BackgroundIn Europe, men have lower rates of attempted suicide compared to women and at the same time a higher rate of completed suicides, indicating major gender differences in lethality of suicidal behaviour. The aim of this study was to analyse the extent to which these gender differences in lethality can be explained by factors such as choice of more lethal methods or lethality differences within the same suicide method or age. In addition, we explored gender differences in the intentionality of suicide attempts.Methods and FindingsMethods. Design: Epidemiological study using a combination of self-report and official data. Setting: Mental health care services in four European countries: Germany, Hungary, Ireland, and Portugal. Data basis: Completed suicides derived from official statistics for each country (767 acts, 74.4% male) and assessed suicide attempts excluding habitual intentional self-harm (8,175 acts, 43.2% male).Main Outcome Measures and Data Analysis. We collected data on suicidal acts in eight regions of four European countries participating in the EU-funded “OSPI-Europe”-project (www.ospi-europe.com). We calculated method-specific lethality using the number of completed suicides per method * 100 / (number of completed suicides per method + number of attempted suicides per method). We tested gender differences in the distribution of suicidal acts for significance by using the χ2-test for two-by-two tables. We assessed the effect sizes with phi coefficients (φ). We identified predictors of lethality with a binary logistic regression analysis. Poisson regression analysis examined the contribution of choice of methods and method-specific lethality to gender differences in the lethality of suicidal acts.Findings Main ResultsSuicidal acts (fatal and non-fatal) were 3.4 times more lethal in men than in women (lethality 13.91% (regarding 4106 suicidal acts) versus 4.05% (regarding 4836 suicidal acts)), the difference being significant for the methods hanging, jumping, moving objects, sharp objects and poisoning by substances other than drugs. Median age at time of suicidal behaviour (35–44 years) did not differ between males and females. The overall gender difference in lethality of suicidal behaviour was explained by males choosing more lethal suicide methods (odds ratio (OR) = 2.03; 95% CI = 1.65 to 2.50; p < 0.000001) and additionally, but to a lesser degree, by a higher lethality of suicidal acts for males even within the same method (OR = 1.64; 95% CI = 1.32 to 2.02; p = 0.000005). Results of a regression analysis revealed neither age nor country differences were significant predictors for gender differences in the lethality of suicidal acts. The proportion of serious suicide attempts among all non-fatal suicidal acts with known intentionality (NFSAi) was significantly higher in men (57.1%; 1,207 of 2,115 NFSAi) than in women (48.6%; 1,508 of 3,100 NFSAi) (χ2 = 35.74; p < 0.000001).Main limitations of the studyDue to restrictive data security regulations to ensure anonymity in Ireland, specific ages could not be provided because of the relatively low absolute numbers of suicide in the Irish intervention and control region. Therefore, analyses of the interaction between gender and age could only be conducted for three of the four countries. Attempted suicides were assessed for patients presenting to emergency departments or treated in hospitals. An unknown rate of attempted suicides remained undetected. This may have caused an overestimation of the lethality of certain methods. Moreover, the detection of attempted suicides and the registration of completed suicides might have differed across the four countries. Some suicides might be hidden and misclassified as undetermined deaths.ConclusionsMen more often used highly lethal methods in suicidal behaviour, but there was also a higher method-specific lethality which together explained the large gender differences in the lethality of suicidal acts. Gender differences in the lethality of suicidal acts were fairly consistent across all four European countries examined. Males and females did not differ in age at time of suicidal behaviour. Suicide attempts by males were rated as being more serious independent of the method used, with the exceptions of attempted hanging, suggesting gender differences in intentionality associated with suicidal behaviour. These findings contribute to understanding of the spectrum of reasons for gender differences in the lethality of suicidal behaviour and should inform the development of gender specific strategies for suicide prevention.

Historical chart and dataset showing India suicide rate by year from 2000 to 2021.

The dataset contains World Bank Suicide mortality rate WDI (world development indicator) (2000-2019) world-wide data in original and processed form. In addition to the statistical data this dataset also contains bibliographic records of articles published on the topic of suicide in relation to individual countries during (2000-2019) in original and processed form.

The data consists of six archives:

1. World development indicator suicide mortality rate SH.STA.SUIC.P5. This archive contains suicide mortality rate of 159 countries during the period of 2000-2019 per 100,000 population including males and females as of November, 2023.
2. Web of science records country and suicide. This archive contains bibliographic records organized by country on the topic of suicide related to that country published during 2000-2019 as of November, 2023.
3. Suicide mortality rate statistics and keywords. This archive contains processed data of 1 and 2 archives in three files. The 'Countries suicide rates and WOS records' contains organized temporal suicide mortality rate data for each country and each year for males and females including counts of articles on suicide related in that country. The 'words and countries matrix' file contains information about how many times author and paper keywords from suicide related publications were seen in articles associated with each country. This data is organized as matrix in which rows are keywords, columns are countries and cells are counts of the keyword. The 'words and countries pairs' file contains same information only organized as keyword country pairs.
4. Suicide mortality rate clusters countries keywords titles. This archive contains bibliographic data organized by country clusters. These clusters group countries with similar suicide mortality rate dynamics in males and females shown in two included figures. Each folder of the cluster contains a section with bibliographic records; a section with keywords associated with each country; and a section in which each publication associated with the country has a separate filecontaining its title and keywords.
5. Suicide keywords embedding data. This archive contains word embedding vectors and metadata learned by recurrent neural network trained to classify countries from suicide related keywords of articles associated with those countries. Folder 'trained with keywords' contains embeddings learned in classifying countries in which training samples are keyword strings of publications. Folder 'trained with titles' contains embeddings learned in classifying countries in which training samples are strings containing titles of publication plus keywords.
6. Suicide keywords association rule mining. This archive contains files of subsets of keywords frequently mentioned together in suicide related publications. Folder 'Mining in clusters' has frequent keyword itemsets in country clusters. Folder 'Mining in individual countries' has frequent keyword itemsets in countries. Examples of keyword networks connecting clusters and networks connecting countries in individual clusters are included which helps to identify specific and shared keywords by country clusters and by countries in the individual clusters.

These datasets support a data availability statements for upcoming articles.

This dataset contains data about obesity, suicides and unemployment segregated by Country. The sources of data are wikipedia tables as updated on 11/04/2022. More information can be found in project's github: https://github.com/martinsanc/wikipedia_scraper

Países (List of countries by population (United Nations) - Wikipedia)

Country

UN continental region

UN statistical subregion

Population 1 July 2018

Population 1 July 2019

Change

Desempleo (List of countries by unemployment rate - Wikipedia)

Unemployment Rate

Sourcedate of information

Suicidios (List of countries by suicide rate - Wikipedia)

All

Male

Female

Tasa de obesidad por país (List of countries by suicide rate - Wikipedia)

Rank

Obesity rate

Crude suicide rates (per 100 000 population)

  Dataset Description

This dataset provides information on 'Crude suicide rates' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: per 100 000 population

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset is in a wide format.… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/crude-suicide-ratesby-sex-for-african-countries.

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.

Age-standardized suicide rates (per 100 000 population)

  Dataset Description

This dataset provides information on 'Age-standardized suicide rates' for countries in the WHO African Region. The data is disaggregated by the 'Sex' dimension, allowing for analysis of health inequalities across different population subgroups. Units: per 100 000 population

  Dimensions and Subgroups

Dimension: Sex Available Subgroups: Female, Male

  Data Structure

The dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/age-standardized-suicide-ratesby-sex-for-african-countries.

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.

These datasets were collected to fulfil the requirement of University coursework.

The complete source code and paper are available on GitHub. Click here.

About Dataset

These datasets contain the information of the World Development Indicator (WDI) provided by the world bank, the non-communicable mortality rate, the suicide rate and the number of health workforce data by the World Health Organization (WHO).

Implementation

• Data Cleaning and Preparation
• Exploratory Data Analysis
• Machine Learning Prediction - Random Forest
• Deep Learning Prediction - Neural Network

ABSTRACT Objectives: Analyze the suicide mortality time trends among adolescents in Brazil from 2000 to 2015. Methods: Data were collected from the Brazilian Mortality Database and from the Brazilian Institute of Geography and Statistics. Study variables were sex, year and underlying cause of death. The study included deaths from Intentional Self-Harm, X60-X84 – according to the 10th Revision of the International Classification of Diseases (ICD-10), of adolescents aged 10 to 19. The simple linear regression technique was used and results were considered statistically significant when p ≤ 5%. Results: From 2000 to 2015, there were 11,947 deaths due to suicide of adolescents in Brazil and 67% of these occurred in male adolescents, which corresponds to a 2,06:1 male-female ratio. There was a statistically significant increase in adolescent suicide mortality in Brazil (p = 0.016), which increased from 1.71 per 100,000 inhabitants in 2000 to 2.51 in 2015, a raise of 47%. The increase occurred in behalf of the increment in suicides of male adolescents (p = 0.001) specifically in the North (p < 0.001) and Northeast (p < 0.001) of Brazil. In regard to the female group, there was a downtrend of mortality by suicide in the Center West region (p = 0.039), but when it comes to Brazil as a whole, there was a stabilization behavior of mortality by suicide. Conclusions: These results indicate an increase in the suicide rate of adolescents in Brazil, particularly in the male population. The improvement of suicide prevention strategies in Brazil is imperative.

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

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Abstract (en): These data are a collection of demographic statistics for the populations of 125 countries or areas throughout the world, prepared by the Statistical Office of the United Nations. The units of analysis are both country and data year. The primary source of data is a set of questionnaires sent monthly and annually to national statistical services and other appropriate government offices. Data include statistics on approximately 50 types of causes of death for the years 1966 through 1974 for males, females, and total populations. Causes of death in 125 countries or areas throughout the world between the years 1966 and 1974. 2005-11-04 On 2005-03-14 new files were added to one or more datasets. These files included additional setup files as well as one or more of the following: SAS program, SAS transport, SPSS portable, and Stata system files. The metadata record was revised 2005-11-04 to reflect these additions. The causes of death are classified according to the 6th, 7th, and 8th versions of an abbreviated list of the World Health Organization's INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Therefore, data for causes of death are not necessarily comparable across countries or data years. Users should refer to Variable 5 in the Variable List for full discussion of this problem. Users interested in comparing deaths for countries or years that use different versions of the Abbreviated list should consult two publications: A. Joan Klebba, and Alice B. Dolman. COMPARABILITY OF MORTALITY STATISTICS FOR THE SEVENTH AND EIGHTH REVISIONS OF THE INTERNATIONAL CLASSIFICATION OF DISEASES, UNITED STATES. Rockville, MD: United States Department of Health, Education, and Welfare. Public Health Service. Health Services and Mental Health Administration. National Center for Health Statistics, 1975, and World Health Organization. MANUAL OF THE INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES, INJURIES, AND CAUSES OF DEATH. Geneva, Switzerland: World Health Organization, 1967.The user should note that countries have data covering a variety of time spans (the maximum span being 1965-1973), and the data have not been padded to supply missing data codes for those years for which a country does not have data. Thus, Egypt has data for years 1965 through 1972, while Kenya has data for only 1970. (See Appendix D in the codebook to determine the years for which a country has data.)It is important that any user of these data consult the United Nations' DEMOGRAPHIC YEARBOOK, 1976, for further explanation of the data's limitations. Certain countries have modified reporting procedures which are presented in both the footnotes and the technical notes accompanying the tables in the Yearbook. There is no way to identify these problems using only the machine-readable data.In order to eliminate unnecessary repetition of identifying information, data were merged so that each record now contains all the data for a country for one particular year. In this process, breakdowns of deaths by ethnic group and/or urban/rural classification were omitted since only a few countries provided such information. Each record now contains the data for the number of deaths from each cause of death for male, female, and total.While the data appear to be in a rectangular matrix, such is not the case. This occurs because different versions of the abbreviated list are referenced in different data years. The lack of a rectangular data matrix does little to restrict the manageability of the dataset. See codebook for examples.While the data have been reformatted and documented by ICPSR staff, there has been no attempt to verify the accuracy and consistency of the data received from the U.N. Statistical Office.

Age-adjusted rate of patient discharges after being hospitalized due to suicide attempts/ideation for Santa Clara County residents. The data are provided for the total county population and by sex and race/ethnicity. The data trends are presented from 2007 to 2014. Source: Office of Statewide Planning and Development, 2007-2014 Patient Discharge Data; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes and sourceYear (Numeric): Year of hospital dischargeCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, and race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only).Age adjusted rate per 100,000 people (String): The Ninth Revision of the International Classification of Diseases codes (ICD-9) are used for coding patient discharge data. Age-adjusted rate is calculated using 2000 U.S. Standard Population. Rate of hospitalization due to suicide attempt/ideation is number of related hospital discharges in a year per 100,000 people in the same time period. Data are not presented if the number of hospital discharges is 15 or less.

2020 - 2022, county-level U.S. stroke death rates. Dataset developed by the Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.Create maps of U.S. stroke death rates by county. Data can be stratified by age, race/ethnicity, and sex.Visit the CDC Atlas of Heart Disease and Stroke for additional data and maps. Atlas of Heart Disease and StrokeData SourceMortality data were obtained from the National Vital Statistics System. Bridged-Race Postcensal Population Estimates were obtained from the National Center for Health Statistics. International Classification of Diseases, 10th Revision (ICD-10) codes: I60-I69; underlying cause of death.Data DictionaryData for counties with small populations are not displayed when a reliable rate could not be generated. These counties are represented in the data with values of '-1.' CDC excludes these values when classifying the data on a map, indicating those counties as 'Insufficient Data.'Data field names and descriptionsstcty_fips: state FIPS code + county FIPS codeOther fields use the following format: RRR_S_aaaa (e.g., API_M_35UP)  RRR: 3 digits represent race/ethnicity    All - Overall    AIA - American Indian and Alaska Native, non-Hispanic    ASN - Asian, non-Hispanic    BLK - Black, non-Hispanic    HIS - Hispanic NHP – Native Hawaiian or Other Pacific Islander, non-Hispanic MOR – More than one race, non-Hispanic    WHT - White, non-Hispanic  S: 1 digit represents sex    A - All    F - Female    M - Male  aaaa: 4 digits represent age. The first 2 digits are the lower bound for age and the last 2 digits are the upper bound for age. 'UP' indicates the data includes the maximum age available and 'LT' indicates ages less than the upper bound. Example: The column 'BLK_M_65UP' displays rates per 100,000 black men aged 65 years and older.MethodologyRates are calculated using a 3-year average and are age-standardized in 10-year age groups using the 2000 U.S. Standard Population. Rates are calculated and displayed per 100,000 population. Rates were spatially smoothed using a Local Empirical Bayes algorithm to stabilize risk by borrowing information from neighboring geographic areas, making estimates more statistically robust and stable for counties with small populations. Data for counties with small populations are coded as '-1' when a reliable rate could not be generated. County-level rates were generated when the following criteria were met over a 3-year time period within each of the filters (e.g., age, race, and sex).At least one of the following 3 criteria:At least 20 events occurred within the county and its adjacent neighbors.ORAt least 16 events occurred within the county.ORAt least 5,000 population years within the county.AND all 3 of the following criteria:At least 6 population years for each age group used for age adjustment if that age group had 1 or more event.The number of population years in an age group was greater than the number of events.At least 100 population years within the county.More Questions?Interactive Atlas of Heart Disease and StrokeData SourcesStatistical Methods

IntroductionMany national and subnational governments need to routinely measure the completeness of death registration for monitoring and statistical purposes. Existing methods, such as death distribution and capture-recapture methods, have a number of limitations such as inaccuracy and complexity that prevent widespread application. This paper presents a novel empirical method to estimate completeness of death registration at the national and subnational level.MethodsRandom-effects models to predict the logit of death registration completeness were developed from 2,451 country-years in 110 countries from 1970–2015 using the Global Burden of Disease 2015 database. Predictors include the registered crude death rate, under-five mortality rate, population age structure and under-five death registration completeness. Models were developed separately for males, females and both sexes.FindingsAll variables are highly significant and reliably predict completeness of registration across a wide range of registered crude death rates (R-squared 0.85). Mean error is highest at medium levels of observed completeness. The models show quite close agreement between predicted and observed completeness for populations outside the dataset. There is high concordance with the Hybrid death distribution method in Brazilian states. Uncertainty in the under-five mortality rate, assessed using the dataset and in Colombian departmentos, has minimal impact on national level predicted completeness, but a larger effect at the subnational level.ConclusionsThe method demonstrates sufficient flexibility to predict a wide range of completeness levels at a given registered crude death rate. The method can be applied utilising data readily available at the subnational level, and can be used to assess completeness of deaths reported from health facilities, censuses and surveys. Its utility is diminished where the adult mortality rate is unusually high for a given under-five mortality rate. The method overcomes the considerable limitations of existing methods and has considerable potential for widespread application by national and subnational governments.

This dataset includes count and age-adjusted rate per 100,000 population of mortality (death) in Virginia for 9 chronic conditions by year and by demographic groups (i.e., age, race/ethnicity, and sex). Age group values include 0 to 17 years, 18 to 44 years, 45 to 54 years, 55 to 64 years, 65 to 74 years, and 75+ years. Race/ethnicity values include American Indian or Alaska Native, Asian or Pacific Islander, Black or African American, Hispanic or Latino, and White. Sex values include female and male. Data set includes mortality data from 2016 to the most current year for Virginia residents.

The 9 chronic conditions include: Alzheimer’s Disease, Cardiovascular disease, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Asthma, Diabetes, Stroke, Heart Disease, and Hypertension. The International Classification of Diseases, Tenth Revision (ICD-10) codes are used to identify chronic disease mortality indicators. Definitions are based on Underlying Cause of Death on the death certificate outlined in the “Underlying Cause-of-Death List for Tabulating Mortality Statistics” instruction manual developed by the National Center for Health Statistics at the Centers for Disease Control and Prevention (CDC) found here OCR Document (cdc.gov).

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

THIS DATASET WAS LAST UPDATED AT 2:11 AM EASTERN ON JULY 12

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.