In 2022, the most significant cause of death among men in the United States was heart disease, which contributed to 22.5 percent of deaths. COVID-19 was the third leading cause of death among U.S. men in both 2020 and 2021, and the fourth leading cause in 2022. This statistic shows the distribution of the 10 leading causes of death among men in the United States from 2020 to 2022.

The leading causes of death in the United States are heart disease and cancer. However, in 2022, COVID-19 was the fourth leading cause of death in the United States, accounting for around six percent of all deaths that year. In 2022, there were around 45 deaths from COVID-19 per 100,000 population.

Cardiovascular disease

Deaths from cardiovascular disease are more common among men than women but have decreased for both sexes over the past few decades. Coronary heart disease accounts for the highest portion of cardiovascular disease deaths in the United States, followed by stroke and high blood pressure. The states with the highest death rates from cardiovascular disease include Oklahoma, Mississippi, and Alabama. Smoking tobacco, physical inactivity, poor diet, stress, and being overweight or obese are all risk factors for developing heart disease.

Cancer

Although cancer is the second leading cause of death in the United States, like deaths from cardiovascular disease, deaths from cancer have decreased over the last few decades. The highest death rates from cancer come from lung cancer for both men and women. Breast cancer is the second deadliest cancer for women, while prostate cancer is the second deadliest cancer for men. West Virginia, Mississippi, and Kentucky lead the nation with the highest cancer death rates.

In the United States, the leading causes of death among women are heart disease and cancer. Heart disease and cancer are similarly the leading causes of death among U.S. men. In 2022, heart disease accounted for **** percent of all deaths among women in the United States, while cancer accounted for **** percent of deaths. COVID-19 was the third leading cause of death among both men and women in 2020 and 2021, and the fourth leading cause in 2022. Cancer among women in the U.S. The most common types of cancer among U.S. women are breast, lung and bronchus, and colon and rectum. In 2025, there were around ******* new breast cancer cases among women, compared to ******* new cases of lung and bronchus cancer. Although breast cancer is the most common form of cancer among women in the United States, lung and bronchus cancer causes the highest number of cancer deaths. In 2025, around ****** women were expected to die from lung and bronchus cancer, compared to ****** from breast cancer. Breast cancer Although breast cancer is the second most deadly form of cancer among women, rates of death have decreased over the past few decades. This decrease is possibly due to early detection, progress in therapy, and increasing awareness of risk factors. In 2023, the death rate due to breast cancer was **** per 100,000 population, compared to a rate of **** per 100,000 in the year 1990. The state with the highest rate of deaths due to breast cancer is Oklahoma, while South Dakota had the lowest rates.

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.

In 2023, malignant neoplasms were the leading cause of death among the male population in Japan at around ***** thousand cases. This number accounted for approximately **** percent of about ***** thousand death cases of men recorded in the country during that year. Heart diseases, excluding hypertensive, followed with a share of around ** percent. Malignant neoplasmsIn recent years, malignant neoplasms have been the leading cause of death for both female and male populations in Japan. The most frequent cause of cancerous tumor related deaths has continued to be lung cancer for both men and women. As smoking and passive smoking are some of the main causes of lung cancer, the health ministry in Japan set the goal of reducing the smoking rate of adults from around ** to ** percent by 2022. To minimize the risk of passive smoking, the government also amended the Health Promotion Act and prohibited smoking in public facilities, offices, most restaurants, and public areas starting from April 2020. SuicideOne of the leading causes of death specific to men in Japan was suicide. In the last decade, the number of suicides committed by men in Japan remained roughly double the number of those committed by women. While close to half of the suicides in Japan were committed due to health reasons in previous years, the number of suicides owning to work-related problems has also become a serious social issue in the current Japanese society. One of the reason behind it is said to be the working condition of employees in Japan with a severe workload. The government has been aiming to reduce working hours and overtime to improve the working conditions of workers in Japan.

In 2021, approximately ****** male Filipinos succumbed to death due to ischaemic heart diseases. Ischaemic heart disease was also the leading cause of death among female population in the Philippines.

In 2023, the leading causes of death in Canada were malignant neoplasms (cancer) and diseases of the heart. Together, these diseases accounted for around ** percent of all deaths in Canada that year. COVID-19 was the sixth leading cause of death in Canada in 2023 with *** percent of deaths. The leading causes of death in Canada In 2023, around ****** people in Canada died from cancer, making it by far the leading cause of death in the country. In comparison, an estimated ****** people died from diseases of the heart, while ****** died from accidents. In 2023, the death rate for diabetes mellitus was **** per 100,000 population, making it the seventh leading cause of death. Diabetes is a growing problem in Canada, with around ***** percent of the population diagnosed with the disease as of 2023. What is the deadliest form of cancer in Canada? In Canada, lung and bronchus cancer account for the largest share of cancer deaths, followed by colorectal cancer. In 2023, the death rate for lung and bronchus cancer was **** per 100,000 population, compared to **** deaths per 100,000 population for colorectal cancer. However, although lung and bronchus cancer are the deadliest cancers for both men and women in Canada, breast cancer is the second-deadliest cancer among women, accounting for **** percent of all cancer deaths. Colorectal cancer is the second most deadly cancer among men in Canada, followed by prostate cancer. In 2023, colorectal cancer accounted for around **** percent of all cancer deaths among men in Canada, while prostate cancer was responsible for **** percent of such deaths.

The graph illustrates the number of deaths per day in the United States from 1950 to 2025. The x-axis represents the years, abbreviated from '50 to '24, while the y-axis indicates the daily number of deaths. Over this 75-year period, the number of deaths per day ranges from a low of 4,054 in 1950 to a high of 9,570 in 2021. Notable figures include 6,855 deaths in 2010 and 8,333 in 2024. The data shows a general upward trend in daily deaths over the decades, with recent years experiencing some fluctuations. This information is presented in a line graph format, effectively highlighting the long-term trends and yearly variations in daily deaths across the United States.

This dataset presents the footprint of male cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

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.

In 2023, most male deaths in Germany were caused by chronic ischaemic heart disease, with 41.4 thousand deaths. It is worth to point out that among the 10 leading causes of death, three were related to cardiovascular complications, summing up to a total of 82.8 thousand deaths. This statistic shows the leading causes of male death in Germany in 2023, by type of disease.

In 2022, heart disease was the leading cause of death among Hispanics in the United States, accounting for **** percent of deaths, followed by cancer and accidents, which accounted for **** and **** percent of all deaths, respectively. COVID-19, cerebrovascular diseases, and diabetes were also among the top 10 leading causes of death among Hispanics in the United States. Cancer among Hispanics Cancer (malignant neoplasms) is the second leading cause of death in the United States as of 2022. In 2021, there were ****** deaths among Hispanic males attributed to cancer, compared to ****** deaths among Hispanic females. The most common treatments for cancer include surgery, chemotherapy, and radiation. Hispanic healthcare Access to health care and health insurance remains a problem for many Hispanics in the United States. In 2022, around ** percent of Hispanic men reported having no personal doctor or health care provider. Furthermore, as of 2022, around ** percent of children without health insurance were Hispanic.

About this dataset

Context

The dataset tabulates the population of United States by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for United States. The dataset can be utilized to understand the population distribution of United States by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in United States. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for United States.

Key observations

Largest age group (population): Male # 30-34 years (11.65 million) | Female # 30-34 years (11.41 million). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the United States population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the United States is shown in the following column.
• Population (Female): The female population in the United States is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in United States for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Gender. You can refer the same here

San Marino Nonpregnant and Nonnursing Women Can Do the Same Jobs as Men: 1=Yes; 0=No data was reported at 1.000 NA in 2017. This stayed constant from the previous number of 1.000 NA for 2015. San Marino Nonpregnant and Nonnursing Women Can Do the Same Jobs as Men: 1=Yes; 0=No data is updated yearly, averaging 1.000 NA from Dec 2015 (Median) to 2017, with 2 observations. The data reached an all-time high of 1.000 NA in 2017 and a record low of 1.000 NA in 2017. San Marino Nonpregnant and Nonnursing Women Can Do the Same Jobs as Men: 1=Yes; 0=No data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s San Marino – Table SM.World Bank: Policy and Institutions. Non-pregnant and non-nursing women can do the same jobs as men indicates whether there are specific jobs that women explicitly or implicitly cannot perform except in limited circumstances. Both partial and full restrictions on women’s work are counted as restrictions. For example, if women are only allowed to work in certain jobs within the mining industry, e.g., as health care professionals within mines but not as miners, this is a restriction.; ; World Bank: Women, Business and the Law.; ;

Footnotes: 1 Sources: Statistics Canada, Canadian Vital Statistics, Birth, Death and Stillbirth Databases. The table 13-10-0110-01 is an update of table 13-10-0408-01. 2 Infant mortality corresponds to the death of a child under one year of age. Expressed as a rate per 1,000 live births. 3 Perinatal deaths include late fetal deaths (stillbirths with a gestational age of 28 weeks or more) and early neonatal deaths (deaths of infants aged less than one week). 4 Numbers and rates in this table may differ from those found in similar data published by the Vital Statistics program as the data here have been tabulated based on postal codes available for place of residence. 5 2017 data for Yukon are not available. 6 The number of births, stillbirths, and deaths in Ontario for 2016 and 2017 are considered preliminary. 7 Due to improvements in methodology and timeliness, the duration of data collection has been shortened compared to previous years. As a result, there may have been fewer births and stillbirths captured by the time of the release. The 2017 data are therefore considered preliminary. 8 A census metropolitan area (CMA) is an area consisting of one or more adjacent municipalities situated around a major urban core. To form a census metropolitan area, the urban core must have a population of at least 100,000. The CMAs are those defined for the 2016 Census. To form a census agglomeration, the urban core must have a population of at least 10,000. 9 The metropolitan influenced zone (MIZ) classification is an approach to better differentiate areas of Canada outside of census metropolitan areas and census agglomerations. Census subdivisions that lie outside these areas are classified into one of four zones of influence. They are assigned to categories based on the flow of residents travelling to work in an urban area with a population greater than 10,000. Municipalities where more that 30% of the residents commute to work in an urban core are assigned to the strong MIZ category. Municipalities where between 5% and 30% of the residents commute to work in an urban core are assigned to the moderate MIZ category. Municipalities where between 0% and 5% of the residents commute to work in an urban core are assigned to the weak MIZ category. Municipalities where fewer than 40 or none of the residents commute to work in an urban core are assigned to the zero MIZ category. 10 Geographical areas are modified every 5 years to reflect the most recent census definitions, therefore, data are not strictly comparable historically. 11 Counts and rates in this table are based on three consecutive years of data. 12 The 95% confidence interval (CI) illustrates the degree of variability associated with a rate. 13 Wide confidence intervals (CIs) indicate high variability, thus, these rates should be interpreted and compared with due caution. 14 The following standard symbols are used in this Statistics Canada table: (..) for figures not available for a specific reference period, (...) for figures not applicable and (x) for figures suppressed to meet the confidentiality requirements of the Statistics Act. 15 The figures shown in the tables have been subjected to a confidentiality procedure known as controlled rounding to prevent the possibility of associating statistical data with any identifiable individual. Under this method, all figures, including totals and margins, are rounded either up or down to a multiple of 5. Controlled rounding has the advantage over other types of rounding of producing additive tables as well as offering more protection.

As of 2022, the third leading cause of death among teenagers aged 15 to 19 years in the United States was intentional self-harm or suicide, contributing around 17 percent of deaths among age group. The leading cause of death at that time was unintentional injuries, contributing to around 37.4 percent of deaths, while 21.8 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 2022, Montana had the highest rate of suicides among U.S. teenagers with around 39 deaths per 100,000 teenagers, followed by South Dakota with a rate of 33 per 100,000. The states with the lowest death rates among adolescents are New York and New Jersey. 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.

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

The number of road traffic casualties divided by the population of the area in thousands multiplied by 100. This indicator contains data from all ‘types’ of road user, including pedestrians, pedal cyclists, motorcyclists, car users, and other vehicle users. Motor vehicle traffic accidents are a major cause of preventable deaths and morbidity, particularly in younger age groups. For children and for men aged 20-64 years, mortality rates for motor vehicle traffic accidents are higher in lower socioeconomic groups. For instance, there would be 600 fewer deaths in men aged 20-64 years from motor vehicle traffic accidents each year if all men had the same death rates as those in social classes I and II combined. There is evidence that some groups, like children, old people and potential cyclists, avoid roads because they are dangerous, which can reduce casualties but lower the quality of life. Ideally, casualty data need to be combined with other information. For example, a rise in journeys on foot and bicycle combined with a fall in accidents would indicate real progress. One of the Department for Transport’s PSA targets is to reduce the number of people killed or seriously injured in Great Britain in road accidents by 40 % by 2010 and the number of children killed or seriously injured by 50 % by 2010, compared with the averages for 1994-1998. Legacy unique identifier: P01049

About this dataset

Context

The dataset tabulates the population of New Orleans by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for New Orleans. The dataset can be utilized to understand the population distribution of New Orleans by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in New Orleans. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for New Orleans.

Key observations

Largest age group (population): Male # 35-39 years (15,110) | Female # 30-34 years (17,105). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the New Orleans population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the New Orleans is shown in the following column.
• Population (Female): The female population in the New Orleans is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in New Orleans for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for New Orleans Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Broad Top City by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Broad Top City. The dataset can be utilized to understand the population distribution of Broad Top City by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Broad Top City. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Broad Top City.

Key observations

Largest age group (population): Male # 25-29 years (33) | Female # 45-49 years (23). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Broad Top City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Broad Top City is shown in the following column.
• Population (Female): The female population in the Broad Top City is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Broad Top City for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Broad Top City Population by Gender. You can refer the same here