In 2023, the U.S. states with the highest share of the population that had a disability were West Virginia, Arkansas, and Kentucky. At that time, around 19.7 percent of the population of West Virginia had some form of disability. The states with the lowest rates of disability were New Jersey, Utah, and Minnesota. Disability in the United States A disability is any condition, either physical or mental, that impairs one’s ability to do certain activities. Some examples of disabilities are those that affect one’s vision, hearing, movement, or learning. It is estimated that around 14 percent of the population in the United States suffers from some form of disability. The prevalence of disability increases with age, with 46 percent of those aged 75 years and older with a disability, compared to just six percent of those aged 5 to 15 years. Vision impairment One common form of disability comes from vision impairment. In 2023, around 3.6 percent of the population of West Virginia had a vision disability, meaning they were blind or had serious difficulty seeing even when wearing glasses. The leading causes of visual disability are age-related and include diseases such as cataracts, glaucoma, and age-related macular degeneration. This is clear when viewing the prevalence of vision disability by age. It is estimated that 8.3 percent of those aged 75 years and older in the United States have a vision disability, compared to 4.3 percent of those aged 65 to 74 and only 0.9 percent of those aged 5 to 15 years.

In 2023, the unemployment rate of persons with a disability was at 7.2 percent in the United States. This was a decrease from the previous year, when the unemployment rate was at 7.6 percent. The high unemployment among persons with disabilities may be due to the COVID-19 pandemic that has impacted everyone's employment, as can be seen in the unemployment rate of adults in the United States. The persons with a disability section of the Current Population Survey (CPS) is a set of six questions to identify persons who have physical, mental, or emotional conditions that cause serious difficulty with their daily activities. Disability in the labor force The U.S. Bureau of Labor Statistics (BLS) shows the unemployment rate of persons with a disability that have the ability to participate in the civilian labor force. In 2020 around 20.5 percent of persons with disabilities in the United States participated in the civilian labor force. Among those capable of participating in the civilian labor force, persons with a disability tend to have a higher chance of employment the higher their level of education. Persons with a disability that had a bachelor's degree or higher had the highest employment rate in 2020 at 25.7 percent. Social Security benefits Due to the inability to work, or the lack of access to suitable employment, many persons with a disability rely on government sources for financial aid. A portion of civilian paychecks are taxed to fund programs like the Old-Age, Survivors, and Disability Insurance (OASDI) and Supplemental Security Income (SSI) which provide this aid. In 2018, around 12.46 million disabled persons received OASDI or SSI benefits in the United States.

A survey from the fall of 2024 of over 25,000 college students from 48 schools across the United States found that around ** percent of respondents reported suffering from attention deficit hyperactivity disorder (ADHD). Furthermore, around **** percent stated they had autism spectrum disorder. Health conditions among college students Some of the most common health conditions that college students had been diagnosed with in the last year, as of fall 2024, included a cold/virus or other respiratory illness, the flu, and stomach problems. However, the most common health conditions that college students reported they had ever been diagnosed with included anxiety, environmental allergies, acne, and depression. In the fall of 2024, around ** percent of college students reported that at some point in their life they had been diagnosed with anxiety, while ** percent had been diagnosed with depression. Many universities in the United States now promote and offer mental health services, but many college students still do not receive the treatment they require. Mental health treatment According to mental health clinicians, the top concerns for their college student patients are anxiety, depression, and relationship problems. These issues are not uncommon among college students as many are living on their own for the first time in their lives, perhaps far away from home, and are likely dealing with new levels of academic, financial, and social stress. However, although universities are increasingly aware of these issues and a higher number now provide on-campus resources, many students are still not receiving treatment. For example, a survey of over 104,000 college students in 2023-2024 found that around ***percent felt they didn’t know where to go for on-campus professional mental health services. Furthermore, around ** percent of respondents stated that due to financial reasons they received fewer services (counseling, therapy, or medications) in the past year for their mental or emotional health than they would have otherwise received.

The prevalence of disabilities in the United States shows a clear correlation with age, with nearly half of Americans aged 75 and older experiencing some form of disability. This stark contrast to younger age groups highlights the increasing challenges faced by the elderly population in maintaining their independence and quality of life. Disability rates across age groups According to 2023 data, only 0.7 percent of children under 5 years old have a disability, compared to 6.3 percent of those aged 5 to 15. The percentage rises steadily with age, reaching 11.2 percent for adults between 21 and 64 years old. A significant jump occurs in the 65 to 74 age group, where 23.9 percent have a disability. The most dramatic increase is seen in those 75 and older, with 45.3 percent experiencing some form of disability. These figures underscore the importance of accessible services and support systems for older Americans. The Individuals with Disabilities Education Act (IDEA) The prevalence of disabilities among younger Americans has significant implications for the education system. The Individuals with Disabilities Education Act (IDEA) is a law in the United States that guarantees the right to a free appropriate education for children with disabilities. In the 2021/22 academic year, 7.26 million disabled individuals aged 3 to 21 were covered by the Individuals with Disabilities Education Act (IDEA). This number includes approximately 25,000 children with traumatic brain injuries and 434,000 with intellectual disabilities.

Graph and download economic data for Labor Force Participation Rate - With a Disability, 16 Years and over (LNU01374597) from Jun 2008 to Jun 2025 about disability, participation, civilian, 16 years +, labor force, labor, household survey, rate, and USA.

This layer shows disability status by sex and age group. 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. This layer is symbolized to show the percentage of elderly (65+) with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B18101Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..For cognitive difficulty, ambulatory difficulty, and self-care difficulty, the 'Population under 18 years' includes persons aged 5 to 17. Children under 5 are not included in these measures..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

The average elapsed time (in days) between the submission of an initial SSDI or SSI application and the decision for each month. Applications denied prior to a medical determination (i.e., a “technical denial”) are not included.

Residential facilities providing services for individuals with intellectual and developmental disabilities (IDD) have encountered several challenges. Rising wages have significantly contributed to profit erosion, compounded by additional financial pressures from the pandemic early in the period. Medicaid is a key financial support for IDD services, so state funding varies, impacting facilities differently. Small-sized, community-based providers faced particular fiscal challenges because of their higher staff-to-resident ratios and inability to benefit from economies of scale that more extensive facilities in healthcare services enjoy. Depending on geographical and unionization factors, staffing demands also increased payroll expenses. Despite these challenges, revenue is expected to climb at a CAGR of 2.0% through 2025 to total $41.4 billion, when revenue will climb up by an estimated 3.5% in 2025 alone, with profit at 6.0% of revenue. The emphasis on social inclusion, independence and improved quality of life for individuals with IDD has driven a shift away from large state-run institutions (PRFs) towards smaller community facilities. Preferences for HCBS waivers are directing revenue away from ICF services and institutional care and have promoted continued fragmentation within the industry. Addressing this fragmentation will necessitate a dual strategy that tackles both the volatility of funds and the adoption of technology, which presents challenges and opportunities. Government policies on reimbursements influence revenue volatility and unpredictability, which is furthered by labor market shifts, which impact operational capacity and costs. Technological advancements (automated systems, wearable devices) are emerging as powerful tools for enhancing service delivery and reducing operational costs. Facilities that adopt these advancements can benefit as they face increased competition from home-based and other community care options. However, their acceptance and use still represent a small fraction of expenditures. Looking ahead, facilities must adapt to a complex funding and competitive landscape to sustain profit. State funding will continue to be crucial for industry growth, with some states testing privatization and managed care models for IDD services funded through Medicaid. To compete with out-of-market substitutes and home-based providers, facilities can differentiate their services by including innovative technologies (virtual reality aids, adaptive learning software). Given the uncertainty around potential Medicaid cuts because of the current administration's efficiency directives, diversifying funding sources, including private insurance, can help fill gaps left by public funding shortfalls. With continued strength in disposable income and employment, industry revenue is forecast to strengthen at a CAGR of 2.3% through 2030 to $46.5 billion as profit lifts to 6.1%.

Assistive Technology Market Overview

Assistive technology companies include devices and services that help individuals with disabilities perform tasks more easily. This includes mobility aids, hearing and vision tools, communication devices, and adaptive computer technology.

AT promotes independence, supports education and employment access, enhances safety, and encourages social participation.

Despite challenges like cost and accessibility, advancements in AI and biotechnology are expanding AT's capabilities, significantly improving the quality of life for those with disabilities.

Household Pulse Survey (HPS): HPS is a rapid-response survey of adults ages ≥18 years led by the U.S. Census Bureau, in partnership with seven other federal statistical agencies, to measure household experiences during the COVID-19 pandemic. Detailed information on probability sampling using the U.S. Census Bureau’s Master Address File, questionnaires, response rates, and bias assessment is available on the Census Bureau website (https://www.census.gov/data/experimental-data-products/household-pulse-survey.html). Data from adults ages ≥18 years and older are collected by a 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). Data are weighted to represent total persons ages 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Data from adults ages ≥18 years and older are collected by 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). For more information on this survey, see https://www.census.gov/programs-surveys/household-pulse-survey.html. Data are weighted to represent total persons ages 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Responses in the Household Pulse Survey (https://www.census.gov/programs-surveys/household-pulse-survey.html) are self-reported. Estimates of vaccination coverage may differ from vaccine administration data reported at COVID-19 Vaccinations in the United States (https://covid.cdc.gov/covid-data-tracker/#vaccinations).

Disabled and Elderly Assistive Technologies Market Outlook

The global disabled and elderly assistive technologies market size was valued at approximately USD 27.5 billion in 2023 and is projected to reach around USD 49.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.7% during the forecast period. The significant growth of this market can be attributed to the increasing aging population worldwide, rising incidences of disabilities due to chronic health conditions, and technological advancements in assistive devices. The growing demand for improved quality of life among the elderly and disabled, along with favorable government initiatives and policies promoting the use of such technologies, has further propelled the market growth.

The demand for disabled and elderly assistive technologies is significantly driven by the demographic shift towards an aging population and the rise in life expectancy globally. As the baby boomer generation ages, the need for support in daily activities and mobility increases, leading to an uptick in the adoption of assistive devices. Furthermore, the prevalence of chronic diseases and conditions such as arthritis, dementia, and vision impairment among the elderly necessitates the use of assistive technologies to enhance their quality of life. This demographic trend is a vital growth driver, as it expands the potential consumer base that requires these technologies to maintain independence and manage day-to-day tasks effectively.

Another growth factor is the continuous advancements and innovations in technology enhancing the functionality and efficiency of assistive devices. Technological advancements have made devices more user-friendly, efficient, and accessible. The integration of artificial intelligence, the Internet of Things (IoT), and machine learning in assistive technologies has paved the way for smart assistive devices that can adapt to the individual needs of users, providing personalized support and improving the overall user experience. This technological evolution is critical as it not only enhances the functionality of assistive devices but also reduces the stigma associated with their use, encouraging more widespread adoption.

Additionally, government initiatives and policies aimed at improving the accessibility and affordability of assistive technologies have played a crucial role in market growth. Many governments worldwide are implementing policies to support the elderly and disabled populations, including subsidies for assistive devices and investments in healthcare infrastructure. These initiatives have made assistive technology more accessible to a broader range of people, particularly in developing regions where cost constraints may otherwise limit access. The supportive regulatory framework and funding opportunities foster an environment conducive to market expansion, encouraging manufacturers to innovate and offer cost-effective solutions.

The Paralysis Assistive Technology Market is emerging as a crucial component within the broader assistive technologies landscape. This market specifically addresses the needs of individuals with paralysis, providing them with innovative solutions to enhance mobility, communication, and daily living activities. With advancements in technology, devices such as exoskeletons, brain-computer interfaces, and adaptive communication tools are becoming more sophisticated and accessible. These technologies not only improve the quality of life for individuals with paralysis but also empower them to lead more independent lives. The growing focus on personalized care and the integration of cutting-edge technologies are driving the expansion of this market, offering new opportunities for innovation and growth.

Regionally, North America is anticipated to dominate the disabled and elderly assistive technologies market due to its well-established healthcare system and increasing geriatric population. The region's robust healthcare infrastructure, high healthcare expenditure, and greater awareness of assistive technologies among the population contribute to this dominance. Europe is also expected to hold a significant share of the market, driven by the presence of key market players and favorable government policies. Meanwhile, the Asia Pacific region is projected to exhibit the highest growth rate during the forecast period, attributed to the rapidly aging population, urbanization, and improving healthcare infrastructure. The growing awareness and economic development in emerging countries such as China and India are

The unemployment rate of white disabled persons in the United States amounted to 6.7 percent in 2023. This is significantly lower than the unemployment rate of disabled black people, which was 10.2 percent in the same year.

North America Intellectual And Development Disability (IDD) Care Market size was valued at USD 105,250.82 Million in 2023 and is projected to reach USD 158,248.82 Million by 2031, growing at a CAGR of 5.27% from 2024 to 2031.North America Intellectual And Development Disability (IDD) Care Market OverviewThe Intellectual and Development Disability (IDD) Care Market refers to services and support provided to individuals with intellectual and developmental disabilities, which encompass a broad range of disorders such as autism, Down syndrome, cerebral palsy, and other cognitive impairments. These disabilities can affect the individual’s physical, intellectual, and behavioural functioning, leading to challenges in daily living. I/DD can be caused by a variety of factors, including conditions that begin during the mother's pregnancy, such as cerebral palsy, fetal alcohol syndrome, infections like cytomegalovirus, or Down syndrome; complications at birth.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Contains the count of new initial applications for the Social Security Administration’s SSDI and SSI disability benefit programs and the total number of initial applications that are still pending nationwide for each month.

Contains the total size of the Social Security Administration’s backlog of unprocessed initial applications for its disability benefit programs (SSDI and SSI) for 2019 and 2022 in each state. Calculations for percent change in backlog size between the two years by state and the average difference in backlog size per state on a monthly basis are also included.

IDEA Section 618 Data Products: Static Tables Part B Assessment Number and percent of students grades 3 through 8 and high school, served under IDEA, Part B, who participated in reading and math assessments, by assessment type and state. Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who received a valid and proficient score on assessments for math, by assessment type, grade level, and state. Number and percent of students grades 3 through 8 and high school served under IDEA, Part B, who received a valid and proficient score on assessments for reading, by assessment type, grade level, and state. Part B Child Count and Educational Environments Number of children and students served under IDEA, Part B, by age group and state. Number of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by disability and state. Number of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by disability and state. Number and percent of children ages 3 through 5 (not in kindergarten) and students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by EL status and state. Number and percent of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by race/ethnicity and state. Number and percent of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by race/ethnicity and state. Children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, as a percentage of population, by disability category and state. Students ages 5 (in kindergarten) through 21 served under IDEA, Part B, as a percentage of population, by disability category and state. Children and students ages 3 through 21 served under IDEA, Part B, as a percentage of population, by age and state. Number and percent of children in race/ethnicity category ages 3 through 5 (not in kindergarten) with disabilities served under IDEA, Part B, by disability category and state. Number and percent of children in race/ethnicity category ages 5 (in kindergarten) through 21 with disabilities served under IDEA, Part B, by disability category and state. Number and percent of children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of female/male children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of female/male students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children ages 3 through 5 (not in kindergarten) served under IDEA, Part B, by educational environment and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) students ages 5 (in kindergarten) through 21 served under IDEA, Part B, by educational environment and state. Number and percent of children in race/ethnicity category ages 3 through 5 (not in kindergarten) with disabilities served under IDEA, Part B, by educational environment and state. Number and percent of students in race/ethnicity category ages 5 (in kindergarten) through 21 with disabilities served under IDEA, Part B, by educational environment and state. Number of children and students served under IDEA, Part B, in the US, Outlying Areas, and Freely Associated States by age and disability category. Part B Discipline Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state by disability. Number of children and students ages 3 through 21 served under IDEA, Part B, suspended/expelled by total number of days removed and state by disability. Number of children and students ages 3 through 21 served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state by type of disability. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state by race/ethnicity. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed and state by race/ethnicity. Number of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, and state by race/ethnicity. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed and state. Number and percent of female and male children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of removed and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year and state. Number of children and students, ages 3 through 21, subject to expulsion, by disability status, receipt of educational services and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, disability, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, removed to an Interim Alternative Educational Setting by type of removal, race/ethnicity, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, suspended/expelled by total number of days removed, race/ethnicity, and state. Percent of children and students ages 3 through 21 with disabilities served under IDEA, Part B, subject to disciplinary removal by total cumulative number of days removed during school year, race/ ethnicity, and state. Part B Dispute Resolution Number and percent of written, signed complaints initiated through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number and percent of mediations held through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number and percent of hearings (fully adjudicated) through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Number of expedited hearing requests (related to disciplinary decision) filed through dispute resolution procedures for children ages 3 through 21 served under IDEA, Part B, by case status and state. Part B Exiting Number of students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Number of students ages 14 through 21 with disabilities served under IDEA, Part B, in the U.S., Outlying Areas, and Freely Associated States who exited special education, by exit reason and age. Number and percent of students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason, race/ethnicity, and state. Number and percent of female and male students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Number and percent of non-English Learner (non-EL) and English Learner (EL) students ages 14 through 21 with disabilities served under IDEA, Part B, who exited special education, by exit reason and state. Part B Maintenance of Effort Reduction and Coordinated Early Intervening Services Number and percent of LEAs reported under each determination level that controls whether the LEA may be able to reduce MOE Amount reduced under the IDEA MOE provision in IDEA §613(a)(2)(C) Number and percent of LEAs that met requirements and had an increase in 611 allocations and took the MOE reduction Number and percent of LEAs required to use 15% of funds for CEIS due to significant disproportionality or voluntarily reserved funds for CEIS Number of children who received CEIS anytime in the past two years and who received special education and related services Number and percent of LEAs/ESAs that were determined to meet the MOE compliance standard in SY 2016-17 Part B Personnel Teachers employed (FTE) to work with children, ages 3 through 5, who are receiving special education under IDEA, Part B, by qualification status and state. Teachers employed

In 2019, a study investigating equality and representation within Hollywood revealed that *** percent of the industry's top 500 movies focused on characters with disabilities, which means that ** movies erased the disability community that year. This represents a decrease from the previous year, once ** movies eliminated characters with disabilities in 2018.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Census Bureau introduced a new set of disability questions in the 2008 ACS questionnaire. Accordingly, comparisons of disability data from 2008 or later with data from prior years are not recommended. For more information on these questions and their evaluation in the 2006 ACS Content Test, see the Evaluation Report Covering Disability..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.