In 2024, white Americans remained the largest racial group in the United States, numbering just over 254 million. Black Americans followed at nearly 47 million, with Asians totaling around 23 million. Hispanic residents, of any race, constituted the nation’s largest ethnic minority. Despite falling fertility, the U.S. population continues to edge upward and is expected to reach 342 million in 2025. International migrations driving population growth The United States’s population growth now hinges on immigration. Fertility rates have long been in decline, falling well below the replacement rate of 2.1. On the other hand, international migration stepped in to add some 2.8 million new arrivals to the national total that year. Changing demographics and migration patterns Looking ahead, the U.S. population is projected to grow increasingly diverse. By 2060, the Hispanic population is expected to grow to 27 percent of the total population. Likewise, African Americans will remain the largest racial minority at just under 15 percent.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

About this dataset

Context

This list ranks the 51 states in the United States by Some Other Race (SOR) population, as estimated by the United States Census Bureau. It also highlights population changes in each states over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Other Race Population: This column displays the rank of states in the United States by their Some Other Race (SOR) population, using the most recent ACS data available.
• states: The states for which the rank is shown in the previous column.
• Other Race Population: The Other Race population of the states is shown in this column.
• % of Total states Population: This shows what percentage of the total states population identifies as Other Race. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total U.S. Other Race Population: This tells us how much of the entire United States Other Race population lives in that states. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Explore various maps to learn more about the population in the US based on how people respond to the American Community Survey (ACS). Based on how people responded, we can learn more about where different race and ethnicity groups live throughout the country. The pattern for each map portrays the most current 5-year ACS estimates, and is offered for states, counties, and tracts. Zoom and explore the map to see the patterns in your area.In this collection, you'll find various different topics:The predominant race in each area (which one has the largest count)Race by dot densityPeople of color (non-white population)Percent of the population by each raceWhere is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.

About this dataset

Context

The dataset tabulates the population of Norwood Young America by race. It includes the population of Norwood Young America across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Norwood Young America across relevant racial categories.

Key observations

The percent distribution of Norwood Young America population by race (across all racial categories recognized by the U.S. Census Bureau): 84.31% are white, 5.39% are Black or African American, 6.31% are some other race and 3.99% are multiracial.

https://i.neilsberg.com/ch/norwood-young-america-mn-population-by-race.jpeg" alt="Norwood Young America population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Norwood Young America
• Population: The population of the racial category (excluding ethnicity) in the Norwood Young America is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Norwood Young America total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Norwood Young America Population by Race & Ethnicity. You can refer the same here

In the fiscal year of 2019, 21.39 percent of active-duty enlisted women were of Hispanic origin. The total number of active duty military personnel in 2019 amounted to 1.3 million people.

Ethnicities in the United States The United States is known around the world for the diversity of its population. The Census recognizes six different racial and ethnic categories: White American, Native American and Alaska Native, Asian American, Black or African American, Native Hawaiian and Other Pacific Islander. People of Hispanic or Latino origin are classified as a racially diverse ethnicity.

The largest part of the population, about 61.3 percent, is composed of White Americans. The largest minority in the country are Hispanics with a share of 17.8 percent of the population, followed by Black or African Americans with 13.3 percent. Life in the U.S. and ethnicity However, life in the United States seems to be rather different depending on the race or ethnicity that you belong to. For instance: In 2019, native Hawaiians and other Pacific Islanders had the highest birth rate of 58 per 1,000 women, while the birth rae of white alone, non Hispanic women was 49 children per 1,000 women.

The Black population living in the United States has the highest poverty rate with of all Census races and ethnicities in the United States. About 19.5 percent of the Black population was living with an income lower than the 2020 poverty threshold. The Asian population has the smallest poverty rate in the United States, with about 8.1 percent living in poverty.

The median annual family income in the United States in 2020 earned by Black families was about 57,476 U.S. dollars, while the average family income earned by the Asian population was about 109,448 U.S. dollars. This is more than 25,000 U.S. dollars higher than the U.S. average family income, which was 84,008 U.S. dollars.

About this dataset

Context

The dataset tabulates the population of United States by race. It includes the population of United States across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of United States across relevant racial categories.

Key observations

The percent distribution of United States population by race (across all racial categories recognized by the U.S. Census Bureau): 65.88% are white, 12.47% are Black or African American, 0.84% are American Indian and Alaska Native, 5.77% are Asian, 0.19% are Native Hawaiian and other Pacific Islander, 6.05% are some other race and 8.80% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the United States
• Population: The population of the racial category (excluding ethnicity) in the United States is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Race & Ethnicity. You can refer the same here

BackgroundFew studies have examined weight transitions in contemporary multi-ethnic populations spanning early childhood through adulthood despite the ability of such research to inform obesity prevention, control, and disparities reduction.Methods and ResultsWe characterized the ages at which African American, Caucasian, and Mexican American populations transitioned to overweight and obesity using contemporary and nationally representative cross-sectional National Health and Nutrition Examination Survey data (n = 21,220; aged 2–80 years). Age-, sex-, and race/ethnic-specific one-year net transition probabilities between body mass index-classified normal weight, overweight, and obesity were estimated using calibrated and validated Markov-type models that accommodated complex sampling. At age two, the obesity prevalence ranged from 7.3% in Caucasian males to 16.1% in Mexican American males. For all populations, estimated one-year overweight to obesity net transition probabilities peaked at age two and were highest for Mexican American males and African American females, for whom a net 12.3% (95% CI: 7.6%-17.0%) and 11.9% (95% CI: 8.5%-15.3%) of the overweight populations transitioned to obesity by age three, respectively. However, extrapolation to the 2010 U.S. population demonstrated that Mexican American males were the only population for whom net increases in obesity peaked during early childhood; age-specific net increases in obesity were approximately constant through the second decade of life for African Americans and Mexican American females and peaked at age 20 for Caucasians.ConclusionsAfrican American and Mexican American populations shoulder elevated rates of many obesity-associated chronic diseases and disparities in early transitions to obesity could further increase these inequalities if left unaddressed.

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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.

About this dataset

Context

The dataset tabulates the American Falls population by race and ethnicity. The dataset can be utilized to understand the racial distribution of American Falls.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• American Falls, ID Population Breakdown by Race
• American Falls, ID Non-Hispanic Population Breakdown by Race
• American Falls, ID Hispanic or Latino Population Distribution by Their Ancestries

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/.

In New York City, the population of Chinese Americans has grown faster than that of any other minority racial/ethnic group, and now this community constitutes almost half of all Chinese Americans living in the northeastern United States. Nonetheless, scant research attention has been given to Chinese American ethnic enclaves and little is known about the health status of their residents. This study aims to help address this gap in the literature by: (1) improving our understanding of the spatial settlement of Chinese Americans living in New York City from 2000 to 2016; and (2) assessing associations between a New York City resident's likelihood of living in a Chinese American enclave and their access to health care and perceived health status, two measures of community health. In support of this aim, this study establishes a robust criterion for defining ethnic enclaves at the Census tract level in New York City as the communities of interest in this paper. An ethnic enclave is defined as an area at the Census tract level with high dissimilarity and a spatial cluster of Chinese Americans. The spatial findings were that Chinese Americans in New York City were least segregated from other Asian American residents, somewhat segregated from White residents, and most segregated from Black residents. Also, the population density of Chinese Americans increased since 2000, as reflected by their declining exposure index with other Asian Americans. Results from logistic regression indicated that the probability of living in a Chinese American enclave was negatively associated with positive self-perception of general health and positively associated with delays in receiving health care. For Chinese American residents of New York City, living in an ethnic enclave was also associated with both lower socioeconomic status and poorer community health.

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.

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

US Age-Standardized Stroke Mortality Rates (2013-15) by State/County/Gender/Race

Investigating Variations in Rates

By US Open Data Portal, data.gov [source]

About this dataset

This dataset contains the age-standardized stroke mortality rate in the United States from 2013 to 2015, by state/territory, county, gender and race/ethnicity. The data source is the highly respected National Vital Statistics System. The rates are reported as a 3-year average and have been age-standardized. Moreover, county rates are spatially smoothed for further accuracy. The interactive map of heart disease and stroke produced by this dataset provides invaluable information about the geographic disparities in stroke mortality across America at different scales - county, state/territory and national. By using the adjustable filter settings provided in this interactive map, you can quickly explore demographic details such as gender (Male/Female) or race/ethnicity (e.g Non-Hispanic White). Conquer your fear of unknown with evidence! Investigate these locations now to inform meaningful action plans for greater public health resilience in America and find out if strokes remain a threat to our millions of citizens every day! Updated regularly since 2020-02-26, so check it out now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The US Age-Standardized Stroke Mortality Rates (2013-2015) by State/County/Gender/Race dataset provides valuable insights into stroke mortality rates among adults ages 35 and over in the USA between 2013 and 2015. This dataset contains age-standardized data from the National Vital Statistics System at the state, county, gender, and race level. Use this guide to learn how best use this dataset for your purposes!

Understand the Data

This dataset provides information about stroke mortality rates among adult Americans aged 35+. The data is collected from 2013 to 2015 in three year averages. Even though it is possible to view county level data, spatial smoothing techniques have been applied here. The following columns of data are provided: - Year – The year of the data collection - LocationAbbr – The abbreviation of location where the data was collected
- LocationDesc – A description of this location
- GeographicLevel – Geographic level of granularity where these numbers are recorded * DataSource - source of these statistics * Class - class or group into which these stats fall * Topic - overall topic on which we have stats * Data_Value - age standardized value associated with each row * Data_Value_Unit - units associated with each value * Stratification1– First stratification defined for a given row * Stratification2– Second stratification defined for a given row

Additionally, several other footnotes fields such as ‘Data_value_Type’; ‘Data_Value_Footnote _Symbol’; ‘StratificationCategory1’ & ‘StratificatoinCategory2’ etc may be present accordingly .  

## Exploring Correlations

Now that you understand what individual columns mean it should take no time to analyze correlations within different categories using standard statistical methods like linear regressions or boxplots etc. If you want to compare different regions , then you can use LocationAbbr column with locations reduced geographical levels such as State or Region. Alternatively if one wants comparisons across genders then they can refer column labelled Stratifacation1 alongwith their desired values within this

Research Ideas

• Creating a visualization to show the relationship between stroke mortality and specific variations in race/ethnicity, gender, and geography.
• Comparing two or more states based on their average stroke mortality rate over time.
• Building a predictive model that disregards temporal biases to anticipate further changes in stroke mortality for certain communities or entire states across the US

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:--...

About this dataset

Context

The dataset tabulates the Non-Hispanic population of American Falls by race. It includes the distribution of the Non-Hispanic population of American Falls across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of American Falls across relevant racial categories.

Key observations

Of the Non-Hispanic population in American Falls, the largest racial group is White alone with a population of 2,301 (99.78% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/american-falls-id-population-by-race-and-ethnicity.jpeg" alt="American Falls Non-Hispanic population by race">

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the American Falls
• Population: The population of the racial category (for Non-Hispanic) in the American Falls is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of American Falls total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 American Falls Population by Race & Ethnicity. You can refer the same here

BackgroundLeukocyte telomere length(LTL) has been associated with age, self-reported race/ethnicity, gender, education, and psychosocial factors, including perceived stress, and depression. However, inconsistencies in associations of LTL with disease and other phenotypes exist across studies. Population characteristics, including race/ethnicity, laboratory methods, and statistical approaches in LTL have not been comprehensively studied and could explain inconsistent LTL associations.MethodsLTL was measured using Southern Blot in 1510 participants from a multi-ethnic, multi-center study combining data from 3 centers with different population characteristics and laboratory processing methods. Main associations between LTL and psychosocial factors and LTL and race/ethnicity were evaluated and then compared across generalized estimating equations(GEE) and linear regression models. Statistical models were adjusted for factors typically associated with LTL(age, gender, cancer status) and also accounted for factors related to center differences, including laboratory methods(i.e., DNA extraction). Associations between LTL and psychosocial factors were also evaluated within race/ethnicity subgroups (Non-hispanic Whites, African Americans, and Hispanics).ResultsBeyond adjustment for age, gender, and cancer status, additional adjustments for DNA extraction and clustering by center were needed given their effects on LTL measurements. In adjusted GEE models, longer LTL was associated with African American race (Beta(β)(standard error(SE)) = 0.09(0.04), p-value = 0.04) and Hispanic ethnicity (β(SE) = 0.06(0.01), p-value = 0.02) compared to Non-Hispanic Whites. Longer LTL was also associated with less than a high school education compared to having greater than a high school education (β(SE) = 0.06(0.02), p-value = 0.04). LTL was inversely related to perceived stress (β(SE) = -0.02(0.003), p

This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This map shows which race/ethnicity group has the lowest median income in the United States by tract, county and state, using the latest available data from the U.S. Census Bureau's American Community Survey (ACS).For each group showing a median income figure, the lowest median income determines the color used on the map. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. The map's topic is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. To see the full list of attributes available in this map's layers, go to a layer listed under the "Layers" section below and choose the "Data" tab for that layer, and choose "Fields" at the top right on that page.

About this dataset

Context

The dataset tabulates the population of Earth by race. It includes the population of Earth across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Earth across relevant racial categories.

Key observations

The percent distribution of Earth population by race (across all racial categories recognized by the U.S. Census Bureau): 60.83% are white, 3.52% are Black or African American, 4.59% are American Indian and Alaska Native, 2.77% are some other race and 28.28% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Earth
• Population: The population of the racial category (excluding ethnicity) in the Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Earth total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Earth Population by Race & Ethnicity. You can refer the same here

In 2024 the median annual income of Asian households in the United States was 121,700 U.S. dollars. They were followed by White households, who's median earnings were 92,530 U.S. dollars. Furthermore, Black Americans and American Indian and Alaska Native families had the lowest household incomes. That year, median income among all U.S. household rose to 83,730 U.S. dollars.