Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter. -----------------------------------------This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates and joined with Tempe census tracts. This layer is symbolized to show the percent of population 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 (in ArcGIS Online). Layer includes percent of population with a disability categorized as: · an independent living difficulty · a hearing difficulty · an ambulatory difficulty · a vision difficulty · a cognitive difficulty · a selfcare difficulty Data is from US Census American Community Survey (ACS) 5-year estimates. Vintage: 2015-2019 ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.) Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: December 10, 2020 National Figures: data.census.gov

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.

This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population 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 (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.Layer includes percent of population with a disability categorized as:an independent living difficultya hearing difficultyan ambulatory difficultya vision difficultya cognitive difficultya selfcare difficultyA ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: March 17, 2022National Figures: data.census.gov

Background and Purpose Stroke, increasingly referred to as a "brain attack", is one of the leading causes of death and the leading cause of adult disability in the United States. It has recently been estimated that there were three quarters of a million strokes in the United States in 1995. The aim of this study was to replicate the 1995 estimate and examine if there was an increase from 1995 to 1996 by using a large administrative claims database representative of all 1996 US inpatient discharges. Methods We used the Nationwide Inpatient Sample of the Healthcare Cost and Utilization Project, release 5, which contains ≈ 20 percent of all 1996 US inpatient discharges. We identified stroke patients by using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes from 430 to 438, and we compared the 1996 database with that of 1995. Results There were 712,000 occurrences of stroke with hospitalization (95% CI 688,000 to 737,000) and an estimated 71,000 occurrences of stroke without hospitalization. This totaled 783,000 occurrences of stroke in 1996, compared to 750,000 in 1995. The overall rate for occurrence of total stroke (first-ever and recurrent) was 269 per 100,000 population (age- and sex-adjusted to 1996 US population). Conclusions We estimate that there were 783,000 first-ever or recurrent strokes in the United States during 1996, compared to the figure of 750,000 in 1995. This study replicates and confirms the previous annual estimates of approximately three quarters of a million total strokes. This slight increase is likely due to the aging of the population and the population gain in the US from 1995 to 1996.

This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population 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 (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.Layer includes percent of population with a disability categorized as:an independent living difficultya hearing difficultyan ambulatory difficultya vision difficultya cognitive difficultya selfcare difficultyA ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2018-2022ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community SurveyData Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 15, 2023National Figures: data.census.gov

This layer shows Disability Status of the Civilian Noninstitutionalized Population and Households with 1+ Person with a Disability. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the 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 Total Civilian Noninstitutionalized Population - with a disability 65 and over. 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: 2018-2022ACS Table(s): DP02, S2201, S1810 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 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. 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 Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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 Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of population 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 (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.Layer includes percent of population with a disability categorized as:an independent living difficultya hearing difficultyan ambulatory difficultya vision difficultya cognitive difficultya selfcare difficultyA ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2019-2023ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 12, 2024National Figures: data.census.gov

Analysis of ‘Disability - ACS 2015-2019 - Tempe Tracts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9578f12a-1d71-44d0-bddc-b8dd60cd30e6 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter.

-----------------------------------------

This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates and joined with Tempe census tracts.

This layer is symbolized to show the percent of population 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 (in ArcGIS Online).

Layer includes percent of population with a disability categorized as:

· an independent living difficulty

· a hearing difficulty

· an ambulatory difficulty

· a vision difficulty

· a cognitive difficulty

· a selfcare difficulty

Data is from US Census American Community Survey (ACS) 5-year estimates.

Vintage: 2015-2019

ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.)

Data downloaded from: 'https://www.census.gov/data/developers/data-sets.html' rel='nofollow ugc'>Census Bureau's API for American Community Survey

Date of Census update: December 10, 2020

National Figures: data.census.gov

--- Original source retains full ownership of the source dataset ---

This layer shows Disability Status of the Civilian Noninstitutionalized Population. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the 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 Total Civilian Noninstitutionalized Population - with a disability 65 and over. 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: 2017-2021ACS Table(s): DP02, S2201, S1810Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 16, 2023National 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. 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 Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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 Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. 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.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIONALIZED POPULATION - DP02 Universe - Total Civilian Noninstitutionalized Population Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 Under the conceptual framework of disability described by the Institute of Medicine (IOM) and the International Classification of Functioning, Disability, and Health (ICF), disability is defined as the product of interactions among individuals’ bodies; their physical, emotional, and mental health; and the physical and social environment in which they live, work, or play. Disability exists where this interaction results in limitations of activities and restrictions to full participation at school, at work, at home, or in the community. For example, disability may exist where a child has difficulty learning because the school cannot accommodate the child’s deafness.

The RAND Center for Population Health and Health Disparities (CPHHD) Data Core Series is composed of a wide selection of analytical measures, encompassing a variety of domains, all derived from a number of disparate data sources. The CPHHD Data Core's central focus is on geographic measures for census tracts, counties, and Metropolitan Statistical Areas (MSAs) from two distinct geo-reference points, 1990 and 2000. The current study, Disability, contains cross-sectional data from the year 2000. Based on the Decennial Census Special Table Series published by the Administration on Aging, this study contains a large number of disability measures categorized by age (55+), type of disability (sensory, learning, employment, and self-care), and poverty status.

Background: Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. It has been estimated that 64–74 million individuals experience TBI from all causes each year. Due to these variations in reporting TBI prevalence in the general population, we decided to perform a meta-analysis of published studies to better understand the prevalence of TBI in the general adult population of the US which can help health decision-makers in determining general policies to reduce TBI cases and their costs and burden on the healthcare system. Methods: Our meta-analysis was performed using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. The study protocol was registered with PROSPERO (CRD42024534598). A comprehensive literature search of PubMed from the National Library of Medicine and Google Scholar was performed from database inception to April 2024. Sixteen studies that evaluated the US general population met our inclusion criteria. A meta-analysis using a random-effects model was performed to estimate the prevalence of TBI in the general adult population of the US.
Results: The total sample consisted of 27,491 individuals, of whom 4,453 reported a lifetime history of TBI with LOC (18.2%, 95% CI 14.4–22.7%). Some studies did not report relevant information based on gender, but based on available data, among males, 1,843 individuals out of 8,854 reported a lifetime history of TBI with LOC (20.8%). Among females, 1,363 individuals out of 11,943 reported a lifetime history of TBI with LOC (11.4%). The odds of sustaining TBI in males was higher than in females with moderate heterogeneity between studies (OR = 2.09, 95% CI 1.85–2.36, p < 0.01, I2 = 40%). Conclusion: The prevalence of TBI in the US general population is 18.2%, making it a major public health concern. In addition, males were more than twice as likely as females to sustain TBI with LOC. Considering the irreparable long-term adverse effects of TBI on survivors, their families, and the healthcare system, prevention strategies can facilitate substantial reductions in TBI-related permanent disabilities and medical care costs.

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).

This layer shows six different types of disability. This is shown by tract, county, and state centroids. 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 count and percent of population 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): B18101, B18102, B18103, B18104, B18105, B18106, B18107, C18108 (Not all lines of these ACS tables are available in this feature layer.)Data 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.

Abstract copyright UK Data Service and data collection copyright owner. The main purpose of the research was to establish the prevalence rates of disability within the Northern Ireland household population. That is, the study aimed to provide us with estimates of the numbers of people with different types of disability. The definition of disability for the purposes of the NISALD was based on the concepts of the International Classification of Functioning, Disability and Health (ICF) which was developed and endorsed by the World Health Organisation. The NISALD series of questionnaires included an initial set of questions that established the type, nature and severity of disabilities. The survey instrument also included questions dedicated to collecting information on the socio-economic characteristics of the respondents and their perceptions of the environment in which they live. Fieldwork for adults and children living in private households was carried out throughout 2006 and was completed in early 2007. Results are planned to be released via a series of bulletins. The first bulletin containing top-line results from NISALD was published on 5 July 2007. Results showed that, in 2006/07, 18% of all people living in private households in Northern Ireland have some degree of disability. The prevalence rate for adults is 21% and 6% for children. If researchers or other interested parties require more in-depth analysis to be carried out requiring the use of string variables such as ‘cause of limitation’, which are not included in the UK Data Archive version, they should contact NISRA to discuss their needs. NISRA may be able to complete analysis on their behalf, thus ensuring any sensitive data remains protected. Main Topics: Disability; Activity limitation; Impairment; Health; Disabled; ICF Simple random sample Face-to-face interview Telephone interview 2006 2007 ACCESS TO PUBLIC SE... ACCIDENTS ADULTS AGE AIDS FOR THE DISABLED AIDS FOR THE HEARIN... AIDS FOR THE SPEECH... AIDS FOR THE VISUAL... BEHAVIOURAL DISORDERS BEHAVIOURAL PROBLEMS BULLYING CARE IN THE COMMUNITY CARE OF DEPENDANTS CARE OF THE DISABLED CARERS BENEFITS CHILD BEHAVIOUR CHILD BENEFITS CHILD CARE CHILDREN CHRONIC ILLNESS COGNITION DISORDERS COGNITIVE PROCESSES COMMUNICATION DISAB... COMMUNICATION PROCESS CONGENITAL DISORDERS CONTACT LENSES DISABILITIES DISABLED CHILDREN DISABLED FACILITIES DISABLED PERSONS DISMISSAL DOMESTIC EQUIPMENT ... DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL INSTITU... EMPLOYERS EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENGLISH LANGUAGE EVERYDAY LIFE Education FAMILIES FAMILY LIFE FEAR OF CRIME FINANCIAL COMPENSATION FINANCIAL INCENTIVES FINANCIAL RESOURCES FINANCIAL SUPPORT GENDER GRANTS General health and ... HATE CRIME HEALTH HEALTH PROFESSIONALS HEALTH SERVICES HEARING AIDS HEARING IMPAIRED PE... HEARING IMPAIRMENTS HOLIDAYS HOSPITAL SERVICES HOSPITALIZATION HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING HOUSING FACILITIES HOUSING TENURE Health INCOME INDUSTRIES INFANTS INTELLECTUAL IMPAIR... INTERPERSONAL COMMU... INTERPERSONAL RELAT... JOB CHARACTERISTICS LANGUAGE DISCRIMINA... LEARNING DISABILITIES LEISURE TIME ACTIVI... LIFE STYLES LIVING CONDITIONS LOCAL COMMUNITY FAC... Labour and employment MARITAL STATUS MEDICAL EQUIPMENT A... MEMORY DISORDERS MENTAL DISORDERS MENTALLY DISABLED P... MOBILITY AIDS MOTOR PROCESSES NEIGHBOURHOODS NON VERBAL COMMUNIC... Northern Ireland OWNERSHIP AND TENURE PAIN PAIN CONTROL PAYMENTS PERSONAL HYGIENE PHYSICAL ACTIVITIES PHYSICAL DISABILITIES PHYSICAL MOBILITY PHYSICALLY DISABLED... PLACE OF BIRTH PROPERTY PUBLIC TRANSPORT QUALIFICATIONS QUALITY OF LIFE READING ACTIVITY RELIGIOUS AFFILIATION RESIDENTIAL CARE OF... RESIDENTIAL CHILD CARE RESIDENTIAL MOBILITY RESPIRATORY TRACT D... RESPITE CARE SCHOOLS FOR THE DIS... SICKNESS AND DISABI... SIGHT SOCIAL ACTIVITIES L... SOCIAL PARTICIPATION SOCIAL SERVICES SOCIAL SUPPORT SPECIAL EDUCATION SPECTACLES SPEECH IMPAIRED PER... SPORT SPOUSE S EMPLOYMENT SPOUSES STANDARD OF LIVING SURGICAL AIDS Social welfare poli... Specific diseases TRAINING COURSES TRANSPORT UNEMPLOYMENT VERBAL SKILLS VISION IMPAIRMENTS VISUALLY IMPAIRED P... WAGES WORKING CONDITIONS disorders and medic...

The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between all of the states in the United States and participating US territories and the Centers for Disease Control and Prevention (CDC).

BRFSS’s objective is to collect uniform state-specific data on health risk behaviors, chronic diseases and conditions, access to health care, and use of preventive health services related to the leading causes of death and disability in the United States. BRFSS conducts both landline and mobile phone-based surveys with individuals over the age of 18. General factors assessed by the BRFSS in 2020 included health status and healthy days, exercise, insufficient sleep, chronic health conditions, oral health, tobacco use, cancer screenings, and access to healthcare.

Section Names:

1. Record Identification -> Columns 0 to 8
2. Land Line Introduction -> Columns 9 to 20
3. Cell Phone Introduction -> Columns 21 to 30
4. Respondent Sex -> Column 31
5. Health Status -> Column 32 a. Healthy Days -> Columns 33 to 35 b. Health Care Access -> Columns 36 to 39 c. Exercise -> Column 40 d. Inadequate Sleep -> Column 41 e. Chronic Health Conditions -> Columns 42 to 54 f. Oral Health -> Columns 55 and 56
6. Demographics -> Columns 57 to 69
7. Disability -> Columns 70 to 75
8. Tobacco Use -> Columns 71 to 75
9. Alcohol Consumption -> Columns 76 to 79
10. Immunization -> Columns 80 to 83
11. Falls -> Columns 84 and 85
12. Seatbelt Use and Drinking and Driving -> Columns 86 and 87
13. Breast and Cervical Cancer Screening -> Columns 88 to 94
14. Prostate Cancer Screening -> Columns 95 to 100
15. Colorectal Cancer Screening -> Columns 101 to 110
16. HIV/AIDS -> Columns 111 to 113
17. Diabetes -> Columns 114 to 124
18. ME/CFS -> Columns 125 to 127
19. Hepatitis Treatment -> Columns 128 to 133
20. Health Care Access -> Column 134
21. Cognitive Decline -> Columns 135 to 140
22. Caregiver -> Columns 141 to 149
23. E-Cigarettes -> Columns 150 and 151
24. Marijuana Use -> Columns 152 to 154
25. Lung Cancer Screening -> Columns 155 to 158
26. Cancer Survivorship: a. Type of Cancer -> Columns 159 to 162 b. Course of Treatment -> Columns 163 to 170 c. Pain Management -> Columns 171 and 172
27. Prostate Cancer Screening Decision Making -> Columns 173 and 174
28. HPV Vaccination -> Columns 175 and 176
29. Tetanus Diphtheria (Tdap) (Adults) -> Column 177
30. Place of Flu Vaccination -> Column 178
31. Sex at Birth -> Column 179
32. Sexual Orientation and Gender Identity (SOGI) -> Columns 180 to 182
33. Adverse Childhood Experience -> Columns 183 to 193
34. Random Child Selection -> Columns 194 and 195
35. Childhood Asthma Prevalence -> Columns 196 and 197
36. Questionnaire Version -> Column 198
37. Questionnaire Language -> Column 199
38. Urban Rural -> Columns 200 and 201
39. Weighting Variables -> Columns 202 to 207, 212 to 215
40. Child Demographic Variables -> Columns 208 to 210
41. Child Weighting Variables -> Column 211
42. Calculated Variables -> Columns 216 to 228, 236 to 278
43. Calculated Race Variables -> Columns 229 to 235

Acknowledgements

This dataset has been published annually by the CDC since 1984. You can find the original dataset as a ASCII format and past years data from here

Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [2020].