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.

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;

United States US: Suicide Mortality Rate: per 100,000 Population data was reported at 15.300 Number in 2016. This records an increase from the previous number of 15.000 Number for 2015. United States US: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 13.200 Number from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 15.300 Number in 2016 and a record low of 11.300 Number in 2000. United States US: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: 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;

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

United States US: Suicide Mortality Rate: Female data was reported at 7.200 NA in 2016. This records an increase from the previous number of 7.100 NA for 2015. United States US: Suicide Mortality Rate: Female data is updated yearly, averaging 5.900 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 7.200 NA in 2016 and a record low of 4.900 NA in 2000. United States US: Suicide Mortality Rate: Female 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;

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 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.

This report provides information regarding suicide mortality for the years 2001–2014. It incorporates the most recent mortality data from the VA/Department of Defense (DoD) Joint Suicide Data Repository and includes information for deaths from suicide among all known Veterans of U.S. military service. Data for the Joint VA/DoD Suicide Data Repository were obtained from the National Center for Health Statistics’ National Death Index through collaboration with the DoD, the CDC, and the VA/DoD Joint Suicide Data Repository initiative. Data available from the National Death Index include reports of mortality submitted from vital statistics systems in all 50 U.S. states, New York City, Washington D.C., Puerto Rico, and the U.S. Virgin Islands.

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.

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

OVERVIEW

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

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

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

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

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

About this Dataset

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

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

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

Using this Dataset

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

Mass killings by year

Mass shootings by year

To get these counts just for your state:

Filter killings by state

Definition of "mass murder"

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

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

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

Methodology

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

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

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

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

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

This project started at USA TODAY in 2012.

Contacts

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

BackgroundThe rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need.ObjectiveThis study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk.MethodSixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants' feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC).ResultsFor both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51–0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43–0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52–0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65–0.87). Participants reported the experience comfortable and shared their feelings.ConclusionMDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks.

ABSTRACT Objective To describe the epidemiological profile and analyze the time trend of suicide mortality among adolescents (10-19 years old) from the Brazilian Northeast, from 2001 to 2015. Methods This is an observational study, which took place in the Northeast region, Brazil. The study period was from 2001 to 2015. Deaths from intentional self-harm (X60 to X84). exogenous poisoning of undetermined intent (Y10 to Y19) and intentional self-harm (Y87.0) were considered, according to the 10th Review of the International Classification of Diseases (ICD-10), for adolescents aged 10 to 19 years. The variables analyzed were: sex, age group, race / color, specific ICD, state of residence and suicide mortality rate/100,000 inhabitants. Results There were 3,194 deaths due to suicide in the age group studied, with a male predominance (62.1%; n = 1,984), age group 15 to 19 years (84.8%; n = 2,707), race/brown color (65.4%; n = 2,090); between 4 and 7 years of schooling (31.7%; n = 1,011) and at CID X70 (47.8%; n = 1,528). The time trend of mortality was increasing from 2001 to 2015 (APC: 2.4%; p < 0.01), with higher rates in males. There was an increasing trend in the suicide rate, among men, throughout the period (AAPC: 2.9%; p < 0.01). In women, a decreasing trend was identified as of 2004 (APC: -2.2%; p < 0.01). Conclusion The epidemiological profile was characterized by male gender, age group 15-19 years, color/brown race and average schooling. The trend showed a growth pattern in males and a decline in females. It is recommended that public policies are aimed at the adolescent population.

Abstract: Although depression and anxiety are known to result in disabilities and workplace and health system losses, population-based studies on this problem are rare in Brazil. The current study assessed the prevalence of mental disorders in adolescents, youth, and adults and the relationship to sociodemographic characteristics in five birth cohorts (RPS) in Ribeirão Preto (São Paulo State), Pelotas (Rio Grande do Sul State), and São Luís (Maranhão State), Brazil. Major depressive episode, suicide risk, social phobia, and generalized anxiety disorder were assessed with the Mini International Neuropsychiatric Interview. Bootstrap confidence intervals were estimated and prevalence rates were stratified by sex and socioeconomic status in the R program. The study included 12,350 participants from the cohorts. Current major depressive episode was more prevalent in adolescents in São Luís (15.8%; 95%CI: 14.8-16.8) and adults in Ribeirão Preto (12.9%; 95%CI: 12.0-13.9). The highest prevalence rates for suicide risk were in adults in Ribeirão Preto (13.7%; 95%CI: 12.7-14.7), and the highest rates for social phobia and generalized anxiety were in youth in Pelotas, with 7% (95%CI: 6.3-7.7) and 16.5% (95%CI: 15.4-17.5), respectively. The lowest prevalence rates of suicide risk were in youth in Pelotas (8.8%; 95%CI: 8.0-9.6), social phobia in youth in Ribeirão Preto (1.8%; 95%CI: 1.5-2.2), and generalized anxiety in adolescents in São Luís (3.5%; 95%CI: 3.0-4.0). Mental disorders in general were more prevalent in women and in individuals with lower socioeconomic status, independently of the city and age, emphasizing the need for more investment in mental health in Brazil, including gender and socioeconomic determinants.

ObjectiveMajor depressive disorder (MDD) is associated with suicidal attempts (SAs) among adolescents, with suicide being the most common cause of mortality in this age group. This study explored the predictive utility of support vector machine (SVM)-based analyses of amplitude of low-frequency fluctuation (ALFF) results as a neuroimaging biomarker for aiding the diagnosis of MDD with SA in adolescents.MethodsResting-state functional magnetic resonance imaging (rs-fMRI) analyses of 71 first-episode, drug-naive adolescent MDD patients with SA and 54 healthy control individuals were conducted. ALFF and SVM methods were used to analyze the imaging data.ResultsRelative to healthy control individuals, adolescent MDD patients with a history of SAs showed reduced ALFF values in the bilateral medial superior frontal gyrus (mSFG) and bilateral precuneus. These lower ALFF values were also negatively correlated with child depression inventory (CDI) scores while reduced bilateral precuneus ALFF values were negatively correlated with Suicidal Ideation Questionnaire Junior (SIQ-JR) scores. SVM analyses showed that reduced ALFF values in the bilateral mSFG and bilateral precuneus had diagnostic accuracy levels of 76.8% (96/125) and 82.4% (103/125), respectively.ConclusionAdolescent MDD patients with a history of SA exhibited abnormal ALFF. The identified abnormalities in specific brain regions may be involved in the pathogenesis of this condition and may help identify at-risk adolescents. Specifically, reductions in the ALFF in the bilateral mSFG and bilateral precuneus may be indicative of MDD and SA in adolescent patients.

Association between the risk factors and suicidal ideation among respondents.

BackgroundThe United States Food and Drug Administration (FDA) maintains a black-box warning for antidepressants warning of an increased risk of suicidality in children and young adults that is based on proprietary clinical trial data from study sponsors that were submitted for regulatory approval. This article aimed to assess whether the black-box warning for antidepressants is still valid today using recent drug safety data.MethodsPost-marketing adverse drug event data were obtained from the US FDA’s Adverse Event Reporting System (FAERS) for the years 2017 through 2023. Logistic regression analysis was conducted using the case versus non-case methodology and adjusted for gender, age group, drug role (primary drug, secondary drug, interacting drug, and concomitant drug), initial FDA reporting year, reporter country, and a drug*gene*age group interaction.ResultsIn the multivariate analysis, compared to fluoxetine and patients aged 25 to 64 years, children [adjusted reporting odds ratio (aROR) = 7.38, 95% CI, 6.02–9.05] and young adults (aROR = 3.49, 95% CI, 2.65–4.59) were associated with an increased risk of reporting suicidality, but not for the elderly (aROR = 0.76, 95% CI, 0.53–1.09). Relative to fluoxetine, esketamine was associated with the highest rate of reporting suicidality in children (aROR = 3.20, 95% CI, 2.25–4.54); however, esketamine was associated with a lower risk of reporting suicidality in young adults (aROR = 0.59, 95% CI, 0.41–0.84), but not significantly in the elderly (aROR = 0.77, 95% CI, 0.48–1.23). For country-specific findings, relative to the USA, the Slovak Republic, India, and Canada had the lowest risk of reporting suicidality. For the overall study population, desvenlafaxine (aROR = 0.61, 95% CI, 0.46–0.81) and vilazodone (aROR = 0.56, 95% CI, 0.32–0.99) were the only two antidepressants associated with a reduced risk of reporting suicidality.ConclusionThis study shows that with recent antidepressant drug safety data, the US FDA’s black-box warning for prescribing antidepressants to children and young adults is valid today in the USA. However, relative to the USA, 15 countries had a significantly lower risk of reporting suicidality, while 16 countries had a higher risk of reporting suicidality from 38 antidepressants and lithium.

Socio-demographic characteristics of the respondents (n = 750).

BackgroundThe psychological emptiness (PE) presents a state of emotional emptiness and lack of meaning, and is increasingly relevant in modern society. However, few studies have tried to assess its dimension and suicide history. This study aims to establish the factorial structure of PE and compare its predictive power against a standard depression inventory.MethodsA total of 45,335 university students participated in the study. The 20 items involving lack of purpose, depression, and meaninglessness in life were used to evaluate the PE, alongside the Beck Depression Inventory (BDI). Exploratory factor analysis (EFA) was employed to identify the structure of the 20 items, while confirmatory factor analysis (CFA) was employed to confirm the construct validity of the model. Logistic regression analysis was conducted to assess the predictive relationship between the identified factors and suicide history.ResultsEFA identified a three-factor structure: Depression and Self-Harm/Suicidal Tendencies (DST), Life Meaning and Purpose (LMP), and Study Motivation (SM). The three factors accounted for 23.1, 12.6, and 11.9% of the total variance, respectively. CFA confirmed the construct validity of the model, which showed acceptable fit indices and high internal consistency (Cronbach’s α > 0.80). Logistic regression analysis revealed that DST was the strongest predictor of suicide risk (AUC = 0.84), outperforming traditional depression scales (BDI, AUC = 0.58).ConclusionThe present study provides a comprehensive framework for understanding PE. The PE may include three psychological dimensions, while DST is a strong predictor of suicide history.

Unadjusted and Adjusted Odds Ratios for Association between Tobacco Use Categories and Mental Health Outcomes.