have been transmitted to the World Health Organization by the competent authorities of the countries concerned.

The database contains number of deaths by country, year, sex, age group and cause of death as far back from 1950. Data are included only for countries reporting data properly coded according to the International Classification of Diseases (ICD). "

Tableau Visualization

Acknowledgements

Many thanks to Ken Jee guidelines on analytics: https://www.kaggle.com/code/kenjee/kaggle-project-from-scratch/notebook#Part-1---How-to-Start-a-Kaggle-Competition.

This data contains stats about number of death due to self inflicted injuries/suicides across the world for 118 countries, across age groups ranging from 0-85+ Years for male, female and unknown gender.

It contains data for year 1950-2021, however after analysis I felt the data for recent years were incomplete. The data was collected from website of World Health Organisation, WHO holds any of the rights related to the data; check their Copyright Policies in case of commercial usages.

The dataset consists of two segment, one is Mortality Data directory which consists of collected raw data from WHO website. The directory is categorized into 3 parts for different gender which consists of multiple csv for different age groups. The other file combined_processed_data.csv is the final processed combined data of all the raw data. The notebook used for cleaning and formatting can be accessed from end of this page under related notebooks section.

Jordan JO: Suicide Mortality Rate: Female data was reported at 2.200 NA in 2016. This stayed constant from the previous number of 2.200 NA for 2015. Jordan JO: Suicide Mortality Rate: Female data is updated yearly, averaging 2.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 2.600 NA in 2000 and a record low of 2.100 NA in 2010. Jordan JO: Suicide Mortality Rate: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;