SPAN receives data from VHA suicide prevention coordinators relating to suicidal ideation and suicidal behavior of Veterans. Data include relevant historical activities and related medical concerns as reviewed in the Veteran's medical record. Data are submitted to VSSC and are cleaned, processed, and managed by statistical staff and program analysts at the VISN 2 Center of Excellence for Suicide Prevention on behalf of the Mental Health Services.

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;

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.

Among deaths of despair, the individual and community correlates of US suicides have been consistently identified and are well-known. Yet, the suicide rate has been stubbornly unyielding to reduction efforts, promoting calls for novel research directions. Linking levels of influence have been proposed in theory but blocked by data limitations in the U.S. Guided by theories on the importance of connectedness and responding to unique data challenges of low base rates, geographical dispersion, and appropriate comparison groups, we attempt the first harmonization of data from the National Violence Data Reporting System (NVDRS) and the American Community Survey (ACS) to match individual-county level risks. We theorize cross-level socio-demographic homogeneity between individuals and communities, or “sameness”, focusing on whether having like-others in the community moderates known individual suicide risks. While analyses from this new Multi-level Suicide Data for the US (MSD-US) replicate several individual and contextual findings, considering sameness changes usual understandings of risk in two critical ways. First, the high individual risk for suicide among those who are unemployed, younger, not US born, widowed or married, unemployed, or have physical disabilities is cut substantially with greater sameness. Second, this moderating pattern flips for Native Americans, Alaska Natives, Asians and Hispanics, as well as among native-born and unmarried individuals, where low individual suicide risk increases significantly in places of greater similarity. Results mark the joint influence of social structure and culture, deliver unique insights on the complexity of connectedness in suicide, and offer novel considerations for policy and practice.

NOTE: This dataset is no longer supported and is provided as-is. Any historical knowledge regarding meta data or it's creation is no longer available. All known information is proved as part of this data set. The Veteran Health Administration, in support of the Open Data Initiative, is providing the Veterans Affairs Suicide Prevention Synthetic Dataset (VASPSD). The VASPSD was developed using a real, record-level dataset provided through the VA Office of Suicide Prevention. The VASPSD contains no real Veteran information, however, it reflects similar characteristics of the real dataset. NOTICE: This data is intended to appear similar to actual VASPSD data but it does not have any real predictive modeling value. It should not be used in any real world application.

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 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.

Extracted in machine readable form from the AIHW General Record of Incidence of Mortality (GRIM) books.

GRIM books are Excel workbooks that contain national level, historical and recent deaths data for specific causes of death. The tables present age- and sex-specific counts and rates by cause of death, along with other summary measures.

GRIM books are available for all causes of death combined and 55 other cause of death groupings. They span different years for different causes of death, depending on the data available. GRIM books for some causes of death start at 1907 and they are the only national electronic tabulations of deaths data by cause registered before 1964. Data from 1964 onwards are sourced from the AIHW National Mortality Database. They include mortality data up to 2023.

For more information, please see Deaths data at AIHW or contact us at deaths@aihw.gov.au.

Also available on data.gov.au are the AIHW Mortality Over Regions and Time (MORT) books.

Mortality rate has been age-adjusted to the 2000 U.S. standard population. ICD-10 codes used to identify suicides are X60-X84, Y87.0, and U03. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Suicide is a leading cause of preventable death in Los Angeles County, affecting individuals of all ages and races and ethnicities. While there is a strong association between suicide and health conditions, such as mood and anxiety disorders or substance use disorders, suicide is rarely caused by a single circumstance and is more often due to a combination of individual, relational, and environmental factors. Individual factors can include history of mental illness, previous suicide attempts, adverse childhood events, or financial hardship. Relational factors include experiences of bullying, loss of relationships, or social isolation. Environmental factors include lack of access to healthcare, community violence, or social stigma associated with seeking help for a mental illness.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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.

Source: Suicide Awareness Voices of Education https://jmcintos.pages.iu.edu/SuicideDataCompiled.htm

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.

Iran: Suicide mortality rate, per 100,000 population: The latest value from 2019 is 5.2 suicides per 100,000 people, a decline from 5.7 suicides per 100,000 people in 2018. In comparison, the world average is 9.49 suicides per 100,000 people, based on data from 180 countries. Historically, the average for Iran from 2000 to 2019 is 6.52 suicides per 100,000 people. The minimum value, 5.2 suicides per 100,000 people, was reached in 2019 while the maximum of 8 suicides per 100,000 people was recorded in 2000.

The included dataset contains 10,000 synthetic Veteran patient records generated by Synthea. The scope of the data includes over 500 clinical concepts across 90 disease modules, as well as additional social determinants of health (SDoH) data elements that are not traditionally tracked in electronic health records. Each synthetic patient conceptually represents one Veteran in the existing US population; each Veteran has a name, sociodemographic profile, a series of documented clinical encounters and diagnoses, as well as associated cost and payer data. To learn more about Synthea, please visit the Synthea wiki at https://github.com/synthetichealth/synthea/wiki. To find a description of how this dataset is organized by data type, please visit the Synthea CSV File Data Dictionary at https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary.The included dataset contains 10,000 synthetic Veteran patient records generated by Synthea. The scope of the data includes over 500 clinical concepts across 90 disease modules, as well as additional social determinants of health (SDoH) data elements that are not traditionally tracked in electronic health records. Each synthetic patient conceptually represents one Veteran in the existing US population; each Veteran has a name, sociodemographic profile, a series of documented clinical encounters and diagnoses, as well as associated cost and payer data. To learn more about Synthea, please visit the Synthea wiki at https://github.com/synthetichealth/synthea/wiki. To find a description of how this dataset is organized by data type, please visit the Synthea CSV File Data Dictionary at https://github.com/synthetichealth/synthea/wiki/CSV-File-Data-Dictionary.

"OBJECTIVE: People with alcohol dependence suffer from poor health outcomes, including excessive suicide mortality. This study estimated the suicide rate and explored the risk and protective factors for suicide in a large-scale Asian population.

METHOD: We enrolled patients with alcohol dependence (ICD-9 code 303**) consecutively admitted to a psychiatric center in northern Taiwan from January 1, 1985, through December 31, 2008 (N = 2,793). Using patient linkage to the national mortality database (1985-2008), we determined that 960 patients died during the study period. Of those deaths, 65 patients died of suicide. On the basis of risk-set sampling for the selection of controls, we conducted a nested case-control study and collected the information by means of a standardized chart review process. We estimated the standardized mortality ratio (SMR) for suicide mortality. Conditional logistic regression was employed for exploring the risk and protective factors for suicide.

RESULTS: The study subjects had excessive suicide and all-cause deaths, with SMRs of 21.2 and 12.7, respectively. We pinpointed auditory hallucination (adjusted risk ratio [aRR] = 1.80, P = .04) and attempted suicide (aRR = 7.52, P = .001) as the risk factors associated with suicide. In contrast, protective factors included financial independence (aRR = 0.11, P = .005) and being married (aRR = 0.16, P = .02). Intriguingly, those with physical illnesses had a lower risk of suicide (aRR = 0.15, P = .01).

CONCLUSIONS: Compared with the general population, those with alcohol dependence faced excessive suicide mortality. For a comprehensive approach to suicide prevention, recognizing and improving the protective factors could have equal importance in mitigating the risk of suicide."

ObjectivePreventing suicide in US youth is of paramount concern, with rates increasing over 50% between 2007 and 2018. Statistical modeling using electronic health records may help identify at-risk youth before a suicide attempt. While electronic health records contain diagnostic information, which are known risk factors, they generally lack or poorly document social determinants (e.g., social support), which are also known risk factors. If statistical models are built incorporating not only diagnostic records, but also social determinants measures, additional at-risk youth may be identified before a suicide attempt.MethodsSuicide attempts were predicted in hospitalized patients, ages 10–24, from the State of Connecticut’s Hospital Inpatient Discharge Database (HIDD; N = 38943). Predictors included demographic information, diagnosis codes, and using a data fusion framework, social determinants features transferred or fused from an external source of survey data, The National Longitudinal Study of Adolescent to Adult Health (Add Health). Social determinant information for each HIDD patient was generated by averaging values from their most similar Add Health individuals (e.g., top 10), based upon matching shared features between datasets (e.g., Pearson’s r). Attempts were then modelled using an elastic net logistic regression with both HIDD features and fused Add Health features.ResultsThe model including fused social determinants outperformed the conventional model (AUC = 0.83 v. 0.82). Sensitivity and positive predictive values at 90 and 95% specificity were almost 10% higher when including fused features (e.g., sensitivity at 90% specificity = 0.48 v. 0.44). Among social determinants variables, the perception that their mother cares and being non-religious appeared particularly important to performance improvement.DiscussionThis proof-of-concept study showed that incorporating social determinants measures from an external survey database could improve prediction of youth suicide risk from clinical data using a data fusion framework. While social determinant data directly from patients might be ideal, estimating these characteristics via data fusion avoids the task of data collection, which is generally time-consuming, expensive, and suffers from non-compliance.

Number of suicides and suicide rates by sex, country and quarter, registered from 1981 to 2020.

Chad TD: Suicide Mortality Rate: Male data was reported at 10.300 NA in 2016. This records an increase from the previous number of 10.200 NA for 2015. Chad TD: Suicide Mortality Rate: Male data is updated yearly, averaging 9.500 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 10.300 NA in 2016 and a record low of 8.800 NA in 2000. Chad TD: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.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;

ObjectiveTo evaluate the association between cortisol levels and suicide in patients with depression through a meta-analysis to provide an early warning for suicide prevention.MethodsDatabases including China National Knowledge Infrastructure (CNKI), Weipu Database (VIP), Wanfang Database, PubMed, Cochrane Library, Web of Science, and Embase were searched to collect case-control studies, cohort studies, and cross-sectional studies investigating the relationship between cortisol levels and suicide in patients with depression. The meta-analysis was conducted using Stata 18.0. Meanwhile, we collected data from 131 participants to measure their cortisol levels, including Hair cortisol levels (HCL) were calculated for the 0–1 cm and 1–2 cm segments.ResultsThe meta-analysis indicated that cortisol levels in depressed patients with suicidal behavior were significantly higher than healthy individuals (SMD = 0.350, 95% CI [0.003, 0.696]). However, the cortisol levels in patients with suicidal behavior were only slightly higher than those in patients without suicidal behavior, and the pooled effect size (SMD = 0.108, 95% CI [-0.151, 0.367]) was non-significant. The depression patients with suicidal behaviors had significantly lower levels of 0–1 cm HCL (3.46 ± 1.92 ng/mg) than those in the depression patients without suicidal ideation (5.43 ± 2.42 ng/mg) (P=0.003) and in the depression patients with suicidal ideation (5.04 ± 2.30 ng/mg) (P=0.037). Similarly, 1–2 cm HCL was significantly lower in the depression with suicidal behavior group (3.21 ± 1.47 ng/mg) than in the depression without suicidal ideation group (5.65 ± 3.59 ng/mg) (P=0.009) and in the depression with suicidal ideation group (5.10 ± 2.88 ng/mg) (P=0.050).ConclusionThe study found that cortisol levels in patients with depression who exhibited suicidal behavior were higher than those in the healthy population. However, no significant difference in cortisol levels was observed between patients with depression and suicidal behavior and those without suicidal behavior. The experiment revealed that patients with depression and suicidal behavior had lower hair cortisol levels (HCL) than those depression without suicidal behavior.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024609538, identifier CRD42024609538.