Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automatically
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/).
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
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.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Historical chart and dataset showing India suicide rate by year from 2000 to 2021.
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.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
This data shows deaths (of people age 10 and over) from Suicide and Undetermined Injury, numbers and rates by gender, as 3-year moving-averages. Suicide is a significant cause of premature deaths occurring generally at younger ages than other common causes of premature mortality. It may also be seen as an indicator of underlying rates of mental ill-health. Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates. The figures in this dataset include deaths recorded as suicide (people age 10 and over) and undetermined injury (age 15 and over) as those are mostly likely also to have been caused by self-harm rather than unverifiable accident, neglect or abuse. The population denominators for rates are age 10 and over. Low numbers may result in zero values or missing data. Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator 41001 (E10). This data is updated annually.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
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.
Download data on suicides in Massachusetts by demographics and year. This page also includes reporting on military & veteran suicide, and suicides during COVID-19.
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/
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
This data shows deaths (of people age 10 and over) from Suicide and Undetermined Injury, numbers and rates by gender, as 3-year moving-averages.
Suicide is a significant cause of premature deaths occurring generally at younger ages than other common causes of premature mortality. It may also be seen as an indicator of underlying rates of mental ill-health.
Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.
The figures in this dataset include deaths recorded as suicide (people age 10 and over) and undetermined injury (age 15 and over) as those are mostly likely also to have been caused by self-harm rather than unverifiable accident, neglect or abuse. The population denominators for rates are age 10 and over. Low numbers may result in zero values or missing data.
Data source: Office for Health Improvement and Disparities (OHID), Public Health Outcomes Framework (PHOF) indicator 41001 (E10). This data is updated annually.
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.
Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automatically
Close to 800 000 people die due to suicide every year, which is one person every 40 seconds. Suicide is a global phenomenon and occurs throughout the lifespan. Effective and evidence-based interventions can be implemented at population, sub-population and individual levels to prevent suicide and suicide attempts. There are indications that for each adult who died by suicide there may have been more than 20 others attempting suicide.
Suicide is a complex issue and therefore suicide prevention efforts require coordination and collaboration among multiple sectors of society, including the health sector and other sectors such as education, labour, agriculture, business, justice, law, defense, politics, and the media. These efforts must be comprehensive and integrated as no single approach alone can make an impact on an issue as complex as suicide.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains counts of deaths for California counties 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 each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county 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.
This dataset provides model-based provisional estimates of the weekly numbers of drug overdose, suicide, and transportation-related deaths using “nowcasting” methods to account for the normal lag between the occurrence and reporting of these deaths. Estimates less than 10 are suppressed. These early model-based provisional estimates were generated using a multi-stage hierarchical Bayesian modeling process to generate smoothed estimates of the weekly numbers of death, accounting for reporting lags. These estimates are based on several assumptions about how the reporting lags have changed in recent months across different jurisdictions, and the resulting estimates differ from other sources of provisional mortality data. For now, these estimates should be considered highly uncertain until further evaluations can be done to determine the validity of these assumptions about timeliness. The true patterns in reporting lags will not be known until data are finalized, typically 11–12 months after the end of the calendar year. Importantly, these estimates are not a replacement for monthly provisional drug overdose death counts, or quarterly provisional mortality estimates. For more detail about the nowcasting methods and models, see: Rossen LM, Hedegaard H, Warner M, Ahmad FB, Sutton PD. Early provisional estimates of drug overdose, suicide, and transportation-related deaths: Nowcasting methods to account for reporting lags. Vital Statistics Rapid Release; no 11. Hyattsville, MD: National Center for Health Statistics. February 2021. DOI: https://doi.org/10.15620/ cdc:101132
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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;
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Number of suicides and suicide rates, by sex and age, for England and Wales.
This dataset contains counts of deaths for California residents by ZIP Code based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths of California residents. The data tables include deaths of residents of California by ZIP Code of residence (by residence). The data are reported as totals, as well as stratified by age and gender. 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.
Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automatically
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/).