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.

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

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.

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 2021 are final. Changes as of January 10th 2023: The final figures for 2021 have been added. When will new figures be published: In the third quarter of 2023 the provisonal figures for 2022 will be published.

Dataset Description

This dataset provides comprehensive information on the death rates for suicide in the United States, segmented by sex, race, Hispanic origin, and age, spanning from 1950 to 2020. The data is sourced from reputable public health records and aims to offer valuable insights into the demographic factors associated with suicide rates over an extensive period.

Columns Overview

1. INDICATOR: This column indicates that the dataset pertains to "Death rates for suicide." It remains constant throughout the dataset.
2. UNIT: Specifies the unit of measurement, which is "Deaths per 100,000 resident population, age-adjusted." This standardization allows for consistent comparison across different demographic groups.
3. UNIT_NUM: A numerical representation of the unit, where 1 corresponds to the unit described above. This is primarily useful for data processing.
4. STUB_NAME: Represents the broad category of the population, such as "Total" (all populations), "Male," "Female," and different racial and ethnic groups (e.g., "White, non-Hispanic," "Black, non-Hispanic").
5. STUB_NAME_NUM: A numerical representation of the STUB_NAME column, facilitating easier data manipulation.
6. STUB_LABEL: Provides a detailed description of the population category, giving a more specific breakdown of the demographic groups.
7. STUB_LABEL_NUM: A numerical representation of the STUB_LABEL column.
8. YEAR: The year the data corresponds to, ranging from 1950 to 2020. This allows for time-series analysis of suicide rates.
9. YEAR_NUM: A numerical representation of the year, useful for chronological data analysis.
10. AGE: The age category of the population, such as "All ages," "Under 1 year," "1-4 years," etc. This breakdown helps in understanding the impact of age on suicide rates.
11. AGE_NUM: A numerical representation of the AGE column, aiding in numerical analysis.
12. ESTIMATE: The estimated suicide rate for the specified demographic group in the given year, expressed in deaths per 100,000 resident population.
13. FLAG: Additional notes or flags about the data, which may include indications of data quality or other relevant information. This column helps in identifying potential issues or special considerations in the data.

Dataset Highlights

• Longitudinal Data: Covers an extensive period from 1950 to 2020, allowing for a detailed analysis of trends and changes in suicide rates over time.
• Demographic Breakdown: Provides insights into how suicide rates vary by sex, race, Hispanic origin, and age, highlighting potential disparities and areas for targeted intervention.
• Public Health Relevance: This dataset is crucial for public health officials, researchers, and policymakers to understand and address the factors contributing to suicide in various demographic groups.

Potential Analyses

1. Trend Analysis: Examine how suicide rates have changed over the decades for different demographic groups.
2. Demographic Disparities: Identify which groups are most affected by suicide and how these disparities have evolved.
3. Age-Specific Rates: Analyze suicide rates across different age groups to identify vulnerable populations.
4. Impact of Societal Changes: Correlate changes in suicide rates with significant societal events or policy changes.

Usage Recommendations

• Visualizations: Create line charts, bar graphs, and heatmaps to visualize trends and disparities in suicide rates.
• Statistical Analysis: Use regression models to identify factors significantly associated with changes in suicide rates.
• Public Health Policy: Leverage insights from the data to inform and evaluate public health interventions aimed at reducing suicide rates.

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;

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.

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.

The Youth Risk Behavior Surveillance System (YRBSS) is a set of surveys that monitor priority health risk behaviors and experiences that contribute markedly to the leading causes of death, disability, and social problems among youth of grade 9 -12 in the United States. The surveys are administered every other year and it is maintained by the Centers for Disease Control and Prevention (CDC). A total of 107 questionnaire are asked. Some of the health-related behaviors and experiences monitored are: * Student demographics: sex, sexual identity, race and ethnicity, and grade * Youth health behaviors and conditions: sexual, injury and violence, bullying, diet and physical activity, obesity, and mental health, suicide attempt * Substance use behaviors: electronic vapor product and tobacco product use, alcohol use, and other drug use * Student experiences: parental monitoring, school connectedness, unstable housing, and exposure to community violence The dataset is used by a group of graduate students from Texas State University for 2025 TXST Open Datathon. The main YRBSS dataset includes data of multiple years, various states, district. For analyzing demographic variations associated with suicide, the 1991–2023 combined district dataset (https://www.cdc.gov/yrbs/files/sadc_2023/HS/sadc_2023_district.dat) is used, which offers a broad historical perspective on trends across different groups. To examine the preventive measures and develop a predictive model for suicide risk, the 2023 dataset (https://www.cdc.gov/yrbs/files/2023/XXH2023_YRBS_Data.zip) was used, ensuring the inclusion of the most recent behavioral and attributes. Please review the 2023 YRBS Data User's Guide by CDC for further information.

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

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;

Estimates of suicides among higher education students by sex, age group, ethnicity, type of study, and student term time accommodation between the academic years ending 2017 and 2023. Based on mortality records linked to Higher Education Statistics Agency (HESA) student records. These are official statistics in development.

Age-adjustment mortality rates are rates of deaths that are computed using a statistical method to create a metric based on the true death rate so that it can be compared over time for a single population (i.e. comparing 2006-2008 to 2010-2012), as well as enable comparisons across different populations with possibly different age distributions in their populations (i.e. comparing Hispanic residents to Asian residents).
Age adjustment methods applied to Montgomery County rates are consistent with US Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS) as well as Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA). PHS Planning and Epidemiology receives an annual data file of Montgomery County resident deaths registered with Maryland Department of Health and Mental Hygiene’s Vital Statistics Administration (DHMH VSA).
Using SAS analytic software, MCDHHS standardizes, aggregates, and calculates age-adjusted rates for each of the leading causes of death category consistent with state and national methods and by subgroups based on age, gender, race, and ethnicity combinations. Data are released in compliance with Data Use Agreements between DHMH VSA and MCDHHS. This dataset will be updated Annually.

This dataset tracks the updates made on the dataset "Vital Statistics Suicide Deaths by Age-Group, Race/Ethnicity, Resident Region, and Gender: Beginning 2003" as a repository for previous versions of the data and metadata.

Mental health characteristics and suicidal thoughts, by age group and sex, Canada (excluding territories) and provinces.

Number of suicides and suicide rates, by sex and age, for England and Wales.

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.

United States US: Suicide Mortality Rate: Male data was reported at 23.600 NA in 2016. This records an increase from the previous number of 23.000 NA for 2015. United States US: Suicide Mortality Rate: Male data is updated yearly, averaging 20.700 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.600 NA in 2016 and a record low of 17.900 NA in 2000. United States US: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.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;

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.