100+ datasets found
  1. Share of people in the U.S. with a disability as of 2023, by state

    • statista.com
    • ai-chatbox.pro
    Updated Apr 11, 2025
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    Statista (2025). Share of people in the U.S. with a disability as of 2023, by state [Dataset]. https://www.statista.com/statistics/794278/disabled-population-us-by-state/
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    Dataset updated
    Apr 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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.

  2. Share of people with a disability in the U.S. as of 2023, by age

    • statista.com
    Updated Jun 5, 2025
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    Statista (2025). Share of people with a disability in the U.S. as of 2023, by age [Dataset]. https://www.statista.com/statistics/793952/disability-in-the-us-by-age/
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    Dataset updated
    Jun 5, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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.

  3. F

    Population - With a Disability, 16 Years and over

    • fred.stlouisfed.org
    json
    Updated Jun 6, 2025
    + more versions
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    (2025). Population - With a Disability, 16 Years and over [Dataset]. https://fred.stlouisfed.org/series/LNU00074597
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 6, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Population - With a Disability, 16 Years and over (LNU00074597) from Jun 2008 to May 2025 about disability, civilian, 16 years +, population, and USA.

  4. People with a disability Australia 2022 by state

    • statista.com
    Updated Oct 23, 2024
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    Statista (2024). People with a disability Australia 2022 by state [Dataset]. https://www.statista.com/statistics/1070802/australia-people-with-a-disability-by-state/
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    Dataset updated
    Oct 23, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    Australia
    Description

    In 2022, the state in Australia which had the highest number of people with disabilities living there was New South Wales, in which approximately 1.5 million people were reported as having a disability. Contrastingly, it was calculated that the Northern Territory had only approximately 32,000 people living there with disabilities.

  5. H

    Disability Statistics Center

    • data.niaid.nih.gov
    • dataverse.harvard.edu
    Updated Feb 2, 2011
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    (2011). Disability Statistics Center [Dataset]. http://doi.org/10.7910/DVN/1LHI3O
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    Dataset updated
    Feb 2, 2011
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Users can access data pertaining to individuals with disabilities. Topics include but are not limited to: people with disabilities’ access to employment, technology, healthcare, and community based services. Background The Disability Statistics Center is based at the Institute for Health and Aging at the University of California, San Francisco (UCSF). The Disability Statistics Center generates reports ranging from employment opportunities, Medicaid home and community-based services, mobility device use, computer and internet use, wheelchair use, vocational rehabilitation, education, medical expenditures, and functional limitations among people with disabilities. User functiona lity Data is presented in report or abstract form and can be downloaded in PDF or HTML formats by clicking on the publications link. All reports and abstracts use United States data. Additional data sources are listed under “Finding Disability Data” and include data from the United States as well as international data. Data Notes The data sources are clearly referenced for each article. The most recent publications are from 2003. There is no indication on the site when the data will be updated.

  6. Share of people in the U.S. with a cognitive disability as of 2023, by state...

    • statista.com
    Updated Apr 11, 2025
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    Statista (2025). Share of people in the U.S. with a cognitive disability as of 2023, by state [Dataset]. https://www.statista.com/statistics/794320/cognitively-disabled-population-us-by-state/
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    Dataset updated
    Apr 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    As of 2023, almost eight percent of those living in the state of West Virginia had a cognitive disability, such as Down syndrome, autism or dementia. This statistic shows the percentage of people in the U.S. who had a cognitive disability as of 2023, by state.

  7. ACS Disability Status Variables - Boundaries

    • covid-hub.gio.georgia.gov
    • coronavirus-resources.esri.com
    • +10more
    Updated Oct 20, 2018
    + more versions
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    Esri (2018). ACS Disability Status Variables - Boundaries [Dataset]. https://covid-hub.gio.georgia.gov/maps/ef1492a820674160ba6815c5e1637c27
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    Dataset updated
    Oct 20, 2018
    Dataset authored and provided by
    Esrihttp://esri.com/
    Area covered
    Description

    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.

  8. Share of people in the U.S. with an ambulatory disability as of 2023, by...

    • statista.com
    Updated Apr 11, 2025
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    Statista (2025). Share of people in the U.S. with an ambulatory disability as of 2023, by state [Dataset]. https://www.statista.com/statistics/794330/ambulatory-disability-population-us-by-state/
    Explore at:
    Dataset updated
    Apr 11, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    As of 2023, an estimated 9.2 percent of those living in the state of Mississippi had an ambulatory disability. This statistic shows the percentage of people in the U.S. who had an ambulatory disability as of 2023, by state.

  9. 2023 American Community Survey: S1810 | Disability Characteristics (ACS...

    • data.census.gov
    Updated Oct 30, 2023
    + more versions
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    ACS (2023). 2023 American Community Survey: S1810 | Disability Characteristics (ACS 1-Year Estimates Subject Tables) [Dataset]. https://data.census.gov/cedsci/table?q=Texas%20disability
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    Dataset updated
    Oct 30, 2023
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Authors
    ACS
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    2023
    Description

    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 and the group quarters population 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, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..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..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.

  10. W

    Georgia - Number of people living with disabilities by region

    • cloud.csiss.gmu.edu
    xlsx
    Updated Jun 18, 2019
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    UN Humanitarian Data Exchange (2019). Georgia - Number of people living with disabilities by region [Dataset]. https://cloud.csiss.gmu.edu/uddi/fr/dataset/number-of-people-living-with-disabilities-by-region-in-georgia
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    xlsx(13644)Available download formats
    Dataset updated
    Jun 18, 2019
    Dataset provided by
    UN Humanitarian Data Exchange
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    According to information by the Ministry of Labour, Health and Social Affairs , 118 651 persons with disabilities are registered as recipients of state social assistance by 1 March, 2015 in Georgia that constitutes 3 percent of total population resided in Georgia.

    This dataset provides a breakdown of the number of persons with disabilities by first administrative level (region), and a detailed breakdown for the districts belonging to the capital city of Tbilisi.

    The provided information depicts the number of disabled persons receiving state social pension/allowance (beneficiaries) across the country. In light of this, state policy determines the total number of disabled persons by the sum of beneficiaries, which directly is connected to the actual number of disabled people living in Georgia. The actual number of disabled persons in Georgia is likely to be higher.

  11. w

    Georgia - Number of people living with disabilities by region

    • data.wu.ac.at
    • cloud.csiss.gmu.edu
    xlsx
    Updated Feb 6, 2018
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    OCHA ROCCA (2018). Georgia - Number of people living with disabilities by region [Dataset]. https://data.wu.ac.at/schema/data_humdata_org/ZjIxN2VkMWYtOTg2Ny00NmZjLTgwMzEtMjg5ZTY4N2FjNTYz
    Explore at:
    xlsx(13644.0)Available download formats
    Dataset updated
    Feb 6, 2018
    Dataset provided by
    OCHA ROCCA
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    According to information by the Ministry of Labour, Health and Social Affairs , 118 651 persons with disabilities are registered as recipients of state social assistance by 1 March, 2015 in Georgia that constitutes 3 percent of total population resided in Georgia.

    This dataset provides a breakdown of the number of persons with disabilities by first administrative level (region), and a detailed breakdown for the districts belonging to the capital city of Tbilisi.

    The provided information depicts the number of disabled persons receiving state social pension/allowance (beneficiaries) across the country. In light of this, state policy determines the total number of disabled persons by the sum of beneficiaries, which directly is connected to the actual number of disabled people living in Georgia. The actual number of disabled persons in Georgia is likely to be higher.

  12. Share of people in the U.S. with disabilities from 2008 to 2022

    • statista.com
    Updated Nov 19, 2024
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    John Elflein (2024). Share of people in the U.S. with disabilities from 2008 to 2022 [Dataset]. https://www.statista.com/topics/4380/disability-in-the-us/
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    Dataset updated
    Nov 19, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    John Elflein
    Area covered
    United States
    Description

    In 2022, it was estimated that almost 20 percent of the population of the U.S. had some form of disability, such as a vision disability, hearing disability, or cognitive disability. This statistic presents the percentage of people in the U.S. who had a disability from 2008 to 2022.

  13. F

    Employed - With a Disability, 16 Years and over

    • fred.stlouisfed.org
    json
    Updated Jun 6, 2025
    + more versions
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    (2025). Employed - With a Disability, 16 Years and over [Dataset]. https://fred.stlouisfed.org/series/LNU02074597
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 6, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Employed - With a Disability, 16 Years and over (LNU02074597) from Jun 2008 to May 2025 about disability, 16 years +, household survey, employment, and USA.

  14. a

    DISABILITY CHARACTERISTICS (S1810)

    • data-seattlecitygis.opendata.arcgis.com
    • data.seattle.gov
    Updated Aug 10, 2023
    + more versions
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    City of Seattle ArcGIS Online (2023). DISABILITY CHARACTERISTICS (S1810) [Dataset]. https://data-seattlecitygis.opendata.arcgis.com/datasets/SeattleCityGIS::disability-characteristics-s1810
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    Dataset updated
    Aug 10, 2023
    Dataset authored and provided by
    City of Seattle ArcGIS Online
    Description

    Table from the American Community Survey (ACS) S1810 disability characteristics by age. These are multiple, nonoverlapping vintages of the 5-year ACS estimates of population and housing attributes starting in 2015 shown by the corresponding census tract vintage. Also includes the most recent release annually.King County, Washington census tracts with nonoverlapping vintages of the 5-year American Community Survey (ACS) estimates starting in 2010. Vintage identified in the "ACS Vintage" field.The census tract boundaries match the vintage of the ACS data (currently 2010 and 2020) so please note the geographic changes between the decades. Tracts have been coded as being within the City of Seattle as well as assigned to neighborhood groups called "Community Reporting Areas". These areas were created after the 2000 census to provide geographically consistent neighborhoods through time for reporting U.S. Census Bureau data. This is not an attempt to identify neighborhood boundaries as defined by neighborhoods themselves.Vintages: 2015, 2020, 2021, 2022, 2023ACS Table(s): S1810Data downloaded from: Census Bureau's Explore Census Data The 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. 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: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 2020 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.

  15. m

    Survey of Disabled Persons , July-Dec 1990 - India

    • microdata.gov.in
    Updated Mar 27, 2019
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    National Sample Survey Office (2019). Survey of Disabled Persons , July-Dec 1990 - India [Dataset]. https://microdata.gov.in/NADA/index.php/catalog/66
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    Dataset updated
    Mar 27, 2019
    Dataset authored and provided by
    National Sample Survey Office
    Area covered
    India
    Description

    Abstract

    In NSS 36th and 47th round surveys, information was collected on three types of physical disabilities - visual, communication and loco motor - along with the cause of disability, aid/appliance acquired by the disabled, general and vocational educational level of the disabled etc. In addition, data on developmental milestones and behavioural pattern of all children of age 5-14 years, regardless of whether they were physically disabled or not, were collected.

    The Report of the study not found at external Resouce.

    Geographic coverage

    National, State, Urban , Rural

    Analysis unit

    Households

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    Outline of Sample Design: A stratified multi-stage design was adopted for the conduct of survey of NSS 58th round. The first-stage units were census villages (panchayat wards for Kerala) in the rural sector and the NSSO Urban Frame Survey (UFS) blocks in the urban sector. The ultimate stage units were households in both the sectors.

    Sampling Frame for First-Stage Units:
    For the rural sector, the list of Census 1991 villages (panchayat wards for Kerala) and Census 1981 villages for J & K constituted the sampling frame. For the urban sector, the list of latest available Urban Frame Survey (UFS) blocks was considered as the sampling frame.

    Stratification

    Rural sector: Two special strata were formed as given below at the State/ UT level on the basis of Population Census 1991 viz.

    Stratum 1: all FSUs with population between 0 to 50, and Stratum 2: FSUs with population more than 15,000

    The special stratum 1 was formed if at least 50 such FSU's were found in a State/UT. Similarly, special stratum 2 was formed if at least 4 such FSUs were found in a State/UT. Otherwise, such FSUs were merged with the general strata.

    From the remaining FSUs (not covered under stratum 1 &2) general strata (hereafter, stratum will refer to general stratum unless otherwise mentioned) was formed and numbered 3, 4, 5 …. etc. (even if no special strata have been formed). Each district of a State/UT was normally treated as a separate stratum. However, if the provisional population of the district was greater than or equal to 2.5 million as per Census 2001, the district was divided into two or more strata with more or less equal population as per population census 1991 by grouping contiguous tehsils. However, in Gujarat, some districts were not wholly included in an NSS region. In such cases, the part of the district falling in an NSS region constituted a separate stratum.

    Urban sector: In the urban sector, stratum was formed within each NSS region on the basis of size class of towns as per Census 1991 town population except for towns specified in Table 4. The stratum number and their composition (within each region) are given below:

    stratum 1: all towns with population (P) < 0.1 million stratum 2: all towns with 0.1= P < 0.5 million stratum 3: all towns with 0.5= P < 1 million
    stratum 4,5,6, … each town with P= 1 million

    The stratum numbers was retained as above even if, in some regions, some of the stratum is not formed.

    Total sample size (FSUs):
    A total number of 8338 and 9076 first-stage units were selected for survey in the Central and State samples respectively. The sample size by State and Sector is given in the Annexure

    Allocation of total sample to States and UTs:
    The total sample FSUs was allocated to the States and UTs in proportion to provisional population as per Census 2001 subject to the availability of investigators ensuring more or less uniform work-load.

    Allocation of State/ UT level sample to Rural and Urban sectors:
    State/UT level sample was allocated between two sectors in proportion to provisional population as per Census 2001 with double weightage to urban sector.

    Allocation of Rural /Urban sector level sample size to strata / sub-strata:
    Both rural and urban sector samples allotted to a State/UT were allocated to different strata in proportion to population of the stratum. All the stratum-level allocations were adjusted to multiple of 2. Stratum-level sample size in the urban sector was further allocated to 2 sub-strata in proportion to the number of UFS blocks in them with double weightage to sub-stratum 1 subject to a minimum sample size of 2 or 4 to sub-stratum 1 according as stratum-level allocation is 4 or greater than 4. Sub-stratum level allocations in the urban sector were made even.

    Selection of FSUs:
    FSUs were selected in the form of two independent sub-samples in both the sectors. For special stratum 2 and all the general strata of rural sector, FSUs were selected by probability proportional to size with replacement (PPSWR) where size was the 1991 census population. For urban sector and special stratum 1 of rural sector, FSUs were selected by simple random sampling without replacement (SRSWOR).

    Sampling deviation

    There was no deviation from the original sampling design.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The schedule on Survey of Disabled Persons (Schedule 26) consists of the following blocks:

    Block 0: descriptive identification of sample household
    Block 1: identification of sample household
    Block 2: particulars of field operation
    Block 3: household characteristics
    Block 4: demographic and other particulars of household members
    Block 5: particulars of disability of the disabled member
    Block 6: particulars of enrolment of disabled persons of age 5-14 years
    Block 7: remarks by investigator
    Block 8: comments by supervisory officer(s)
    
  16. U.S. median earnings for people with and without disabilities from 2008 to...

    • statista.com
    Updated Nov 19, 2024
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    John Elflein (2024). U.S. median earnings for people with and without disabilities from 2008 to 2022 [Dataset]. https://www.statista.com/topics/4380/disability-in-the-us/
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    Dataset updated
    Nov 19, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    John Elflein
    Area covered
    United States
    Description

    In the United States, the median salary for people with a disability was considerably lower throughout the years under consideration. In 2022, the median salary for people with a disability was 46,887 U.S. dollars. Conversely, the median salary for people without a disability in the same year was 55,208 U.S. dollars. This statistic presents the median annual salary of people with and without disabilities in the U.S. from 2008 to 2022.

  17. Number of Disabled Workers Receiving Social Security Benefits by Sex, State...

    • catalog.data.gov
    Updated Jan 31, 2025
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    Social Security Administration (2025). Number of Disabled Workers Receiving Social Security Benefits by Sex, State or Other Area, and Age, December 2008 (Table 27) [Dataset]. https://catalog.data.gov/dataset/number-of-disabled-workers-receiving-social-security-benefits-by-sex-state-or-other-area-a
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    Dataset updated
    Jan 31, 2025
    Dataset provided by
    Social Security Administrationhttp://www.ssa.gov/
    Description

    A statistical table reporting the number of beneficiaries entitled to receive benefits under Title II of the Social Security Act (OASDI) by state, age group, and sex.

  18. F

    Civilian Labor Force - With a Disability, 16 Years and over

    • fred.stlouisfed.org
    json
    Updated Jun 6, 2025
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    (2025). Civilian Labor Force - With a Disability, 16 Years and over [Dataset]. https://fred.stlouisfed.org/series/LNU01074597
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    jsonAvailable download formats
    Dataset updated
    Jun 6, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Civilian Labor Force - With a Disability, 16 Years and over (LNU01074597) from Jun 2008 to May 2025 about disability, civilian, 16 years +, labor force, labor, household survey, and USA.

  19. National Survey for Persons with Disabilities 2007 - Kenya

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated Mar 29, 2019
    + more versions
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    National Coordinating Agency for Population and Development (2019). National Survey for Persons with Disabilities 2007 - Kenya [Dataset]. https://datacatalog.ihsn.org/catalog/6684
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    Dataset updated
    Mar 29, 2019
    Dataset provided by
    Kenya National Bureau of Statistics
    National Coordinating Agency for Population and Development
    Time period covered
    2007
    Area covered
    Kenya
    Description

    Abstract

    The 2007 Kenya National Survey for Persons with Disabilities (KNSPWD) was a national sample survey - the first of its kind to be conducted in Kenya - designed to provide up-to-date information for planning, monitoring and evaluating the various activities, programmes and projects intended to improve the wellbeing of persons with disabilities. The survey covered more than 14,000 households in a total of 600 clusters (436 rural and 164 urban).

    The survey interviewed persons with disabilities of all ages in sampled areas to get estimates of their numbers; distribution; and demographic, socio-economic and cultural characteristics. The survey also sought to know the nature, types and causes of disabilities; coping mechanisms; nature of services available to them; and community perceptions and attitudes towards PWDs.

    The survey was undertaken by the National Coordinating Agency for Population and Development (NCAPD) in collaboration with the Kenya National Bureau of Statistics (KNBS); Ministry of Gender, Sports, Culture and Social Services (MGSCSS); Ministry of Health (MOH); and the Ministry of Education Science and Technology (MOEST). Other participants were United Disabled Persons of Kenya (UDPK); Kenya Programmes of Disabled Persons (KPDP); Association for the Physically Disabled of Kenya (ADPK); and Africa Mental Health Foundation (AMHF). Technical and financial support came from the Department for International Development (DFID), the World Bank and the United States Agency for International Development (USAID) under the Statistical Capacity Building Project (STATCAP) project. The United Nations Population Fund (UNFPA) provided support for the design of survey instruments.

    Geographic coverage

    National

    Analysis unit

    Households and individuals

    Universe

    The survey covered all de jure household members (usual residents) and all women aged between 12-49 years.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    While the survey intended to estimate the number of PWDs, it was realized that a significant proportion of these individuals reside in institutions, which are not part of the household sampling frame. However, a comprehensive list of institutions that existed did not form sufficient sampling frame for estimation of numbers of institution-based PWDs for the entire country. A mechanism had to be devised for incorporating these persons into the survey to supplement the data derived from the household-based survey.

    The targeted survey population for the institutional based survey was defined as all people living in homes and occupying long-stay beds in public or private hospitals; or living in long-stay residential units for people with an intellectual, psychiatric/physical disability, vision or hearing impairments, or with multiple disabilities. The following types of institutions were covered: · Hospitals (acute care, chronic care hospitals, nursing homes) · Psychiatric institutions · Treatment centres for persons with physical disabilities · Residential special schools · Private and non-private group homes · Private and non-private children's homes · Orphanages · Private and non-private residences for senior citizens (Mji wa wazee) · Other residential institutions with people with disabilities

    The sampling frame compiled for the institutional survey comprised all institutions indicated above. The frame included the name of the institution, type, number of individuals, location and type of disability. The frame was compiled from various sources, including MOH, MOEST, MSGSS and various organizations dealing with disabilities, among others.

    In order to achieve representation, the institutions were first stratified according to location (provinces) and then by nature of disability. The institutions were further classified into two broad categories depending on nature and size (number of PWDs). All key institutions were sampled with certainty (that is, all selected in the sample). The remaining institutions within a province were arranged and serially listed by disability type and a systematic random sampling procedure used to select the sample.

    A sample size of 102 institutions catering for different population sizes of PWDs was covered. Once the institutions were sampled, the next exercise involved selection of individuals for the survey. Five bands were created depending on the size of the sampled institution. The bands were: less than or equal to 30; 31-50; 51-100; 101-200; and above 200. A listing of all residents was compiled during the day of the interview and a systematic random sample drawn. Five respondents were selected from each of the sampled institutions with up to 30 PWDs, eight from those having 31-50, and ten from those having 51-100. For institutions having 100-200 PWDs, 15 were chosen, and from those having 201 and above, 20.

    The KNSPWD household sample was constructed to allow for estimation of key indicators at the provincial level as well as of the urban and rural components separately. The survey utilized a multi-stage cluster sample design and was based on a master sample frame developed and maintained by KNBS. The master sampling frame is the National Sample Survey and Evaluation Programme (NASSEP) IV. It has 1,800 clusters (data collection area points) that were developed with probability proportional to size (PPS) from the enumeration areas (EAs) delineated during the 1999 Kenya Population and Housing Census. Of the 1,800 clusters, 1,260 are rural based and the other 540 are located in urban areas.

    In the frame, the first stage involved selecting the census EAs using PPS and developing them into clusters. The process involved quick counting of the selected EA and dividing into segments depending on the measure of size (MOS). The MOS was defined as an average of 100 households, with lower and upper bounds of 50 and 149 households, respectively. The EAs that were segmented had only one segment selected randomly to form a cluster. The EAs that had fewer than 50 households were merged prior to the selection process. During the creation of NASSEP IV, other than each of the 69 districts being a stratum, the six major urban areas (Nairobi, Mombasa, Kisumu, Nakuru, Eldoret and Thika) were further stratified into five income classes: upper, lower upper, middle, lower middle and lower. The aim was to ensure that different social classes within these areas were well represented in any time sample that was drawn.

    The second sampling stage involved selecting clusters for the KNSPWD from all the clusters in the NASSEP IV master sampling frame. A total of 600 clusters (436 rural and 164 urban) was sampled from all the districts in the country with boundaries as defined in the 1999 Kenya population and housing census. The third stage of selection involved systematically sampling 25 households from each cluster, hence producing 15,000 households in total.

    Mt. Elgon district was excluded from the survey because of persistent insecurity in the area. The effect of exclusion of the district in the sample is minimal since it contributes 0.5% of the population according to 1999 census.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Models of questionnaires and survey instruments developed by the World Health Organization (WHO), Washington Group Consortium and organizations in other countries were tailored to the Kenyan context. The purpose was not only to make the instruments responsive to the country situation, but also to ensure that the results would be comparable to those from other countries.

    With input from a wide range of people who have worked in the area of disability, and who have conducted national surveys, a workshop was held to develop and adopt the following instruments for Kenya:

    · Household questionnaire: Designed to collect background information at the household level for all the usual members as well as any visitors who slept in the household the night before the interview. This questionnaire was also used to screen PWDs by type to identify those who were eligible for the individual disability questionnaire. This instrument was administered to the most knowledgeable person in the household on the day of the visit. · Individual questionnaire: Administered to any PWDs who had been identified using the household questionnaire. The questionnaire included the following key sections: activity limitation; environmental factors; situation analysis; support services; education; employment and income; immediate surroundings; assistive devices; attitudes towards disability; and health and general well-being · Reproductive health questionnaire: Administered to all eligible females aged 12 to 49 who were living with any form of disability. It collected information on reproductive health. · Institutional questionnaire: Administered to the heads of the various categories of institutions serving PWDs. Randomly selected PWDs in these institutions were interviewed using the individual questionnaire. · Focus group discussion guide: Used to collect qualitative information from a group of 6-10 members within each of the sampled clusters. The groups comprised PWDs, community leaders, service providers, opinion leaders and teachers. The focus group discussions collected information on knowledge, attitudes and beliefs of community members about PWDs and the different services available for PWDs in the different communities. Likewise, focus group discussions were used to collect qualitative information about problems faced by PWDs, their coping mechanisms and their access to essential basic services, as well as an overview of community perceptions of PWDs and views on how best to

  20. England and Wales Census 2021 - RM069: Disability by general health by age

    • statistics.ukdataservice.ac.uk
    csv, json, xlsx
    Updated Jun 10, 2024
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    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service. (2024). England and Wales Census 2021 - RM069: Disability by general health by age [Dataset]. https://statistics.ukdataservice.ac.uk/dataset/england-and-wales-census-2021-rm069-disability-by-general-health-by-age
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    xlsx, json, csvAvailable download formats
    Dataset updated
    Jun 10, 2024
    Dataset provided by
    Northern Ireland Statistics and Research Agency
    UK Data Servicehttps://ukdataservice.ac.uk/
    Office for National Statisticshttp://www.ons.gov.uk/
    Authors
    Office for National Statistics; National Records of Scotland; Northern Ireland Statistics and Research Agency; UK Data Service.
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    Wales, England
    Description

    This dataset provides Census 2021 estimates that classify usual residents in households in England and Wales by disability, by general health, and by age. The estimates are as at Census Day, 21 March 2021.

    Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

    Area type

    Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

    For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

    Lower tier local authorities

    Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

    Coverage

    Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

    • country - for example, Wales
    • region - for example, London
    • local authority - for example, Cornwall
    • health area – for example, Clinical Commissioning Group
    • statistical area - for example, MSOA or LSOA

    Disability - Equality act disabled

    People who assessed their day-to-day activities as limited by long-term physical or mental health conditions or illnesses are considered disabled. This definition of a disabled person meets the harmonised standard for measuring disability and is in line with the Equality Act (2010).

    General health

    A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

    Age (C)

    A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

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Statista (2025). Share of people in the U.S. with a disability as of 2023, by state [Dataset]. https://www.statista.com/statistics/794278/disabled-population-us-by-state/
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Share of people in the U.S. with a disability as of 2023, by state

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5 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Apr 11, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

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.

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