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.

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.

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;

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

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.

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

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;

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

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.

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;

Age-specific suicide rates and rate ratios.

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

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 DATASET WAS LAST UPDATED AT 2:10 AM EASTERN ON OCT. 7

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.

This dataset describes drug poisoning deaths at the U.S. and state level by selected demographic characteristics, and includes age-adjusted death rates for drug poisoning. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html.

Descriptive statistics of county suicide, index scores, and index items.

Suicide is a tragedy and an important public health concern. Suicide prevention is a top priority for the Canadian Armed Forces (CAF). Monitoring and analyzing suicide events of CAF members provides valuable information to guide and refine ongoing suicide prevention efforts. The dataset shows the comparison of CAF regular force male member’s suicide rates by deployment history to Canadian rates using standardized mortality ratios from 1995 to 2012.

Sample characteristics overall and by component.

This is qualitative data collection of semi-structured interviews conducted between June-July 2023, and online surveys conducted throughout 2022, within a study that examined how the Prisons and Probation Ombudsman (seek to) effect change in prisons following prisoner suicides and how verbatim film can help to increase the impact of research findings. The study ran from 2021-2023. Prisoner suicide rates are consistently higher than rates among communities living outside. Between 2012 and 2016, England and Wales’s prison suicide rates more than doubled, hitting record numbers in 2016. Often those most invested in prison safety are those personally impacted, and campaigns by prisoners’ families can have material effects. This project included a collaboration between an academic research team, a bereaved parent and a theatre company, which aimed to raise awareness of prison suicide through verbatim film and communicate key messages to stakeholders across criminal justice.In May 2019, Dutch courts refused to deport an English suspected drug smuggler, citing the potential for inhuman and degrading treatment at HMP Liverpool. This well publicised judgment illustrates the necessity of my FLF: reconceptualising prison regulation, for safer societies. It seeks to save lives and money, and reduce criminal reoffending. Over 10.74 million people are imprisoned globally. The growing transnational significance of detention regulation was signalled by the Optional Protocol to the United Nations Convention against Torture/OPCAT. Its 89 signatories, including the UK, must regularly examine treatment and conditions. The quality of prison life affects criminal reoffending rates, so the consequences of unsafe prisons are absorbed by our societies. Prison regulation is more urgent than ever. England and Wales' prisons are now less safe than at any point in recorded history, containing almost 83,000 prisoners: virtually all of whom will be released at some point. In 2016, record prison suicides harmed prisoners, staff and bereaved families, draining 385 million punds from public funds. Record prisoner self-harm was seen in 2017, then again in 2018. Criminal reoffending costs £15 billion annually. Deteriorating prison safety poses a major moral, social, economic and public health threat, attracting growing recognition. Reconceptualising prison regulation is a difficult multidisciplinary challenge. Regulation includes any activity seeking to steer events in prisons. Effective prison regulation demands academic innovation and sustained collaboration and implementation with practitioners from different sectors (e.g. public, voluntary), regulators, policymakers, and prisoners: from local to (trans)national levels. Citizen participation has become central to realising more democratic, sustainable public services but is not well integrated across theory-policy-practice. I will coproduce prison regulation with partners, including the Prisons and Probation Ombudsman, voluntary organisations Safe Ground and the Prison Reform Trust, and (former) prisoners. This FLF examines three diverse case study countries: England and Wales, Brazil and Canada, developing multinational implications. This approach is ambitious and risky, but critical for challenging commonsensical beliefs. Interviews, focus groups, observation and creative methodologies will be used. There are three aims, to: i) theorise the (potential) participatory roles of prisoners and the voluntary sector in prison regulation ii) appraise the (normative) relationships between multisectoral regulators (e.g. public, voluntary) from local to (trans)national scales iii) co-produce (with multisectoral regulators), pilot, document and disseminate models of participatory, effective and efficient prison regulation in England and Wales (and beyond) - integrating multisectoral, multiscalar penal overseers and prisoners into regulatory theory and practice. This is an innovative study. Punishment scholars have paid limited attention to regulation. Participatory networks of (former) prisoners are a relatively new formation but rapidly growing in influence. Nobody has yet considered agencies like the Prisons Inspectorate and Ombudsman alongside voluntary sector organisations and participatory networks, nor their collective influences from local to transnational scales. Nobody has tried to work with all of these agencies to reconceptualise prison regulation and test it in practice. Findings will be developed, disseminated and implemented internationally. The research team will present findings and engage with diverse stakeholders and decision makers through interactive workshops (Parliament, London, Manchester, Liverpool and Birmingham), and multimedia outputs (e.g. infographics). This FLF has implications for prisons and detention globally, and broader relevance as a case study of participatory regulation of public services and policy translation. Within this project, 2 semi-structured interviews were undertaken with film co-creators and 27 anonymous online surveys were completed by audience members in film screenings. The sample was purposive for all groups, as appropriate for our exploratory analysis and the resources available, however the sample is not representative of collaborative film creators or audiences. Telephone and videoconferencing (Microsoft TEAMS) interviews (at the participant’s preference) were conducted with filmmakers between June and July 2023. Anonymous online surveys were completed at film screenings between March and November 2022.

BackgroundOur current understanding of Asian American mortality patterns has been distorted by the historical aggregation of diverse Asian subgroups on death certificates, masking important differences in the leading causes of death across subgroups. In this analysis, we aim to fill an important knowledge gap in Asian American health by reporting leading causes of mortality by disaggregated Asian American subgroups.Methods and FindingsWe examined national mortality records for the six largest Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) and non-Hispanic Whites (NHWs) from 2003-2011, and ranked the leading causes of death. We calculated all-cause and cause-specific age-adjusted rates, temporal trends with annual percent changes, and rate ratios by race/ethnicity and sex. Rankings revealed that as an aggregated group, cancer was the leading cause of death for Asian Americans. When disaggregated, there was notable heterogeneity. Among women, cancer was the leading cause of death for every group except Asian Indians. In men, cancer was the leading cause of death among Chinese, Korean, and Vietnamese men, while heart disease was the leading cause of death among Asian Indians, Filipino and Japanese men. The proportion of death due to heart disease for Asian Indian males was nearly double that of cancer (31% vs. 18%). Temporal trends showed increased mortality of cancer and diabetes in Asian Indians and Vietnamese; increased stroke mortality in Asian Indians; increased suicide mortality in Koreans; and increased mortality from Alzheimer’s disease for all racial/ethnic groups from 2003-2011. All-cause rate ratios revealed that overall mortality is lower in Asian Americans compared to NHWs.ConclusionsOur findings show heterogeneity in the leading causes of death among Asian American subgroups. Additional research should focus on culturally competent and cost-effective approaches to prevent and treat specific diseases among these growing diverse populations.