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.

This dataset combines historical county-level data from the Community Health Assessment Tool (CHAT) with last year's suicide rate data from the Pierce County Medical Examiners' database (MEDIS). The purpose of this combined dataset is to provide the most up-to-date information on suicide rates in Pierce County with historical data for comparing Pierce County to other neighboring counties.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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.

Number of suicides, suicide rates and median registration delays, by local authority in England and Wales.

For a summary of the case study, please go to "Portfolio Project".

Context

This data analysis was meant to show that men have their own issues in society that are being ignored. The mental health has been declining especially for men. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. This data analysis was meant to show that men have their own issues in society that are being ignored. This decline worldwide maybe due to a multitude of other variables that may correlate such as: internet usage/social media usage, social belonging, work hours, dating apps, and physical health. These variables may require a separate dataset going into more detail about them.

A space dedicated just for men and another just for women to speak about their problems with help and constructive criticism for growth and for social belonging maybe required to improve the mental health of society (among other variables). This does not mean that the struggles of women are nonexistent. There are already a multitude of datasets and articles dedicated to some of the possible struggles of women from MSNBC, CNN, NBC, BBC, Netflix movies, and even popular secular music like recent songs WAP from Megan Thee Stallion, God is a Women by Arianna Grande, etc. This dataset's objective was not made to continue to light a flame between the already hostile relationships that modern men and women have with each other. Awareness without bias is the goal.

For the results, please read the portfolio project and leave comments.

Content

Where the data were obtained:

1. The first excel file was obtained from https://data.world/vizzup/mental-health-depression-disorder-data/workspace/file?filename=Mental+health+Depression+disorder+Data.xlsx

2. The second excel file was obtained from https://ourworldindata.org/grapher/male-vs-female-suicide

3. The third excel file was obtained from https://ourworldindata.org/suicide

4. The fourth excel file was obtained from https://ourworldindata.org/drug-use

Inspiration

I want to be the best data analyst ever, so criticism (regardless of the harshness), it will be greatly appreciated. What would you have added/improved on? Was it easy to understand? What else do you want me to make a dataset on?

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

This dataset provides the age-standardised rate of deaths from suicide and injury of undetermined intent. It includes deaths registered in calendar years and classified using specific ICD-10 codes. The data is aggregated into quinary age bands starting from age 10 and is expressed per 100,000 population. Age standardisation ensures comparability across different population groups and time periods.

Rationale Reducing the suicide rate is a critical public health goal. Monitoring this indicator helps identify trends, assess the effectiveness of mental health interventions, and inform policy decisions aimed at preventing suicide and supporting at-risk populations.

Numerator The numerator is the number of deaths from suicide and injury of undetermined intent. These are classified by underlying cause of death using ICD-10 codes X60–X84 (ages 10+ only) and Y10–Y34 (ages 15+ only), and are registered in the respective calendar years. The data is grouped into quinary age bands.

Denominator The denominator is the aggregated population-years for individuals aged 10 and over, also grouped into quinary age bands. These population estimates are based on the 2021 Census.

Caveats Rates for the period 2001 to 2006 were revised in March 2015. Prior to this revision, ICD code Y33.9 was incorrectly included, which resulted in inflated rates for those years. Users should be cautious when comparing data across this time span.

External References Further details and related indicators can be accessed on the Fingertips Public Health Profiles website.

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

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.

Time series data for the statistic Suicide mortality rate (per 100,000 population) and country Jamaica. Indicator Definition:Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).The indicator "Suicide mortality rate (per 100,000 population)" stands at 1.69 as of 12/31/2021, the highest value since 12/31/2010. Regarding the One-Year-Change of the series, the current value constitutes an increase of 8.33 percent compared to the value the year prior.The 1 year change in percent is 8.33.The 3 year change in percent is 2.42.The 5 year change in percent is 4.97.The 10 year change in percent is 11.92.The Serie's long term average value is 1.42. It's latest available value, on 12/31/2021, is 19.28 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2006, to it's latest available value, on 12/31/2021, is +128.38%.The Serie's change in percent from it's maximum value, on 12/31/2007, to it's latest available value, on 12/31/2021, is -36.70%.

Time series data for the statistic Suicide mortality rate, male (per 100,000 male population) and country Liberia. Indicator Definition:Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).The indicator "Suicide mortality rate, male (per 100,000 male population)" stands at 8.02 as of 12/31/2021, the highest value at least since 12/31/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 1.01 percent compared to the value the year prior.The 1 year change in percent is 1.01.The 3 year change in percent is 4.84.The 5 year change in percent is 6.51.The 10 year change in percent is 0.7538.The Serie's long term average value is 7.30. It's latest available value, on 12/31/2021, is 9.94 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2005, to it's latest available value, on 12/31/2021, is +24.34%.The Serie's change in percent from it's maximum value, on 12/31/2021, to it's latest available value, on 12/31/2021, is 0.0%.

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

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.

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;

Suicide rates per 100,000 person years (October 1, 2007- December 31, 2018).

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_suicide_rate. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: 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. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

ObjectiveWest Virginia’s (WV) suicide rate is 50% higher than the national average and is the highest in the Appalachian Region. Appalachia has several social factors that have contributed to greater socioeconomic deprivation, a known contributor of suicide. Given WV’s high prevalence of suicide and poverty, the current study aims to examine the relationship between socioeconomic deprivation and suicide rates in WV.MethodsThe Townsend Deprivation Index (TDI), Social Deprivation Index (SDI), and Social Vulnerability Index (SVI) measured socioeconomic deprivation. Negative binomial regression models assessed the relationship between socioeconomic deprivation scores, individual index items, and suicide rates. Model comparisons evaluated the indices’ ability to assess suicide rates. A backward selection strategy identified additional key items for examining suicide rates.ResultsThere was a significant increase in suicide rates for every 10% increase in TDI (β = 0.04; p < 0.01), SDI (β = 0.03; p = 0.04), and SVI scores (β = 0.05; p < 0.01). Household overcrowding and unemployment had a positive linear relationship with suicide in TDI (β = 0.04, p = 0.02; β = 0.07, p = 0.01), SDI (β = 0.10, p = 0.02; β = 0.01, p<0.01), and the SVI (β = 0.10, p = 0.02; β = 0.03, p<0.01). The backwards selection strategy identified additional key items included by the SVI when assessing suicide.ConclusionGreater socioeconomic deprivation, measured by the TDI, SDI, and SVI, was significantly associated with higher suicide rates. Expanding unemployment benefits and increasing the availability of affordable housing, especially in rural areas, may be useful in reducing suicide rates. Our results suggest racial and ethnic minorities and adults living with a disability may benefit from targeted suicide prevention strategies.

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.

In 2024, Japan reported 16.4 suicides per 100,000 inhabitants. The country's suicide rate resumed its downward trend after an unexpected surge in recent years, likely connected to the COVID-19 pandemic. What are the reasons behind Japan’s high suicide rates?  While the majority of suicides in Japan stemmed from health reasons, existential concerns and problems directly related to work also accounted for thousands of self-inflicted deaths in the past years. One of the most profound issues faced by employees in Japan leading to self-harm is exhaustion. “Karoshi,” or death by overwork, is a well-known phenomenon in Japanese society. In addition to physical fatigue, karoshi may be precipitated by mental stress resulting from employment. Occupational stress or overwork-induced suicide is referred to as “karojisatsu (overwork suicide)” in Japan. Which demographic groups are affected? Although *************** are frequently depicted as the most at-risk demographic for suicide in Japan, the increasing occurrence of suicides among the elderly people and schoolchildren is causing concern. Bullying, isolation, and the lack of a proficient mental healthcare system can be additional factors contributing to the country’s high suicide rates among all age groups.

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.

IntroductionThe majority of people who die by suicide have never seen a mental health professional or been diagnosed with a mental illness. To date, this majority group has largely been ignored, with most existing research focusing on predictors of suicide such as past suicide attempts. Identifying the characteristics of people who die by suicide without receiving services, often with a fatal first attempt, is crucial to reduce suicide rates through guiding improvements to service pathways and “just in time” interventions.MethodsIn this systematic review, PsycInfo, PubMed, CINAHL, and Web of Science were searched for peer-reviewed articles published from 1980 to 1st March 2021. Included studies examined predictors of non-receipt of formal mental health services among people who died by suicide. Data were extracted from published reports and the quality of included studies was assessed using a modified version of the Joanna Briggs Institute Checklist for Analytical Cross Sectional Studies. This review was registered with PROSPERO, CRD 42021226543.ResultsSixty-seven studies met inclusion criteria, with sample sizes ranging from 39 to 193,152 individuals. Male sex, younger or older age, and rural location were consistently associated with non-receipt of mental health services. People not receiving mental health services were also less likely to have a psychiatric diagnosis, past suicidal behavior or contact with general health services, and more likely to use violent means of suicide. There was some evidence that minority ethnicity and psychosocial stressors were associated with service non-receipt.ConclusionPeople who die by suicide without receiving mental health services are likely to have diverse profiles, indicating the need for multifaceted approaches to effectively support people at risk of suicide. Identifying the needs and preferences of individuals who are at risk of suicide is crucial in developing new support pathways and services, and improving the quality of existing services.Systematic Review Registrationhttp://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42021226543.