The leading causes of death among Black residents in the United States in 2023 included diseases of the heart, cancer, unintentional injuries, and stroke. The leading causes of death for African Americans generally reflect the leading causes of death for the entire United States population. However, a major exception is that death from assault or homicide is the seventh leading cause of death among African Americans but is not among the ten leading causes for the general population. Homicide among African Americans The homicide rate among African Americans has been higher than that of other races and ethnicities for many years. In 2023, around 9,284 Black people were murdered in the United States, compared to 7,289 white people. A majority of these homicides are committed with firearms, which are easily accessible in the United States. In 2023, around 13,350 Black people died by firearms. Cancer disparities There are also major disparities in access to health care and the impact of various diseases. For example, the incidence rate of cancer among African American males is the greatest among all ethnicities and races. Furthermore, although the incidence rate of cancer is lower among African American women than it is among white women, cancer death rates are still higher among African American women.

The leading causes of death among the white population of the United States are cardiovascular diseases and cancer. Cardiovascular diseases and cancer accounted for a combined **** percent of all deaths among this population in 2023. In 2020 and 2021, COVID-19 was the third leading cause of death among white people but was the eighth leading cause in 2023. Disparities in causes of death In the United States, there exist disparities in the leading causes of death based on race and ethnicity. For example, chronic liver disease and cirrhosis is the ***** leading cause of death among the white population and the ******* among the Hispanic population but is not among the ten leading causes for Black people. On the other hand, homicide is the ******leading cause of death among the Black population but is not among the 10 leading causes for whites or Hispanics. However, cardiovascular diseases and cancer by far account for the highest share of deaths for every race and ethnicity. Diseases of despair The American Indian and Alaska Native population in the United States has the highest rates of death from suicide, drug overdose, and alcohol. Together, these three behavior-related conditions are often referred to as diseases of despair. Asians have by far the lowest rates of death due to drug overdose and alcohol, as well as slightly lower rates of suicide.

African American males in the United States are much more likely to die from homicide than white males. In 2016, the death rate by homicide for African American males was ** per 100,000 population, compared to a rate of just *** per 100,000 population for white males. African American males are twice as likely to die from firearm-related injuries than white males, with handguns involved in the largest share of homicides in the U.S. Homicide as a leading cause of death While the leading causes of death for black and white residents in the U.S. are similar in many ways, there are two distinct differences. Homicide is not in the leading 10 causes of death among whites, but it is the ******* leading cause of death for blacks, accounting for around ***** percent of all deaths in this group. However, suicide is the ***** leading cause of death among whites, while it is not included in the ** leading causes of death for blacks. Death rates Overall, the death rate in the United States is higher among non-Hispanic whites than any other ethnicity. Furthermore, males across all ethnicities in the U.S. have higher death rates than females. The *** leading causes of death for every ethnicity in the U.S. are cancer and heart disease.

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.

The leading causes of death in Massachusetts are cancer, heart disease, unintentional injury, stroke, and chronic lower respiratory disease. These mortality rates tend to be higher for people of color; and Black residents have a higher premature mortality rate overall and Asian residents have a higher rate of mortality due to stroke.

ObjectiveTo examine whether investments made in public health research align with the health burdens experienced by white and black Americans.MethodsIn this cross-sectional study of all deaths in the United States in 2015, we compared the distribution of potential years of life lost (PYLL) across 39 causes of death by race and identified key differences. We examined the relationship between cause-of-death-specific PYLL and key indicators of public health investment (federal funding and number of publications) by race using linear spline models. We also compared the number of courses available at the top schools of public health relevant to the top causes of death contributor to PYLL for black and white Americans.ResultsHomicide was the number one contributor to PYLL among black Americans, while ischemic heart disease was the number one contributor to PYLL among white Americans. Firearm-related violence accounted for 88% of black PYLL attributed to homicide and 71% of white PYLL attributed to homicide. Despite the high burden of PYLL, homicide research was the focus of few federal grants or publications. In comparison, ischemic heart disease garnered 341 grants and 594 publications. The number of public health courses available relevant to homicide (n = 9) was similar to those relevant to ischemic heart disease (n = 10).ConclusionsBlack Americans are disproportionately affected by homicide, compared to white Americans. For both black and white Americans, the majority of PYLL due to homicide are firearm-related. Yet, homicide research is dramatically underrepresented in public health research investments in terms of grant funding and publications, despite available public health training opportunities. If left unchecked, the observed disproportionate distribution of investments in public health resources threatens to perpetuate a system that disadvantages black Americans.

Heart disease is currently the leading cause of death in the United States. In 2022, COVID-19 was the fourth leading cause of death in the United States, accounting for almost six percent of all deaths that year. The leading causes of death worldwide are similar to those in the United States. However, diarrheal diseases and neonatal conditions are major causes of death worldwide, but are not among the leading causes in the United States. Instead, accidents and chronic liver disease have a larger impact in the United States.

Racial differences

In the United States, there exist slight differences in leading causes of death depending on race and ethnicity. For example, assault, or homicide, accounts for around three percent of all deaths among the Black population but is not even among the leading causes of death for other races and ethnicities. However, heart disease and cancer are still the leading causes of death for all races and ethnicities.

Leading causes of death among men vs women

Similarly, there are also differences in the leading causes of death in the U.S. between men and women. For example, among men, intentional self-harm accounts for around two percent of all deaths but is not among the leading causes of death among women. On the other hand, influenza and pneumonia account for more deaths among women than men.

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 race and ethnicity. 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 COVID-19 update.

The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf

Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic.

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 subject to future revision as reporting changes.

Starting in July 2020, this dataset will be updated every weekday.

Additional notes: A delay in the data pull schedule occurred on 06/23/2020. Data from 06/22/2020 was processed on 06/23/2020 at 3:30 PM. The normal data cycle resumed with the data for 06/23/2020.

A network outage on 05/19/2020 resulted in a change in the data pull schedule. Data from 5/19/2020 was processed on 05/20/2020 at 12:00 PM. Data from 5/20/2020 was processed on 5/20/2020 8:30 PM. The normal data cycle resumed on 05/20/2020 with the 8:30 PM data pull. As a result of the network outage, the timestamp on the datasets on the Open Data Portal differ from the timestamp in DPH's daily PDF reports.

Starting 5/10/2021, the date field will represent the date this data was updated on data.ct.gov. Previously the date the data was pulled by DPH was listed, which typically coincided with the date before the data was published on data.ct.gov. This change was made to standardize the COVID-19 data sets on data.ct.gov.

According to the NCHS classification, the leading causes of death are provided for the total Santa Clara County population and by race/ethnicity and sex. Data are for Santa Clara County residents.Data trends are from year 2007 to 2016. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017.METADATA:Notes (String): Lists table title, sourceYear (Numeric): Year of death Category (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).Causes of death (String): Cause-of-death were coded using the Tenth Revision of the International Classification of Diseases codes (ICD-10). Causes are classified according to the Centers for Disease Control and Prevention, National Center for Health Statistics, Leading causes of death methodology.Count (Numeric): Number of deaths per cause of deathPercentage (Numeric): Percentage of deaths per cause of death out of total deaths in that year. Percentage value less than 1 is replaced by '<1'.

As of 2023, the third leading cause of death among teenagers aged 15 to 19 years in the United States was intentional self-harm or suicide, contributing to around 17 percent of deaths among this age group. The leading cause of death at that time was unintentional injuries, contributing to around 38.6 percent of deaths, while 20.7 percent of all deaths in this age group were due to assault or homicide. Cancer and heart disease, the overall leading causes of death in the United States, are also among the leading causes of death among U.S. teenagers. Adolescent suicide in the United States In 2021, around 22 percent of students in grades 9 to 12 reported that they had seriously considered attempting suicide in the past year. Female students were around twice as likely to report seriously considering suicide compared to male students. In 2023, New Mexico had the highest rate of suicides among U.S. teenagers, with around 28 deaths per 100,000 teenagers, followed by Idaho with a rate of 22.5 per 100,000. The states with the lowest death rates among adolescents are New Jersey and New York. Mental health treatment Suicidal thoughts are a clear symptom of mental health issues. Mental health issues are not rare among children and adolescents, and treatment for such issues has become increasingly accepted and accessible. In 2021, around 15 percent of boys and girls aged 5 to 17 years had received some form of mental health treatment in the past year. At that time, around 35 percent of youths aged 12 to 17 years in the United States who were receiving specialty mental health services were doing so because they had thought about killing themselves or had already tried to kill themselves.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

BackgroundA healthy diet, as defined by the US Dietary Guidelines for Americans (DGA), has been associated with lower morbidity and mortality from major chronic diseases in studies conducted in predominantly non-Hispanic white individuals. It is unknown whether this association can be extrapolated to African-Americans and low-income populations.Methods and FindingsWe examined the associations of adherence to the DGA with total and cause-specific mortality in the Southern Community Cohort Study, a prospective study that recruited 84,735 American adults, aged 40–79 y, from 12 southeastern US states during 2002–2009, mostly through community health centers that serve low-income populations. The present analysis included 50,434 African-Americans, 24,054 white individuals, and 3,084 individuals of other racial/ethnic groups, among whom 42,759 participants had an annual household income less than US$15,000. Usual dietary intakes were assessed using a validated food frequency questionnaire at baseline. Adherence to the DGA was measured by the Healthy Eating Index (HEI), 2010 and 2005 editions (HEI-2010 and HEI-2005, respectively). During a mean follow-up of 6.2 y, 6,906 deaths were identified, including 2,244 from cardiovascular disease, 1,794 from cancer, and 2,550 from other diseases. A higher HEI-2010 score was associated with lower risks of disease death, with adjusted hazard ratios (HRs) of 0.80 (95% CI, 0.73–0.86) for all-disease mortality, 0.81 (95% CI, 0.70–0.94) for cardiovascular disease mortality, 0.81 (95% CI, 0.69–0.95) for cancer mortality, and 0.77 (95% CI, 0.67–0.88) for other disease mortality, when comparing the highest quintile with the lowest (all p-values for trend < 0.05). Similar inverse associations between HEI-2010 score and mortality were observed regardless of sex, race, and income (all p-values for interaction > 0.50). Several component scores in the HEI-2010, including whole grains, dairy, seafood and plant proteins, and ratio of unsaturated to saturated fatty acids, showed significant inverse associations with total mortality. HEI-2005 score was also associated with lower disease mortality, with a HR of 0.86 (95% CI, 0.79–0.93) when comparing extreme quintiles. Given the observational study design, however, residual confounding cannot be completely ruled out. In addition, future studies are needed to evaluate the generalizability of these findings to African-Americans of other socioeconomic status.ConclusionsOur results showed, to our knowledge for the first time, that adherence to the DGA was associated with lower total and cause-specific mortality in a low-income population, including a large proportion of African-Americans, living in the southeastern US.

The most recent rate is calculated using ACS population estimates from the previous year, unless otherwise noted. The 2020 rate is calculated using the 2019 ACS population estimates.

Data Source: DC Firearm Injury Surveillance Through Emergency Rooms (FASTER), DC Office of the Chief Medical Examiner (OCME), and American Community Survey (ACS) 1-Year Estimates.

Why This Matters

Firearms are one of the leading causes of death in the United States, and the leading cause of death for American youth.

Gun violence also has a larger impact on communities and families. Knowing someone who died from gun violence is associated with negative mental health outcomes.

Nationally, Black youth are killed by firearm-related incidents at five times the rate of white youth. Segregation and disinvestment in Black communities drives this disparity by reinforcing poverty and failing to provide adequate resources and amenities.

The District Response

The District of Columbia’s Office of Gun Violence Prevention was created in January 2022 to coordinate efforts to prevent gun violence. The Gun Violence Dashboards are updated daily with gun violence data from the previous day.

The Building Block Grants awards funding to community members or organizations to provide innovative programming, activities, resources, and/or services to reduce gun violence in DC neighborhoods.

The Office of Neighborhood Safety and Engagement’s Violence Intervention Initiative is a collaborative community engagement strategy designed to support District residents in reducing gun-related violence in our communities.

In 2021, the leading causes of death in Africa were lower respiratory infections, malaria, and stroke. That year, lower respiratory infections resulted in around 65 deaths per 100,000 population in Africa. Leading causes of death in Africa vs the world Worldwide, the top three leading causes of death in 2021 were heart disease, COVID-19, and stroke. At that time, some of the leading causes of death in Africa, such as lower respiratory infections and stroke, were among the leading causes worldwide, but there were also stark differences in the leading causes of death in Africa compared to the leading causes worldwide. For example, malaria, diarrheal disease, and preterm birth complications were among the top ten leading causes of death in Africa, but not worldwide. Furthermore, HIV/AIDS was the eighth leading cause of death in Africa at that time, but was not among the top ten leading causes worldwide. HIV/AIDS in Africa Although HIV/AIDS impacts every region of the world, Africa is still the region most impacted by this deadly virus. Worldwide, there are around 40 million people currently living with HIV, with about 20.8 million found in Eastern and Southern Africa and 5.1 million in Western and Central Africa. The countries with the highest HIV prevalence worldwide include Eswatini, Lesotho, and South Africa, with the leading 20 countries by HIV prevalence all found in Africa. However, due in part to improvements in education and awareness, the prevalence of HIV in many African countries has decreased. For example, in Botswana, the prevalence of HIV decreased from 26.1 percent to 16.6 percent in the period from 2000 to 2023.

This dataset comprises the third follow-up of the baseline Hispanic EPESE, HISPANIC ESTABLISHED POPULATIONS FOR THE EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1993-1994: ARIZONA, CALIFORNIA, COLORADO, NEW MEXICO, AND TEXAS, and provides information on 1,682 of the original respondents. The Hispanic EPESE collected data on a representative sample of community-dwelling Mexican-American elderly, aged 65 years and older, residing in the five southwestern states of Arizona, California, Colorado, New Mexico, and Texas. The primary purpose of the series was to provide estimates of the prevalence of key physical health conditions, mental health conditions, and functional impairments in older Mexican Americans and to compare these estimates with those for other populations. The Hispanic EPESE attempted to determine whether certain risk factors for mortality and morbidity operate differently in Mexican Americans than in non-Hispanic White Americans, African Americans, and other major ethnic groups. The public-use data cover background characteristics (age, sex, type of Hispanic race, income, education, marital status, number of children, employment, and religion), height, weight, social and physical functioning, chronic conditions, related health problems, health habits, self-reported use of dental, hospital, and nursing home services, and depression. The follow-ups provide a cross-sectional examination of the predictors of mortality, changes in health outcomes, and institutionalization and other changes in living arrangements, as well as changes in life situations and quality of life issues. The vital status of respondents from baseline to this round of the survey may be determined using the Vital Status file (Part 2). This file contains interview dates from the baseline as well as vital status at Wave IV (respondent survived, date of death if deceased, proxy-assisted, proxy-reported cause of death, proxy-true). The first follow-up of the baseline data (Hispanic EPESE Wave II, 1995-1996 [ICPSR 3385]) followed 2,438 of the original 3,050 respondents, and the second follow-up (Hispanic EPESE Wave III, 1998-1999 [ICPSR 4102]) followed 1,980 of these respondents. Hispanic EPESE, 1993-1994 (ICPSR 2851), was modeled after the design of ESTABLISHED POPULATIONS FOR EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1981-1993: EAST BOSTON, MASSACHUSETTS, IOWA AND WASHINGTON COUNTIES, IOWA, NEW HAVEN, CONNECTICUT, AND NORTH CENTRAL NORTH CAROLINA and ESTABLISHED POPULATIONS FOR EPIDEMIOLOGIC STUDIES OF THE ELDERLY, 1996-1997: PIEDMONT HEALTH SURVEY OF THE ELDERLY, FOURTH IN-PERSON SURVEY DURHAM, WARREN, VANCE, GRANVILLE, AND FRANKLIN COUNTIES, NORTH CAROLINA.

Lower respiratory infections were the leading cause of death in Africa in 2021. Lower respiratory infections accounted for 8.6 percent of all deaths in Africa that year, followed by malaria, which was responsible for 6.5 percent of deaths. Although HIV is not one of the leading causes of death worldwide, it remains within the top 10 leading causes of death in Africa. As of 2023, the top 15 countries with the highest prevalence of new HIV infections are all found in Africa. HIV/AIDS HIV (human immunodeficiency virus) is an infectious sexually transmitted disease that is transmitted via exposure to infected semen, blood, vaginal and anal fluids and breast milk. HIV weakens the human immune system, resulting in the affected person being unable to fight off opportunistic infections. HIV/AIDS was the eighth leading cause of death in Africa in 2021, accounting for around 4.6 percent of all deaths, or around 405,790 total deaths. HIV Treatment Although there is currently no effective cure for HIV, death can be prevented by taking HIV antiretroviral therapy (ART). Access to ART worldwide has increased greatly over the last decade; however, there are still barriers to access in some of the countries most impacted by HIV. The African countries with the highest percentage of HIV infected children who were receiving antiretroviral treatment were Eswatini, Lesotho, and Uganda.

THIS DATASET WAS LAST UPDATED AT 7:11 AM EASTERN ON DEC. 1

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.

BackgroundCoronary heart disease (CHD) is the most common cause of death worldwide. Previous studies have identified numerous common CHD susceptibility loci, with the vast majority identified in populations of European ancestry. How well these findings transfer to other racial/ethnic populations remains unclear.Methods and ResultsWe examined the generalizability of the associations with 71 known CHD loci in African American, Latino and Japanese men and women in the Multiethnic Cohort (6,035 cases and 11,251 controls). In the combined multiethnic sample, 78% of the loci demonstrated odds ratios that were directionally consistent with those previously reported (p = 2 × 10−6), with this fraction ranging from 59% in Japanese to 70% in Latinos. The number of nominally significant associations across all susceptibility regions ranged from only 1 in Japanese to 11 in African Americans with the most statistically significant association observed through locus fine-mapping noted for rs3832016 (OR = 1.16, p = 2.5×10−5) in the SORT1 region on chromosome 1p13. Lastly, we examined the cumulative predictive effect of CHD SNPs across populations with improved power by creating genetic risk scores (GRSs) that summarize an individual’s aggregated exposure to risk variants. We found the GRSs to be significantly associated with risk in African Americans (OR = 1.03 per allele; p = 4.1×10−5) and Latinos (OR = 1.03; p = 2.2 × 10−8), but not in Japanese (OR = 1.01; p = 0.11).ConclusionsWhile a sizable fraction of the known CHD loci appear to generalize in these populations, larger fine-mapping studies will be needed to localize the functional alleles and better define their contribution to CHD risk in these populations.

This table contains data on the annual number of fatal and severe road traffic injuries per population and per miles traveled by transport mode, for California, its regions, counties, county divisions, cities/towns, and census tracts. Injury data is from the Statewide Integrated Traffic Records System (SWITRS), California Highway Patrol (CHP), 2002-2010 data from the Transportation Injury Mapping System (TIMS) . The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity]. Transportation accidents are the second leading cause of death in California for people under the age of 45 and account for an average of 4,018 deaths per year (2006-2010). Risks of injury in traffic collisions are greatest for motorcyclists, pedestrians, and bicyclists and lowest for bus and rail passengers. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience 4 times the death rate as Whites or Asians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

This statistic shows the number of deaths from the leading causes of death among U.S. adults aged 65 years and older in 2014, by ethnicity, per 100,000 population. In 2014, the rate of death from heart disease was highest among those aged 65 years and older who were non-Hispanic Black.