Number, percentage and rate (per 100,000 population) of homicide victims, by racialized identity group (total, by racialized identity group; racialized identity group; South Asian; Chinese; Black; Filipino; Arab; Latin American; Southeast Asian; West Asian; Korean; Japanese; other racialized identity group; multiple racialized identity; racialized identity, but racialized identity group is unknown; rest of the population; unknown racialized identity group), gender (all genders; male; female; gender unknown) and region (Canada; Atlantic region; Quebec; Ontario; Prairies region; British Columbia; territories), 2019 to 2024.

Number, percentage and rate (per 100,000 population) of homicide victims, by gender (all genders; male; female; gender unknown) and Indigenous identity (total; Indigenous identity; non-Indigenous identity; unknown Indigenous identity), Canada, provinces and territories, 2014 to 2024.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by gender. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported daily, with timestamps indicated in the daily briefings posted at: portal.ct.gov/coronavirus. Data are subject to future revision as reporting changes. Starting in Ju

There has been little research on United States homicide rates from a long-term perspective, primarily because there has been no consistent data series on a particular place preceding the Uniform Crime Reports (UCR), which began its first full year in 1931. To fill this research gap, this project created a data series on homicides per capita for New York City that spans two centuries. The goal was to create a site-specific, individual-based data series that could be used to examine major social shifts related to homicide, such as mass immigration, urban growth, war, demographic changes, and changes in laws. Data were also gathered on various other sites, particularly in England, to allow for comparisons on important issues, such as the post-World War II wave of violence. The basic approach to the data collection was to obtain the best possible estimate of annual counts and the most complete information on individual homicides. The annual count data (Parts 1 and 3) were derived from multiple sources, including the Federal Bureau of Investigation's Uniform Crime Reports and Supplementary Homicide Reports, as well as other official counts from the New York City Police Department and the City Inspector in the early 19th century. The data include a combined count of murder and manslaughter because charge bargaining often blurs this legal distinction. The individual-level data (Part 2) were drawn from coroners' indictments held by the New York City Municipal Archives, and from daily newspapers. Duplication was avoided by keeping a record for each victim. The estimation technique known as "capture-recapture" was used to estimate homicides not listed in either source. Part 1 variables include counts of New York City homicides, arrests, and convictions, as well as the homicide rate, race or ethnicity and gender of victims, type of weapon used, and source of data. Part 2 includes the date of the murder, the age, sex, and race of the offender and victim, and whether the case led to an arrest, trial, conviction, execution, or pardon. Part 3 contains annual homicide counts and rates for various comparison sites including Liverpool, London, Kent, Canada, Baltimore, Los Angeles, Seattle, and San Francisco.

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.

Femicide is an homicide of women murdered for reasons of gender. This is the most extreme form of violence against women.

Femicides are serious problems in Latin America and the Caribbean. According to data from CEPTAL in 2021, 11 Latin American countries registered a rate equal to or greater than one victim of femicide or femicide for every 100,000 women. The highest femicide rates are in Honduras (4.6 cases per 100,000 women), the Dominican Republic (2.7 cases per 100,000 women) and El Salvador (2.4 cases per 100,000 women).

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 examines the number of unidentified persons reported to the Centers for Disease Control and Preventions (CDC) National Death Index (NDI), by State, from 1980 to 2004. This report also looks at the number of unidentified human remains reported to the Federal Bureau of Investigations (FBI) National Crime Information Center (NCIC) Unidentified Person File. It describes the characteristics by race and gender and the manner of death. Highlights include the following: Between 1980 and 2004, about 10,300 unidentified human remains were reported to the National Death Index (NDI). Almost three-quarters of unidentified persons were reported by 5 states; Arizona, California, Florida, New York, and Texas. Of the 2,900 National Crime Information Center records that contained data on the manner of death, 27% were ruled homicides; 12%, accidental deaths; 7%, natural causes; and 5%, suicides. The majority of unidentified persons were white (70%); blacks made up 15% of unidentified persons; and race could not be determined in 13% of the cases. For more information about this data go to: http://www.ojp.usdoj.gov/bjs/abstract/uhrus04.htm

This data package consists of 26 datasets all containing statistical data relating to the population and particular groups within it belonging to different countries, mostly the United States.

This research file includes data accessed from Prosecution Project records as part of a study of the prosecution and sentencing of inter-gender murder (male on female, and female on male) in NSW 1901-1955. The original records have been supplemented with more attributes including context of event, as well as biographical data for both defendants and victims. The research was directed by Carolyn Strange.

COVID-19 confirmed case death statistics from 2020 onwards, according to region, age group, and gender stratified secondary statistical table. This dataset is updated once daily according to a fixed schedule. Currently, most of the imported cases of COVID-19 are diagnosed through testing at airports or centralized quarantine facilities and are immediately isolated and treated, so their city and county information is not indicated.

Leading Causes of Death US

1980–2009 by Sex, Race, and Hispanic Origin

By Health [source]

About this dataset

This fascinating dataset takes a look at the leading causes of death in the United States from 1980-2009, broken down by sex, race, and Hispanic origin. This data sheds light on how mortality in the US has changed over time among these categories. Accounting for everything from heart disease to cancer to suicide, this insight can be used by health researchers and policy makers to gain a better understanding of disparities in healthcare and deaths across different groups. Whether studying questions related to public health or more targeted population issues such as gender biases in death rates, this dataset provides an important resource for anyone interested in examining mortality across demographic lines

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How to use the dataset

This dataset can be used to explore some of the leading causes of death in the United States from 1980 to 2009, broken down by sex, race, and Hispanic origin. This data can be used to better understand mortality trends and risk factors associated with different populations in America.

By using this dataset you can compare and contrast mortality rates across different gender, racial, and ethnic groups during this time period. You can also compare different causes of death within these demographic categories to see if there are any patterns over time or notable differences between groups.

You could even use this data to track changes across population groups as a whole or look at details for specific years or types of causes of death in particular groups. With this information one may gain insight into health disparities across population segments in America— aiding advocates for social change & public policy shifts toward improved health outcomes for all Americans!

Research Ideas

• Analyzing regional or state-level differences in mortality rates over time.
• Examining the beahvioral factors or risk factors associated with each cause of death for different genders and populations.
• Examining the prevalence of each cause of death as a proportion to an overall population trend in different socio-economic categories such as race or income level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Selected_Trend_Table_from_Health_United_States_2011._Leading_causes_of_death_and_numbers_of_deaths_by_sex_race_and_Hispanic_origin_United_States_1980_and_2009.csv | Column name | Description | |:-------------------|:---------------------------------------------------------------------------------------------------------| | Group | The group of people the cause of death applies to (e.g. men, women, whites, blacks, hispanics). (String) | | Year | The year the cause of death was recorded. (Integer) | | Cause of death | The cause of death. (String) | | Flag | A flag indicating whether the cause of death is considered a leading cause. (Boolean) | | Deaths | The number of deaths attributed to the cause of death. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

Statistical information on confirmed cases and deaths of severe special infectious pneumonia starting in 2020, with secondary statistical tables stratified by region, age group, and gender. This data set is updated once a day according to the system's fixed schedule. At present, there are more cases of severe special infectious pneumonia imported from overseas than those confirmed by tests at airports or centralized quarantine stations and immediately isolated and treated, so their county and city information is not marked.

This data is compiled by the Cook County Department of Public Health using data from the Illinois Department of Public Health Vital Statistics. It includes the annual number of deaths, crude and age-adjusted death rates by selected causes of death. Further analysis is available by age group, race/ethnicity, gender and decedent's place of residence in suburban Cook County at the time of their death. Note: Counts suppressed for events between 1 and 4, Rates not calculated for events less than 20

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

See the Splitgraph documentation for more information.

The data comprises of interview transcripts with young people, wider community members, and national experts on gangs, murder, gender based violence, security and drug trafficking in Port of Spain, Trinidad. The overarching research questions/objectives were: (1) To identify how transnational organized crime and drug-trafficking interfaces with vulnerable communities; how, through whom, when, and why? (2) To better understand the relationship between this upturn in violence and local masculine identities and men’s violence, including male-on-male murder, such as gang violence, and male-on-female/child SGBV. These aimed to explore the impact of transnational organised crime and drug-trafficking (TNOC) on poor urban communities in Port-of-Spain, Trinidad, which has seen crime and violence soar since the mid-1990s as the city became transhipment point in the illegal drugs trade. The research project studies the impact of transnational organised crime and drug-trafficking (TNOC) on poor urban communities in Port-of-Spain, Trinidad, which has seen crime and violence soar since the mid-1990s as the city became transhipment point in the illegal drugs trade. We address the impact of TNOC on vulnerable populations, culture and security by considering the 'transnational-to-community' impact of drug-trafficking. In particular we consider how TNOC contributes to a number of male residents becoming increasingly violent at a micro level as 92% of homicide victims are men: how do relatively benign 'corner kids' turn into violent gang members? In turn we ask, how can these communities work with young men to insulate themselves from the negative impact and violence generation of TNOC? This research uses masculinities as an interpretive lens and draws upon scholars across the disciplines of Peace Studies, Cultural Anthropology, and International Relations. The methodology is rooted in Trinidadian 'Spoken Word' traditions, and art and music, to grasp how male identity, culture, community violence and TNOC intersect. Before high levels of TNOC emerged, the region had relatively low levels of violent crime. However, this changed rapidly with the onset of cocaine trafficking in early 1990s across the Caribbean which dovetailed with the multiple clefts of colonial legacies, exclusion and poverty, worsened by the collapse of traditional agricultural exports, racial divisions and widespread institutional weaknesses. Violent death rates in cities in the region have grown to outstrip many warzones, whilst some of the highest rates of sexual and gender based violence (SGBV) in the world are found in the Caribbean. The answers to understanding violence must be sought at the interface between cocaine-driven TNOC and vulnerable communities, as poor residents have become disproportionately affected by violence. TNOC has weakened the rule of law, posing stiff challenges to already struggling institutions, whilst transforming local communities, hence the rather topical title of this research proposal 'Breaking Bad'. However, we still understand relatively little about the transformative processes between TNOC and community level violence. Furthermore, we understand little about how masculinities become violent in communities traversed by TNOC. It is at the intersection between TNOC, community, and masculinities, that the new violence of Port-of-Spain can be most productively understood. Certainly it is an area where we must strengthen policy and programming. Whilst there is no silver-bullet solution to violence in these cities, masculinities are clearly an important part of the solution and are almost completely overlooked. This research project strives to create pragmatic, evidence based recommendations to lead to concrete impact by promoting innovative, community-led and gender-based solutions for the populations that most suffer from violence, whilst serving to interrupt the negative impact that TNOC has on poor neighbourhoods. Qualitative methods were used based on semi-structured interviews; and the use of innovative Spoken Word workshops to discuss issues on gender, violence, gangs and drugs with young people. Spoken Word Workshops: These used culturally attuned spoken word (slam-poetry) techniques, as well as drawing and arts, and field trips, to encourage young people to engage creatively with the research questions on gender, violence, culture, and the roll of drugs, gangs, weapons and transnational organised crime, on poor neighbourhoods in the Port of Spain. Our researchers gleaned information from the informal discussions that took place during these sessions. During the workshops our researchers built rapport with the youth participants, five of whom were also interviewed one-on-one about these topics. A curriculum from the workshops was piloted and developed into an impact tool to be rolled out by out local partner. Interviews: One-on-one interviews deposited here include those with five young people (18-25) who come from poor, gang afflicted communities in the Port of Spain who took part in the Spoken Word workshops run during the project; twelve experts across multilateral organisations, NGOs, Community Organisations, Government Ministries, and national Military and Police Forces. These used a semi-structured approach (see Breaking Bad Topic and question guide for interviews). Focus Groups: These were held across different populations; two groups of five adult men and women within poor communities afflicted by gang violence; one with five youths from poor communities afflicted by gang violence; one with four security experts; and one with four members of the national security forces, both police and military.

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.

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

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.

Note: Starting April 27, 2023 updates change from daily to weekly.

Summary

The cumulative number of probable COVID-19 deaths among Maryland residents by gender: Female; Male; Unknown.

Description

The MD COVID-19 - Probable Deaths by Gender Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by gender. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Gender Distribution data layer.

Terms of Use

The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

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

See the Splitgraph documentation for more information.

The table provides the number of WCB accepted occupational fatalities by gender in the most current year. It also provides the number of people in employment by gender. The fatality frequency rate was calculated by dividing the number of fatality claims from workers of different gender group to the number of workers employed by different gender group and multiply the result by one million. The number of fatalities was counted based on the year of death occurred.