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

Download data on suicides in Massachusetts by demographics and year. This page also includes reporting on military & veteran suicide, and suicides during COVID-19.

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 taken from the external causes of death (Niet-Natuurlijke dood) file. 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 100 000 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 2023 are final.

Changes as of January 23rd 2025: The figures for 2023 are made final.

When will new figures be published: In the third quarter of 2025 the provisional figures for 2024 will be published.

Number of suicides and suicide rates, by sex and age, in England and Wales. Information on conclusion type is provided, along with the proportion of suicides by method and the median registration delay.

BackgroundAbout 1 million people worldwide commit suicide each year, and college students with suicidal ideation are at high risk of suicide. The prevalence of suicidal ideation in college students has been estimated extensively, but quantitative syntheses of overall prevalence are scarce, especially in China. Accurate estimates of prevalence are important for making public policy. In this paper, we aimed to determine the prevalence of suicidal ideation in Chinese college students.Objective and MethodsDatabases including PubMed, Web of Knowledge, Chinese Web of Knowledge, Wangfang (Chinese database) and Weipu (Chinese database) were systematically reviewed to identify articles published between 2004 to July 2013, in either English or Chinese, reporting prevalence estimates of suicidal ideation among Chinese college students. The strategy also included a secondary search of reference lists of records retrieved from databases. Then the prevalence estimates were summarized using a random effects model. The effects of moderator variables on the prevalence estimates were assessed using a meta-regression model.ResultsA total of 41 studies involving 160339 college students were identified, and the prevalence ranged from 1.24% to 26.00%. The overall pooled prevalence of suicidal ideation among Chinese college students was 10.72% (95%CI: 8.41% to 13.28%). We noted substantial heterogeneity in prevalence estimates. Subgroup analyses showed that prevalence of suicidal ideation in females is higher than in males.ConclusionsThe prevalence of suicidal ideation in Chinese college students is relatively high, although the suicide rate is lower compared with the entire society, suggesting the need for local surveys to inform the development of health services for college students.

Context

This dataset contains information on suicides which happened in India during 2015.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4208638%2Ffab2e99b439f9780daf358511060f514%2FWorld-Suicide-Prevention-Day.jpg?generation=1598114750200382&alt=media" alt="">

The singular age-old social precept of 'Lok Kya Kahenge?' (loosely translated: "What will people say?") suppresses the much-needed psychological care in India. It's high time that we understand why suicides happen and what are the reasons behind it. This dataset aims to spread awareness about suicides in India.

Content

I acquired this dataset from here. Have a look at the website.

This dataset contains 9 files in .csv format. You can find a description for each column. Let me summarize it here as well.

1. Cause-wise distribution of suicides in Central Armed Police Force (CAPF) during 2015.
2. Economic Status-wise distribution of suicides during 2015.
3. Educational Status-wise distribution of suicides during 2015.
4. Farmer or Cultivators distribution of suicides during 2015.
5. Profession-wise distribution of suicides during 2015.
6. Social status-wise distribution of suicides during 2015.
7. Cause-wise distribution of suicides during 2015.
8. Suicides by Agricultural labourers during 2015.
9. Suicides by means adopted during 2015.

Inspiration

We now have plenty of data to explore to draw some conclusions about suicides which happened in India during 2015. Let's start by answering these questions: - What are the top 5 states where Farmers' suicides occurred the most? - What's the top reason that agricultural labourers committed suicide? - Which Profession has the most suicides? What could be the reason? - How many Transgender suicides have occurred in different categories?

I hope these questions interest you in starting to explore this dataset.

Acknowledgements

I thank the Indian Government for making it public under their Open Government Data (OGD) Platform India. Please use this dataset strictly for educational purposes. Thank you.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Deaths by suicide in the city of Austin are reported to annually to Austin Public Health trough the Office of Vital Statistics. The data represents deaths by suicide within the city limits.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/HE-B-4a-Number-of-Deaths-by-Suicide/mqa2-tm7r/

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.

Historical chart and dataset showing Uganda suicide rate by year from 2000 to 2021.

Historical chart and dataset showing Singapore suicide rate by year from 2000 to 2021.

THIS DATASET WAS LAST UPDATED AT 8:11 PM EASTERN ON JULY 30

OVERVIEW

2019 had the most mass killings since at least the 1970s, according to the Associated Press/USA TODAY/Northeastern University Mass Killings Database.

In all, there were 45 mass killings, defined as when four or more people are killed excluding the perpetrator. Of those, 33 were mass shootings . This summer was especially violent, with three high-profile public mass shootings occurring in the span of just four weeks, leaving 38 killed and 66 injured.

A total of 229 people died in mass killings in 2019.

The AP's analysis found that more than 50% of the incidents were family annihilations, which is similar to prior years. Although they are far less common, the 9 public mass shootings during the year were the most deadly type of mass murder, resulting in 73 people's deaths, not including the assailants.

One-third of the offenders died at the scene of the killing or soon after, half from suicides.

About this Dataset

The Associated Press/USA TODAY/Northeastern University Mass Killings database tracks all U.S. homicides since 2006 involving four or more people killed (not including the offender) over a short period of time (24 hours) regardless of weapon, location, victim-offender relationship or motive. The database includes information on these and other characteristics concerning the incidents, offenders, and victims.

The AP/USA TODAY/Northeastern database represents the most complete tracking of mass murders by the above definition currently available. Other efforts, such as the Gun Violence Archive or Everytown for Gun Safety may include events that do not meet our criteria, but a review of these sites and others indicates that this database contains every event that matches the definition, including some not tracked by other organizations.

This data will be updated periodically and can be used as an ongoing resource to help cover these events.

Using this Dataset

To get basic counts of incidents of mass killings and mass shootings by year nationwide, use these queries:

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

Mass murder is defined as the intentional killing of four or more victims by any means within a 24-hour period, excluding the deaths of unborn children and the offender(s). The standard of four or more dead was initially set by the FBI.

This definition does not exclude cases based on method (e.g., shootings only), type or motivation (e.g., public only), victim-offender relationship (e.g., strangers only), or number of locations (e.g., one). The time frame of 24 hours was chosen to eliminate conflation with spree killers, who kill multiple victims in quick succession in different locations or incidents, and to satisfy the traditional requirement of occurring in a “single incident.”

Offenders who commit mass murder during a spree (before or after committing additional homicides) are included in the database, and all victims within seven days of the mass murder are included in the victim count. Negligent homicides related to driving under the influence or accidental fires are excluded due to the lack of offender intent. Only incidents occurring within the 50 states and Washington D.C. are considered.

Methodology

Project researchers first identified potential incidents using the Federal Bureau of Investigation’s Supplementary Homicide Reports (SHR). Homicide incidents in the SHR were flagged as potential mass murder cases if four or more victims were reported on the same record, and the type of death was murder or non-negligent manslaughter.

Cases were subsequently verified utilizing media accounts, court documents, academic journal articles, books, and local law enforcement records obtained through Freedom of Information Act (FOIA) requests. Each data point was corroborated by multiple sources, which were compiled into a single document to assess the quality of information.

In case(s) of contradiction among sources, official law enforcement or court records were used, when available, followed by the most recent media or academic source.

Case information was subsequently compared with every other known mass murder database to ensure reliability and validity. Incidents listed in the SHR that could not be independently verified were excluded from the database.

Project researchers also conducted extensive searches for incidents not reported in the SHR during the time period, utilizing internet search engines, Lexis-Nexis, and Newspapers.com. Search terms include: [number] dead, [number] killed, [number] slain, [number] murdered, [number] homicide, mass murder, mass shooting, massacre, rampage, family killing, familicide, and arson murder. Offender, victim, and location names were also directly searched when available.

This project started at USA TODAY in 2012.

Contacts

Contact AP Data Editor Justin Myers with questions, suggestions or comments about this dataset at jmyers@ap.org. The Northeastern University researcher working with AP and USA TODAY is Professor James Alan Fox, who can be reached at j.fox@northeastern.edu or 617-416-4400.

Abstract Background Suicide is considered a public health problem. Every year 840,000 people take their own lives. In military police, suicide rates are also high. Objective This study aimed to investigate occupational and social characteristics in cases of suicide of military police officers from Santa Catarina, Brazil, from 2012 to 2016. Method This is a documentary, descriptive and quantitative study, using socio-occupational information provided by the organization about suicide cases of military police officers. Results All military police officers who committed suicide are male (n = 14) and most have children (85.71%). About 85.71% of the suicide cases occurred in lower rank positions, especially in the first degree in the military hierarchy. Soldiers accounted for half of the cases (n = 7). In all cases, part of the salary was committed with loans or financial debts. Conclusion Important occupational characteristics in certain situations may become a risk factor for suicide of military police officers. Further research is needed, especially considering other important sources of information not used in this investigation.

Time references: Janke Wally ascended the throne when Kabu kingdom was 606 years Additional information: Janke Wally and many of his people committed suicide to prevent them being captives forever. b) When Janke Wally realized that he was defeated, he ordered the burning of both gun and gunpowder stores. There was a huge explosion and the entire town was set on fire, then the Fula fighters retreated and fled. After they left, the survivors went in search of others survivors and Nyima Manjang was found alive. Nyima Manjang later got married to Sheriff Mulay Bakari. They had children and among them, were Yahya, Yankuba, and Sheriff Seedy. Timbo defeated Kabu because they possessed more fighters however, despite this, the people of Kabu never fled for their lives. Instead, they stood by their ruler to the very end. It is said that the Koring clan will never fight against the Nyancho clan. Sankulleh is the ancestor of the Koring clan at Kunung Mansa Sansang. The clan members are known to be great fighters. They are related to the Nyancho clan who are descendants of Tiramakang and the Koring clan are Sunjata Keita’s descendants. Tiramakang was one of Sunjata’s war generals. Ngansumana, a member of the Nyancho clan was betrayed and killed at Bambadinka when the town of Kansala was destroyed. His sister expressed that the death of Ngansumana was not the end of the Nyancho clan. When the survivors of Kabu Kansala fled, they resettled at Bere Kolong. While in Bere Kolong they were attacked again by Timbo. Foday Baraka Darame an Islamic scholar (Marabout) in Kabu, converted Mansa Bakari to Islam. Mansa Bakari was commonly referred to as Mansa Kolli. After converting to Islam, he relocated to Sonkunda. While in Sonkunda, he was attacked by assailants and he fled. His wife Jenaba was captured and taken to a ruler named Mansa Edrissa. She gave birth to a child called Kumba Njosanne who later married Alfa Yiramang in Labe. Together they had a child named Alfa Yaya, who later gained prominence and was highly respected.

BackgroundIn Europe, men have lower rates of attempted suicide compared to women and at the same time a higher rate of completed suicides, indicating major gender differences in lethality of suicidal behaviour. The aim of this study was to analyse the extent to which these gender differences in lethality can be explained by factors such as choice of more lethal methods or lethality differences within the same suicide method or age. In addition, we explored gender differences in the intentionality of suicide attempts.Methods and FindingsMethods. Design: Epidemiological study using a combination of self-report and official data. Setting: Mental health care services in four European countries: Germany, Hungary, Ireland, and Portugal. Data basis: Completed suicides derived from official statistics for each country (767 acts, 74.4% male) and assessed suicide attempts excluding habitual intentional self-harm (8,175 acts, 43.2% male).Main Outcome Measures and Data Analysis. We collected data on suicidal acts in eight regions of four European countries participating in the EU-funded “OSPI-Europe”-project (www.ospi-europe.com). We calculated method-specific lethality using the number of completed suicides per method * 100 / (number of completed suicides per method + number of attempted suicides per method). We tested gender differences in the distribution of suicidal acts for significance by using the χ2-test for two-by-two tables. We assessed the effect sizes with phi coefficients (φ). We identified predictors of lethality with a binary logistic regression analysis. Poisson regression analysis examined the contribution of choice of methods and method-specific lethality to gender differences in the lethality of suicidal acts.Findings Main ResultsSuicidal acts (fatal and non-fatal) were 3.4 times more lethal in men than in women (lethality 13.91% (regarding 4106 suicidal acts) versus 4.05% (regarding 4836 suicidal acts)), the difference being significant for the methods hanging, jumping, moving objects, sharp objects and poisoning by substances other than drugs. Median age at time of suicidal behaviour (35–44 years) did not differ between males and females. The overall gender difference in lethality of suicidal behaviour was explained by males choosing more lethal suicide methods (odds ratio (OR) = 2.03; 95% CI = 1.65 to 2.50; p < 0.000001) and additionally, but to a lesser degree, by a higher lethality of suicidal acts for males even within the same method (OR = 1.64; 95% CI = 1.32 to 2.02; p = 0.000005). Results of a regression analysis revealed neither age nor country differences were significant predictors for gender differences in the lethality of suicidal acts. The proportion of serious suicide attempts among all non-fatal suicidal acts with known intentionality (NFSAi) was significantly higher in men (57.1%; 1,207 of 2,115 NFSAi) than in women (48.6%; 1,508 of 3,100 NFSAi) (χ2 = 35.74; p < 0.000001).Main limitations of the studyDue to restrictive data security regulations to ensure anonymity in Ireland, specific ages could not be provided because of the relatively low absolute numbers of suicide in the Irish intervention and control region. Therefore, analyses of the interaction between gender and age could only be conducted for three of the four countries. Attempted suicides were assessed for patients presenting to emergency departments or treated in hospitals. An unknown rate of attempted suicides remained undetected. This may have caused an overestimation of the lethality of certain methods. Moreover, the detection of attempted suicides and the registration of completed suicides might have differed across the four countries. Some suicides might be hidden and misclassified as undetermined deaths.ConclusionsMen more often used highly lethal methods in suicidal behaviour, but there was also a higher method-specific lethality which together explained the large gender differences in the lethality of suicidal acts. Gender differences in the lethality of suicidal acts were fairly consistent across all four European countries examined. Males and females did not differ in age at time of suicidal behaviour. Suicide attempts by males were rated as being more serious independent of the method used, with the exceptions of attempted hanging, suggesting gender differences in intentionality associated with suicidal behaviour. These findings contribute to understanding of the spectrum of reasons for gender differences in the lethality of suicidal behaviour and should inform the development of gender specific strategies for suicide prevention.

This dataset contains the cumulative number of deaths, average number of deaths annually, average annual crude and adjusted death rates with corresponding 95% confidence intervals, and average annual years of potential life lost per 100,000 residents aged 75 and younger due to selected causes of death, by Chicago community area, for the years 2004 – 2008. A ranking for each measure is also provided, with the highest value indicated with a ranking of 1. See the full description at https://data.cityofchicago.org/api/assets/AEE362B2-8986-41D1-847E-B5A3ACAC5B76.

DOI Almami Samori part 3 Jawleng Karamoho, Almami Samori's son, was sent to France to study the French people. The French recognized him and displayed their superior equipment, it was their way of signaling a threat. He later returned and reported that the French are more powerful than they were. This angered Samori and he sentenced him to death in prison. When Samori was asked the reason for killing his son. He explained that he killed him because he was a coward. Samori fought with the ruler of Sikasso, Kebba Trawalley for twelve months but could not defeat him. During that period Samori was informed that the people of Wassulong had reverted from the practice of Islam. But before he departed Sikasso he made a peace agreement with the ruler. When he finally left, he was pursued by the Kebba Trawalley’s brother who attacked him but he was defeated and captured by Samori. Samori arrived in Wassulong and fought them mercilessly and those who escaped went to settle in a town called Bolibanna. After, he conquered Wassulong, he returned to Bisandugu. He fought, defeated and killed Bankuntu Saxajigi who was living in the mountains. Samori traveled to Niame and surrounded the town but this was where his mother originated from. The people of Niame decided to commit suicide to avoid being slaves. References to entities made in the recording Culture: Mandinka Language: Mandinka and translated into Wollof Persons: Almami Samori, Jawleng Karamoho, Kebba Trawalley, Bankuntu Saxajigi Relationships: Almami Samori was an Islamic Scholar and a Jihadist Jawleng Karamoho was one of Samori’s sons. Kebba Trawalley was the ruler at Sikasso Places: France, Bisandugu, Sikasso, Wassulong and Niame Movements: The people of Wassulong who escaped the war, went and settled at Bolibanna. Actions: Almami Samori fought and conquered Wassulong and Bakuntu Sahajigi but could not defeat Sikasso

Niumi was first ruled by the Jammeh clan. Seneke Jammeh the first ruler received blessings from the King of Manding Mansa Musa. Nuimi also had female rulers. The Jammeh clan ruled prior to the time of Kalama Koy who had Mansa Nyontiranjang, Bata Juwara, Anna, Mansa Musa Ndenge, and Musu Mama Andamen. During this period Mana Bunta was at Sitanunku and Mansa Wally who committed suicide in Banjul, were both from the Jammeh clan. Mansa Wally committed suicide because he refused being a held captive to by the British. Members of the Manneh clan arrived in Niumi after Sama, Tumana, and Keleman Kotoba were dispatched from Kabu to support the people of Kombo in their war against Foni. Consequently, Foni was defeated and the people fled to Karon. The Manneh clan members settled at Berefet and Keleman Kotoba returned to Kabu. Numerous Manneh clan members moved to the region, while the Jammeh clan ruled the region. The Manneh clan members were advised to respect the Jammeh clan. They settled Buniadu and the first ruler there was Jaali Kassa. Among his children were; Jaali Kambi, Jumo, Yunka, and Komankan Jambujite. Meanwhile, Mansa Mari was staying with Kanuma a member of the Manneh clan. The throne frequently rotated between the Jammeh clan and the Manneh clan. Later, the Sonko clan arrived, who received blessings from their marabout. They journeyed from a place called Diniyang and traveled through Bankiri at Wagan. Gido Yaldi and Demba Yaldi were the parents of Kolli Tengele who settled in Berending, Bubu Tengele settled at Jifate and Yero Tengele settled at Essau. When Kolli Tengele came to settle in Niumi the people often paid tax to the ruler of Saloum and he stated it was his intention to abolish this. He was promised a position of leadership in Niumi if he kept his word. The inhabitants of Niumi fought a vicious war with Saloum and were defeated. They shared the land from Faraba Wagan, located between Sokone and Sangako. A ruler called Arfang Lang Manneh, was assigned to settle there, and he was later succeeded by Lieutenant Ceesay from Niani Kayai. After the First World War, he was in Kongel and then transferred to Sangako as chief. It was intended by the Jammeh clan to betray the Sonkos upon their agreement. They fought bitterly and eventually agreed to appoint a ruler from the Sonko clan. At Berending they selected a ruler. The throne at Berending changed multiple rulers including Biram Sonko, Wagan Sonko, Momisa Nyana, Mansa Demba Koto, and Jenung Wuleng. Mansa Demba also fought alongside Jaliya at Balli and they were both related. The reason for war was Sunkaru Jabunay. The rulers at Essau included Matang Sonko a female, Kama Sonko, Banna Sonko, Biram Tenang Tamba and Hali Maranta. During the time of Mansa Burungay, he had conflicts with Biram Sira, his elder brother. Biram Sira was at Bakau waiting to inherit the throne.

Number and percentage of deaths, by month and place of residence, 1991 to most recent year.