This graph shows the population of the U.S. by race and ethnic group from 2000 to 2023. In 2023, there were around 21.39 million people of Asian origin living in the United States. A ranking of the most spoken languages across the world can be accessed here. U.S. populationCurrently, the white population makes up the vast majority of the United States’ population, accounting for some 252.07 million people in 2023. This ethnicity group contributes to the highest share of the population in every region, but is especially noticeable in the Midwestern region. The Black or African American resident population totaled 45.76 million people in the same year. The overall population in the United States is expected to increase annually from 2022, with the 320.92 million people in 2015 expected to rise to 341.69 million people by 2027. Thus, population densities have also increased, totaling 36.3 inhabitants per square kilometer as of 2021. Despite being one of the most populous countries in the world, following China and India, the United States is not even among the top 150 most densely populated countries due to its large land mass. Monaco is the most densely populated country in the world and has a population density of 24,621.5 inhabitants per square kilometer as of 2021. As population numbers in the U.S. continues to grow, the Hispanic population has also seen a similar trend from 35.7 million inhabitants in the country in 2000 to some 62.65 million inhabitants in 2021. This growing population group is a significant source of population growth in the country due to both high immigration and birth rates. The United States is one of the most racially diverse countries in the world.

This map shows a comparison of diversity and median household income in the US by tract. Esri's Diversity Index measures the likelihood that two persons, chosen at random from the same area, belong to different races or ethnic groups. In theory, the index ranges from 0 (no diversity) to 100 (complete diversity). If an area's entire population is divided evenly into two race groups and one ethnic group, then the diversity index equals 50. As more race groups are evenly represented in the population, the diversity index increases. Minorities accounted for 30.9 percent of the population in 2000 and are expected to make up 42.3 percent of the population by 2023. Vintage of data: 2023Areas in a darker orange are less diverse than light blue areas with higher diversity. Median household income is symbolized by size. The national median household income is $58,100 and any household below the national value has the smallest symbol size. The largest size has a median household income over $100,000 per year. Esri Updated Demographics represent the suite of annually updated U.S. demographic data that provides current-year and five-year forecasts for more than two thousand demographic and socioeconomic characteristics, a subset of which is included in this layer. Included are a host of tables covering key characteristics of the population, households, housing, age, race, income, and much more. Esri's Updated Demographics data consists of point estimates, representing July 1 of the current and forecast years.Esri Updated Demographics DocumentationMethodologyUnderstanding Esri’s Updated Demographics portfolioEssential Esri Demographics vocabularyThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. This layer requires an ArcGIS Online subscription and does not consume credits. Please cite Esri when using this data.

According to a 2022 survey, approximately ** percent of nonprofit organizations in the U.S. offered specialized services for Black or African American people that year. Additionally, ** percent of organizations provided services for Asian or Asian American groups, and ** percent for non-Hispanic white communities in 2022.

To assess differences in psychological outcomes as well as risk and protective factors for these outcomes among several USA ethnic groups and identify correlates of these psychological outcomes among adults with diabetes in the second Diabetes Attitudes, Wishes and Needs (DAWN2) study. The core USA DAWN2 sample was supplemented by independent samples of specific ethnic minority groups, yielding a total of 447 White non-Hispanics, 241 African Americans, 194 Hispanics, and 173 Chinese Americans (n = 1055). Multivariate analysis examined ethnic differences in psychological outcomes and risk/protective factors (disease, demographic and socioeconomic factors, health status and healthcare access/utilization, subjective burden of diabetes and social support/burden). Separate analyses were performed on each group to determine whether risk/protective factors differed across ethnic groups. Psychological outcomes include well-being, quality of life, impact of diabetes on life domains, diabetes distress, and diabetes empowerment. NCT01507116. Ethnic minorities tended to have better psychological outcomes than White non-Hispanics, although their diabetes distress was higher. Levels of most risk and protective factors differed significantly across ethnic groups; adjustment for these factors reduced ethnic group differences in psychological outcomes. Health status and modifiable diabetes-specific risk/protective factors (healthcare access/utilization, subjective diabetes burden, social support/burden) had strong associations with psychological outcomes, especially diabetes distress and empowerment. Numerous interactions between ethnicity and other correlates of psychological outcomes suggest that ethnic groups are differentially sensitive to various risk/protective factors. Potential limitations are the sample sizes and representativeness. Ethnic groups differ in their psychological outcomes. The risk/protective factors for psychological outcomes differ across ethnic groups and different ethnic groups are more/less sensitive to their influence. These findings can aid the development of strategies to overcome the most prominent and influential psychosocial barriers to optimal diabetes care within each ethnic group.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

This map service summarizes racial and ethnic diversity in the United States in 2012.

The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). Diversity in the U.S. population is increasing. The diversity score for the entire United States in 2012 is 61.

The data shown is from Esri's 2012 Updated Demographics. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. This map shows Esri's 2012 estimates using Census 2010 geographies.

This survey of minority groups was part of a larger project to investigate the patterns, predictors, and consequences of midlife development in the areas of physical health, psychological well-being, and social responsibility. Conducted in Chicago and New York City, the survey was designed to assess the well-being of middle-aged, urban, ethnic minority adults living in both hyper-segregated neighborhoods and in areas with lower concentrations of minorities. Respondents' views were sought on issues relevant to quality of life, including health, childhood and family background, religion, race and ethnicity, personal beliefs, work experiences, marital and close relationships, financial situation, children, community involvement, and neighborhood characteristics. Questions on health explored the respondents' physical and emotional well-being, past and future attitudes toward health, physical limitations, energy level and appetite, amount of time spent worrying about health, and physical reactions to those worries. Questions about childhood and family background elicited information on family structure, the role of the parents with regard to child rearing, parental education, employment status, and supervisory responsibilities at work, the family financial situation including experiences with the welfare system, relationships with siblings, and whether as a child the respondent slept in the same bed as a parent or adult relative. Questions on religion covered religious preference, whether it is good to explore different religious teachings, and the role of religion in daily decision-making. Questions about race and ethnicity investigated respondents' backgrounds and experiences as minorities, including whether respondents preferred to be with people of the same racial group, how important they thought it was to marry within one's racial or ethnic group, citizenship, reasons for moving to the United States and the challenges faced since their arrival, their native language, how they would rate the work ethic of certain ethnic groups, their views on race relations, and their experiences with discrimination. Questions on personal beliefs probed for respondents' satisfaction with life and confidence in their opinions. Respondents were asked whether they had control over changing their life or their personality, and what age they viewed as the ideal age. They also rated people in their late 20s in the areas of physical health, contribution to the welfare and well-being of others, marriage and close relationships, relationships with their children, work situation, and financial situation. Questions on work experiences covered respondents' employment status, employment history, future employment goals, number of hours worked weekly, number of nights away from home due to work, exposure to the risk of accident or injury, relationships with coworkers and supervisors, work-related stress, and experience with discrimination in the workplace. A series of questions was posed on marriage and close relationships, including marital status, quality and length of relationships, whether the respondent had control over his or her relationships, and spouse/partner's education, physical and mental health, employment status, and work schedule. Questions on finance explored respondents' financial situation, financial planning, household income, retirement plans, insurance coverage, and whether the household had enough money. Questions on children included the number of children in the household, quality of respondents' relationships with their children, prospects for their children's future, child care coverage, and whether respondents had changed their work schedules to accommodate a child's illness. Additional topics focused on children's identification with their culture, their relationships with friends of different backgrounds, and their experiences with racism. Community involvement was another area of investigation, with items on respondents' role in child-rearing, participation on a jury, voting behavior, involvement in charitable organizations, volunteer experiences, whether they made monetary or clothing donations, and experiences living in an institutional setting or being homeless. Respondents were also queried about their neighborhoods, with items on neighborhood problems including racism, vandalism, crime, drugs, poor schools, teenag

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

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined differences in youth's mental health and substance abuse needs in seven different racial/ethnic groups of justice-involved youth. Using de-identified data from the Survey of Youth in Residential Placement (SYRP), it was assessed whether differences in mental health and substance abuse needs and services existed in a racially/ethnically diverse sample of youth in custody. Data came from a nationally representative sample of 7,073 youth in residential placements across 36 states, representing five program types. An examination of the extent to which there were racial/ethnic disparities in the delivery of services in relation to need was also conducted. This examination included assessing the differences in substance-related problems, availability of substance services, and receipt of substance-specific counseling. One SAS data file (syrp2017.sas7bdat) is included as part of this collection and has 138 variables for 7073 cases, with demographic variables on youth age, sex, race and ethnicity. Also included as part of the data collection are two SAS Program (syntax) files for use in secondary analysis of youth mental health and substance use.

In 2024, as in 2023, approximately 12 percent of Fortune 500 companies' chief marketing officers (CMOs) in the United States belonged to historically underrepresented racial or ethnic groups. In 2022, the share stood at 14 percent. Meanwhile, the percentage of women among Fortune 500 CMOs in the U.S. increased.

Scientific conferences incorporate diversity-focused events into their programming to increase their diversity and inclusivity and to improve the conference experience for scientists from underrepresented groups (URGs). While simply adding diversity-focused events to conferences is positive, maximizing their impact requires that conferences organizeand schedule these events to minimize well-acknowledged, problematic patterns such as the minority tax. To our knowledge, the programming of diversity-focused events at conferences has not been systematically reviewed to identify the extent of these shortcomings and how they can be addressed. This dataset describes temporal trends in the types of diversity-focused events held at biology conferences, the targeted audiences of those events, and scheduling conflicts that occur with each event. Methods Time-series: We gathered publicly available conference programs for the selected biology conferences (Table 1) for the years 2010 through 2019. Not all conferences had programs available for all years, particularly as time from the present increased, thus sample sizes varied across the time series from 17 to 28. Programs were searched for diversity-focused events by both reading through the entire program and conducting keyword searches. The following keywords were used: diversity, gender, female, woman, women, black, race, ethnic*, minorit*, inclusiv*, LGBT*, where asterisks indicate wild-card search terms. For each program, we first scored (yes/no) on whether there were any diversity-focused events. We then scored whether each event was “women-focused” - where the event was specific to women; “ethnic/racial minority groups-focused” – where the event was specific to any URG based on ethnicity and/or race; and/or “LGBTQ+-focused” - where the event was specific to any part of the LGBTQ+ community. Using these scores, we calculated for each calendar year the percent of conferences with (1) any kind of diversity-focused event, (2) women-focused events, (3) ethnic/racial minorities-focused events, and (4) LGBTQ+-focused events. Table 1. Biology conferences were acquired from a list of societies affiliated with the American Association for the Advancement of Science (https://www.aaas.org/group/60/list-aaas-affiliates). We included a conference if its primary focus was on the biological sciences, regardless of whether the conference was hosted by an academic, professional, or not-for-profit organization. Recent publicly available conference programs were used to examine how conferences incorporated diversity-focused events into their schedules.

Society/Conference

Year analyzed

Society/Conference

Year analyzed

American Dairy Science Association

2018

Ecological Society of America

2019

American Ornithological Society

2018

Entomological Society of America

2018

American Physiological Society

2018

International Biometrics Society - Eastern North America

2018

American Phytopathological Society

2018

Microscopy Society of America

2018

American Society for Horticultural Science

2018

Mycological Society of America

2017

American Society for Microbiology

2019

Phycological Society of America

2019

American Society of Agronomy

2018

Poultry Science Association

2018

American Society of Mammalogists

2018

Society for Integrative and Comparative Biology

2018

American Society of Plant Biologists

2019

Society for Neuroscience

2018

Animal Behavior Society

2019

Society for the Study of Evolution

2018

Association for the Sciences of Limnology and Oceanography - Ocean Sciences Meeting

2018

Society of American Foresters

2019

Association of Southeastern Biologists

2018

Society of Toxicology

2018

Behavior Genetics Association

2018

The Wildlife Society

2018

Biophysical Society

2018

Weed Science Society of America

2018

Botanical Society of America

2018

Survey of event-scheduling and targeted audiences: Using one recent program from each conference (years 2017 through 2019), we searched for diversity-focused events by both reading through the entire program and conducting keyword searches. The keywords used are listed above in the Time Series section. From these searches, we found 87 diversity-focused events from 21 out of the 29 conferences. Target audience: For each conference, we used the title and any other description of the event to classify the targeted audience as either an underrepresented group (URG) or the broader conference community. For example, events with titles such as “Inclusive Teaching Workshop” were classified as broadly targeted, whereas events with titles such as “Minority Social” were classified as URG-targeted. However, if any event contained the explicit statement that “all are welcome” (or similar), the event was classified as targeted at the broader conference community. Event format: We also used the titles and other event descriptions to classify the formats of events. Events were classified as socials, workshops, symposia, plenary lectures, forums and town halls, orientations, or poster sessions. The most common events were socials, workshops, and symposia (e.g., “LGBTQ+ Networking Event and Social”, “Workshop for Creating an Inclusive Research Environment”, and “Symposium Honoring the Roles of Women in Microbiology”, respectively). Breaks or scientific sessions: We used the conference schedule to identify whether each diversity-focused event occurred during a scheduled break versus the main scientific sessions. We defined a break as a period that was either explicitly labeled as a break (e.g., lunch, dinner) or occurred outside the daily start or end of conference-wide scientific events, which included workshops, plenary lectures, poster sessions, and contributed oral presentations. Number of conflicting events: We used the conference schedule to count the number of events that overlapped with each diversity-focused event for more than 15 minutes. Events were only counted as separate events if they occurred in separate rooms. “Business” events and other closed, invitation-only events were not included in this calculation. Overlap for an average conference event: Because the baseline number of overlapping events can vary with the size of a conference, we conducted a randomized survey to calculate how many events overlapped with an “average” event at a conference. For each day of a conference, we used a random number generator to identify a single hour with conference activity and counted the number of overlapping events within the first 15 minutes of that hour. The number of events conflicting with an average event was calculated as the total number of overlapping events minus 1. This number was averaged across the different days for each conference. To validate our randomized survey, we also contacted the organizers of each conference to request attendance numbers for the surveyed years - 15 conferences provided this information. Conflict with an average event was strongly correlated with the size of the conference, thus, we concluded that our method of random surveys was a reliable method for quantifying how busy a conference was.

This national, tract-level experienced racial segregation dataset uses data for over 66 million anonymized and opted-in devices in Cuebiq’s Spectus Clean Room data to estimate 15 minute time overlaps of device stays in 38.2m x 19.1m grids across the United States in 2022. We infer a probability distribution of racial backgrounds for each device given their home Census block groups at the time of data collection, and calculate the probability of a diverse social contact during that space and time. These measures are then aggregated to the Census tract and across the whole time period in order to preserve privacy and develop a generalizable measure of the diversity of a place. We propose that this dataset is a better measurement of the segregation and diversity as it is experienced, which we show diverges from standard measurements of segregation. The data can be used by researchers to better understand the determinants of experienced segregation; beyond research, we suggest this data can be used by policy makers to understand the impacts of policies designed to encourage social mixing and access to opportunities such as affordable housing and mixed-income housing, and more.

For the purposes of enhanced privacy, home census block groups were pre-calculated by the data provider, and all calculations are done at the Census tract, with tracts that have more than 20 unique devices over the period of analysis.

This layer summarizes racial and ethnic diversity in the United States. The Diversity Index shows the likelihood that two persons chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity).The data shown is from Esri's 2020 Updated Demographic estimates using Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data. Esri's U.S. Updated Demographic (2020/2025) Data: Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Financial overview and grant giving statistics of American Diversity Group Inc.

IntroductionDementia is characterized by significant declines in cognitive, physical, social, and behavioral functioning, and includes multiple subtypes that differ in etiology. There is limited evidence of the influence of psychiatric and substance use history on the risk of dementia subtypes among older underrepresented racial/ethnic minorities in the United States. Our study explored the role of psychiatric and substance use history on the risk of etiology-specific dementias: Alzheimer’s disease (AD) and vascular dementia (VaD), in the context of a racially and ethnically diverse sample based on national data.MethodsWe conducted secondary data analyses based on the National Alzheimer’s Coordinating Center Uniform Data Set (N = 17,592) which is comprised a large, racially, and ethnically diverse cohort of adult research participants in the network of US Alzheimer Disease Research Centers (ADRCs). From 2005 to 2019, participants were assessed for history of five psychiatric and substance use disorders (depression, traumatic brain injury, other psychiatric disorders, alcohol use, and other substance use). Cox proportional hazard models were used to examine the influence of psychiatric and substance use history on the risk of AD and VaD subtypes, and the interactions between psychiatric and substance use history and race/ethnicity with adjustment for demographic and health-related factors.ResultsIn addition to other substance use, having any one type of psychiatric and substance use history increased the risk of developing AD by 22–51% and VaD by 22–53%. The risk of other psychiatric disorders on AD and VaD risk varied by race/ethnicity. For non-Hispanic White people, history of other psychiatric disorders increased AD risk by 27%, and VaD risk by 116%. For African Americans, AD risk increased by 28% and VaD risk increased by 108% when other psychiatric disorder history was present.ConclusionThe findings indicate that having psychiatric and substance use history increases the risk of developing AD and VaD in later life. Preventing the onset and recurrence of such disorders may prevent or delay the onset of AD and VaD dementia subtypes. Prevention efforts should pay particular attention to non-Hispanic White and African American older adults who have history of other psychiatric disorders.Future research should address diagnostic shortcomings in the measurement of such disorders in ADRCs, especially with regard to diverse racial and ethnic groups.

Background: There is an incomplete understanding of disparities in emergency care for children across racial and ethnic groups in the United States. In this project, we sought to investigate patterns in emergency care utilization, disposition, and resource use in children by race and ethnicity after adjusting for demographic, socioeconomic, and clinical factors.Methods: In this cross-sectional study of emergency department (ED) data from the nationally representative National Hospital Ambulatory Medical Survey (NHAMCS), we examined multiple dimensions of ED care and treatment from 2005 to 2016 among children in the United States. The main outcomes include ED disposition (hospital admission, ICU admission, and in hospital death), resources utilization (medical imaging use, blood tests, and procedure use) and patient ED waiting times and total length of ED stay. The main exposure variable is race/ethnicity, categorized as non-Hispanic white (white), non-Hispanic black (Black), Hispanic, Asian, and Other. Analyses were stratified by race/ethnicity and adjusted for demographic, socioeconomic, and clinical factors.Results: There were 78,471 pediatric (≤18 years old) ED encounters, providing a weighted sample of 333,169,620 ED visits eligible for analysis. Black and Hispanic pediatric patients were 8% less likely (aOR 0.92, 95% CI 0.91–0.92) and 14% less likely (aOR 0.86, CI 0.86–0.86), respectively, than whites to have their care needs classified as immediate/emergent. Blacks and Hispanics were also 28 and 3% less likely, respectively, than whites to be admitted to the hospital following an ED visit (aOR 0.72, CI 0.72–0.72; aOR 0.97, CI 0.97–0.97). Blacks and Hispanics also experienced significantly longer wait times and overall visits as compared to whites.Conclusions: Black and Hispanic children faced disparities in emergency care across multiple dimensions of emergency care when compared to non-Hispanic white children, while Asian children did not demonstrate such patterns. Further research is needed to understand the underlying causes and long-term health consequences of these divergent patterns of racial disparities in ED care within an increasingly racially diverse cohort of younger Americans.

Race and ethnicity are fluid self-identities in the United States, particularly among immigrants, who often redefine their racial and ethnic self-identification as they navigate assimilation and cultural integration. This study uses repeated cross-sectional data from the 2000–2021 American Community Surveys to examine the specific racial and ethnic groups among U.S. immigrants that experienced substantial increases in self-identification. Given that fixed immigration cohorts typically decline in size over time due to emigration and mortality, any observed increase within a cohort indicates individuals reclassifying their reported identity. By controlling for the year of entry into the United States, this analysis employs ordinary least squares (OLS) regressions to estimate annual changes in size and percentage across 46 racial and ethnic categories. The analysis reveals significant increases in identification with multiracial whites and single-race or multiracial “Write-In” groups—categories not printed in the survey questionnaire. These findings underscore the fluidity and complexity of ethnic identities and highlight a shift from broad racial classifications to more specific identities that reflect heritage more accurately. These insights contribute to a broader understanding of identity dynamics and a growing diversity and inclusivity within the U.S. racial and ethnic landscape.

This statistic shows the number of Army National Guard members in the United States from 1995 to 2010 by ethnic group. The Army National Guard had 291,000 white and 48,000 black members in 2010.

GIS Web Map Application of the 10 City Council Voter Districts