In 2022, the U.S. states with the highest share of the population that had a disability were West Virginia, Mississippi, and Kentucky. At that time, around 19.5 percent of the population of West Virginia had some form of disability. The states with the lowest rates of disability were Utah, New Jersey, and Colorado.

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 5.8 percent of those aged 5 to 15 years.

Vision impairment One common form of disability comes from vision impairment. In 2022, around four 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 8.7 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.

As of 2021, it was estimated that the prevalence of autism spectrum disorder among children in the United States aged 3 to 17 years was 3.05 percent. This statistic shows the estimated prevalence of select developmental disabilities among U.S. children aged 3 to 17 years from 2019 to 2021.

Belgian National Burden of Disease Study

Estimates of the morbidity burden of disease for 57 cancer sites

Incidence

Data on new cancer cases in Belgium are collected by the Belgian Cancer Registry (BCR). For the current study, we selected 80 ICD-10 (C00.0-96.9 and chronic myeloid neoplasms) codes resulting in 57 cancer sites. Data were extracted by year (from 2004 to 2022), age group (5-years), sex and region (N=3). We excluded "Respiratory system and intrathoracic organs, NOS (not otherwise specified)" from further analyses because of too few cases.

Prevalence

Prevalence estimates were estimated using the above-described incidence estimates and the survival estimates also provided by BCR, derived from linkage with the Belgian Crossroads Bank for Social Security. We used a 10-year prevalence perspective meaning that from the year 2013 onwards, we were able to define the prevalence in a given year as the sum of person-months spent in the different health states. Specifically, we used a microsimulation approach to simulate future health states for each year-, age-, sex-, region- and cancer-specific cohort of incident cases.

See for more details: https://doi.org/10.1186/s12885-021-09109-4

Years Lived with Disability

Years Lived with Disability (YLDs) were calculated using both an incidence and prevalence perspective as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.

BackgroundEstimating the burden of healthcare-associated infections (HAIs) compared to other communicable diseases is an ongoing challenge given the need for good quality data on the incidence of these infections and the involved comorbidities. Based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) project and 2011–2012 data from the European Centre for Disease Prevention and Control (ECDC) point prevalence survey (PPS) of HAIs and antimicrobial use in European acute care hospitals, we estimated the burden of six common HAIs.Methods and FindingsThe included HAIs were healthcare-associated pneumonia (HAP), healthcare-associated urinary tract infection (HA UTI), surgical site infection (SSI), healthcare-associated Clostridium difficile infection (HA CDI), healthcare-associated neonatal sepsis, and healthcare-associated primary bloodstream infection (HA primary BSI). The burden of these HAIs was measured in disability-adjusted life years (DALYs). Evidence relating to the disease progression pathway of each type of HAI was collected through systematic literature reviews, in order to estimate the risks attributable to HAIs. For each of the six HAIs, gender and age group prevalence from the ECDC PPS was converted into incidence rates by applying the Rhame and Sudderth formula. We adjusted for reduced life expectancy within the hospital population using three severity groups based on McCabe score data from the ECDC PPS. We estimated that 2,609,911 new cases of HAI occur every year in the European Union and European Economic Area (EU/EEA). The cumulative burden of the six HAIs was estimated at 501 DALYs per 100,000 general population each year in EU/EEA. HAP and HA primary BSI were associated with the highest burden and represented more than 60% of the total burden, with 169 and 145 DALYs per 100,000 total population, respectively. HA UTI, SSI, HA CDI, and HA primary BSI ranked as the third to sixth syndromes in terms of burden of disease. HAP and HA primary BSI were associated with the highest burden because of their high severity. The cumulative burden of the six HAIs was higher than the total burden of all other 32 communicable diseases included in the BCoDE 2009–2013 study. The main limitations of the study are the variability in the parameter estimates, in particular the disease models’ case fatalities, and the use of the Rhame and Sudderth formula for estimating incident number of cases from prevalence data.ConclusionsWe estimated the EU/EEA burden of HAIs in DALYs in 2011–2012 using a transparent and evidence-based approach that allows for combining estimates of morbidity and of mortality in order to compare with other diseases and to inform a comprehensive ranking suitable for prioritization. Our results highlight the high burden of HAIs and the need for increased efforts for their prevention and control. Furthermore, our model should allow for estimations of the potential benefit of preventive measures on the burden of HAIs in the EU/EEA.

This web map is part of the Centers for Disease Control and Prevention (CDC) PLACES. It provides model-based estimates of cognitive disability prevalence among adults aged 18 years and older at county, place, census tract, and ZCTA levels in the United States. PLACES is an expansion of the original 500 Cities Project and a collaboration between the CDC, the Robert Wood Johnson Foundation, and the CDC Foundation.

Data sources used to generate these estimates include the Behavioral Risk Factor Surveillance System, Census 2020 population counts or Census annual county population estimates, and the American Community Survey estimates. For detailed information see PLACES. For questions or feedback email places@cdc.gov.

Measure ID used for cognitive disability is COGNITION.

AimThe provisions of the United Nation's Sustainable Development Goals (SDGs) for disability-inclusive education have stimulated a growing interest in ascertaining the prevalence of children with developmental disabilities globally. We aimed to systematically summarize the prevalence estimates of developmental disabilities in children and adolescents reported in systematic reviews and meta-analyses.MethodsFor this umbrella review we searched PubMed, Scopus, Embase, PsycINFO, and Cochrane Library for systematic reviews published in English between September 2015 and August 2022. Two reviewers independently assessed study eligibility, extracted the data, and assessed risk of bias. We reported the proportion of the global prevalence estimates attributed to country income levels for specific developmental disabilities. Prevalence estimates for the selected disabilities were compared with those reported in the Global Burden of Disease (GBD) Study 2019.ResultsBased on our inclusion criteria, 10 systematic reviews reporting prevalence estimates for attention-deficit/hyperactivity disorder, autism spectrum disorder, cerebral palsy, developmental intellectual disability, epilepsy, hearing loss, vision loss and developmental dyslexia were selected from 3,456 identified articles. Global prevalence estimates were derived from cohorts in high-income countries in all cases except epilepsy and were calculated from nine to 56 countries. Sensory impairments were the most prevalent disabilities (approximately 13%) and cerebral palsy was the least prevalent disability (approximately 0.2–0.3%) based on the eligible reviews. Pooled estimates for geographical regions were available for vision loss and developmental dyslexia. All studies had a moderate to high risk of bias. GBD prevalence estimates were lower for all disabilities except cerebral palsy and intellectual disability.ConclusionAvailable estimates from systematic reviews and meta-analyses do not provide representative evidence on the global and regional prevalence of developmental disabilities among children and adolescents due to limited geographical coverage and substantial heterogeneity in methodology across studies. Population-based data for all regions using other approaches such as reported in the GBD Study are warranted to inform global health policy and intervention.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by long-term health problems or disabilities, sex, age and level of deprivation. The estimates are as at Census Day, 21 March 2021. Age-standardisation allows for comparisons between populations that may contain proportions of different ages.

Census questions relating to disability enable different levels of detail in relation to the presence of conditions and extent of activity limitation people experience. For simplicity, we have referred to these as ‘categories’ as shown in the table below. We consider the Census 2021, 2011 and 2001 questions to be broadly comparable. However, the 2021 Census disability question changed compared with 2011 to align more closely with the Equality Act (2010). The potential influence of question changes should be considered when drawing comparisons between years, particularly for older age groups.

Age specific percentage

Age-specific percentages are estimates of disability prevalence in each age group, and are used to allow comparisons between specified age groups.

Age-standardised percentage

Age-standardised percentages are estimates of disability prevalence in the population, across all age groups. They allow for comparison between populations over time and across geographies, as they account for differences in the population size and age structure.

Details can be found here

Category

The measures of disability in each Census (2021, 2011 and 2001) enable different categorisations of responses to the question. These provide different levels of detail from the responses provided. Further information on the categories available is given in the "Questions_asked" sheet.

Count

The count is the number of usual residents in each category (disabled, non-disabled, disabled; limited a lot, disabled; limited a little, Non-disabled; with non-limiting condition, Non-disabled; no condition), sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included.

Disability

The definition of disability used in the 2021 Census is aligned with the definition of disability under the Equality Act (2010) . A person is considered disabled if they self-report having a physical or mental health condition or illness that has lasted or is expected to last 12 months or more, and that this reduces their ability to carry out day-to-day activities. Please see the questions asked tab to see how disability was defined in 2021.

Index of Multiple Deprivation and Welsh Index of Multiple Deprivation

National deciles and quintiles of area deprivation are created through ranking small geographical populations known as Lower layer Super Output Areas (LSOAs), based on their deprivation score from most to least deprived. They are then grouped into 10 (deciles) or 5 (quintiles) divisions based on the subsequent ranking. We have used the 2019 IMD and WIMD because this is the most up-to-date version at the time of publishing.

Population

The population is the number of usual residents of each sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included.

Usual resident

For Census 2021, a usual resident of the UK is anyone who, on census day, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

Small area estimates of the prevalence and characteristics of disability, in particular the demographic and socio-economic profile of people with disabilities, older people and carers, have been produced using data from the Survey of Disability, Aging and Carers 2009 (SDAC) and the Australian Census of Population and Housing (Census) 2006.

According to the data, an estimated 1.85 percent of all children in the United States were diagnosed with an intellectual disability from 2019 to 2021. This statistic shows the estimated prevalence of children aged 3 to 17 years that have ever been diagnosed with an intellectual disability from 2019 to 2021, by gender.

Abstract

The 2018 Tonga National Disabiltiy Survey was conducted jointly by the Tonga Department of Statistics (TDS) and the Ministry of Internal Affairs, Social Protection and Disability. It is the first population-based comprehensive disability survey in the country. Funding was provided through number of bodies including UNICEF, DFAT and Tonga Government. The Pacific Community provided technical supports through out different stages of the survey.

The main purpose of the survey is to desctibe demographic, social and economic characteristics of persons with disabilities and detemine the prevalence by type of disability in Tonga, and thus help the government and decision makers in formulating more suitable national plans and policies relevant to persons with disabilities.

The other objectives of the Disability survey were collect data that would determine but not limited to the following: a. Disability prevalence rate at the national, urban and rural based on the Washington Group recommendations; b. degree of activity limitations and participation restrictions and societal activities for persons with disability: c. ascertain the specific vulnerabilities that children and adults with disability face in Tonga d. establish the accessibility of health and social services for persons with disability in Tonga e. generate data that guides the development of policies and strategies that ensure equity and opportunities for children and adults with disabilities.

An additional module was included to collect information on people's perception/experiences of service delivery of Goverment to the public.

Geographic coverage

National and island division coverage.

There are six statistical regions known as Divisions in Tonga namely Tongatapu urban area, Tongatapu rural area, Vava'u, Ha'apai, Eua and the Niuas.Tongatapu Urban refers to the capital Nuku'alofa is the urban area while the other five divisions are rural areas. Each Division is subdivided into political districts, each district into villages and each village into census enumeration areas known as Census Blocks.

Analysis unit

• Individuals
• Households.

Universe

The survey covers all usual residents of selected households, all children 2-17 years and adults 18 years and above and undertake comparisons between persons with and without disability.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE: the total number of households to interview approximates 5,500 households based on the budget allocation available

SELECTION PROCESS: the selection of the sample is based on different steps (see previous section)

Stratification: this sample design is a stratified multi stage random survey. Stratification happened based on the disability status of the households and their geographical residence.

STAGES OF SELECTION: - the first stage of selection focussed on the selection of Enumeration Areas or Census Blocks as Primary Sampling Unit for households with disability. In total 334 PSUs have to be selected in order to cover the expected sample size. - the stage 2 of the selection concerns only the households with no disability as all households with disability from the selected EA are selected for interview

Level of representation: The survey will provide a comparison of the status between households with and without disability at the island group level.

REPLACEMENT: All non-response have been replaced according to the disability status of the household. Disable households that had to be replaced were replaced by another household with disability from the closest block.

SAMPLING FRAME: The sampling frame used was the 2016 population census. No additional listing were conducted.

The Sampling strategy is designed consistently with the purpose of the survey. The purpose of the 2018 Tonga Disability Survey is not to estimate the prevalence of disability in Tonga, which has been done on a very accurate way in the 2016 Population Census, but to compare the situation of the household with disability with the situation of households with not disability across the 6 geographical zones of Tonga.

The sampling strategy of the 2018 Tonga Disability Survey is based on 2 stages stratified random sample.

The stratification carried out in this survey is based on the disability status of the household: - strata 1: households who declared at least 1 member in disability (according to Washington Group list of question) - strata 2: households who did not report any disability member

The sampling frame used in this survey is the 2016 National Population Census that included the set of question on disability (from the Washington Group). In addition to the first set of stratification, the geographical breakdown of Tonga (by 6 island groups) has to be taken into consideration.

The overall idea is to equally split the total sample in both strata (1 & 2), which has been allocated to approximatively 5,500 households.

A replacement procedure is implemented in case of non -response.

The first step is to identify the households with disability from the population census. Households with disability are the households who reported at least 1 member as disable according to the 6functionning domains recommended by the Washington Group (see, hear, walk, remember, self-care, communicate).

In the strata 1, the sample distribution of approximatively 2,750 households was allocated using the square roots distribution of households across the 6 island groups. The next step consists in determining the number of blocks (Enumeration Areas) to select as Primary Sampling Unit. Again, by getting from the census frame the average number of households with disability in each block by island group will generate the number of blocks to select as PSU. Within each selected block, all households with disability will be selected for interview.

The strategy for strata 2 (non disable households) is to use the same blocks that have been selected for households in strata 1 and interview within those blocks the same number of households as strata 1.

Here is the final sample - after selection: Tongatapu urban: 1336
Tongatapu rural: 1884
Vava'u: 1060
Ha'apai: 550
Eua: 352
Niua: 54
TOTAL: 334

EA SELECTION (Primary Sampling Units labelled as blocks in the 2016 Tonga census): The EA were selected using probability proportional to size (size means number of households with disability within the EA). Within all selected EAs, all households with disability are selected for interview, and the same number of household with no disability. Households with no disability to interview in the EA were randomly selected, using uniform probability of selection.

Sampling deviation

Deviation from the original sampling plan was observed due to challenges in the field: The main fieldwork challenge was to trace the selected households (that were selected from the 2016 census frame) especially after cyclone Gita that hit Tonga before the field operation. Geography and composition of households have changed (and the household listing was not updated).

Under those circumstances, the total number of households interviewed has changed. Here is the percentage of modification between the original sampling plan and the survey achievements for each of the 2 stratas:

-STRATA 1: Tongatapu urban: 5% Tongatapu rural: 3% Vava'u: 6% Ha'apai: 0% Eua: -10% Niua: 103% Total: 4%

-STRATA 2 Tongatapu urban: 6% Tongatapu rural: 5% Vava'u: 2% Ha'apai: 1% Eua: 1% Niua: 133% Total: 5%.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Tonga Disability Survey 2018 used the CAPI system for the interview. However, the questionnaire was developed manually using excel and word software. The questionnaire was then converted to the CAPI using the Survey Solutions software. The questionnaire has two parts - the household and personal questions.

The Household questionnaire containing questions asking about characteristics of all household members of and about the household characteristics. It contains the following parts: · Household schedule/roster - listing all members and recording other social and economic information · Household characteristics - ask about household structure, characteristics, goods, assets and income.

The Personal questionnaire contains questions asking about child functioning among young children (aged 2-4 years) and older children (aged 5-17 years). Questions on adult functioning are also asked of adult aged 18 years and above. The personal questionnaire includes the following sections: · Young Child functioning for children aged 2-4 years old · Older child functioning for children aged 5-17 years old · Adult functioning for persons aged 18 years and older · Tools and service (2 years and above) · Needs and availability (2 years and above) · Transport (2 years and above) · Health care and support (5 years and above) · Education (5 years and above) · Employment and income (15 years and above) · Participation and accessibility (15 years and above) · Other social issues (18 years and above).

The development of the questionnaire went through several consultations and review from key partners and stakeholders within and outside Tonga including Tonga National Statistics Office, Non disability and disability offices in Tonga, UNICEF, WG, PDF, UNESCAP and SPC. Though the questionnaire was originally developped in English, it was also translated to Tongan local language. The first draft of the questionnaire was tested during the Pilot training and fieldwork. The questionnaire is provided as an external resource.

The draft questionnaire was pre-tested during

Abstract

Disability and society: The last 20–30 years have seen an important change in our understanding of disability. From a previous individual perspective on causes and interventions, a social and civil rights approach has taken over. Much of the focus is now on the human and physical environment and how this might reduce or enhance an individual’s level of activity and social participation.

National policy development aimed at improving living conditions in general and among people with disabilities in particular is dependent on the availability of quality data. In many countries these have been lacking, and both the United Nations and National authorities have emphasised the need for this information in order to further develop disability policies.

Information about people with disabilities and their living conditions has the potential for contributing to an improvement of the situation faced by this group in many low-income countries, as has been demonstrated in high-income countries. The Studies on Living Conditions Among People with Activity Limitations in Developing Countries have been applied to inform policy development, for capacity building, awareness creation, and in specific advocacy processes to influence service delivery.

The studies have demonstrated that level of living conditions among disabled people is systematically lower than among non-disabled people. This implies that people with disabilities are denied the equal opportunities to participate and contribute to their society. It is in this context that people with disabilities are denied their human rights.

In Malawi, specific objectives were: - To develop a strategy and methodology for the collection of comprehensive, reliable and culturally adapted statistical data on living conditions among people with disabilities (with particular reference to the International Classification of Functioning, Disability and Health - ICF) - To carry out a representative National survey on the living conditions among persons with disabilities in Malawi so as to provide the much needed data for policy influence and planning - To lay the groundwork for future and long-term data collection among persons with disabilities in Malawi - To develop a collaboration in order to improve and strengthen research on the situation of people with disabilities in Southern Africa, and - To assist in capacity building among Disabled Persons Organisations (DPOs) in Malawi and among government ministries and other disability stakeholders to utilise the research findings.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The target population for sampling was all private households in Malawi excluding institutionalised and homeless people.

Kind of data

Sample survey data [ssd]

Sampling procedure

A two-stage cluster sampling procedure was applied using the National sampling frame in each country, in close collaboration with the National statistical offices who also did sample size calculations to ensure representativity at regional/provincial level. A required number of geographical units (often called Enumeration Areas, EAs) are thus sampled, with all households in these areas included in the first stage of the sampling. Then follows screening where all households in the selected areas are interviewed (normally the head of the household) using the WG 6 screening instrument.

Sampling in Malawi: The sample size was worked out noting that in a survey of living conditions of people with disabilities, the data user would want to know the estimates of proportions of respondents sharing respective views on issues relating to disability. The characteristics requiring respondents' views in this study are many and each characteristic would have its own proportion of respondents responding in a particular manner. In this regard, the proportion would vary from characteristic to characteristic. Determination of sample number of respondents that would give a national estimate of the proportion at a given level of precision depends on the variance of the proportion and the sample design adopted. A characteristic with a proportion having a large variance would require a larger sample to arrive at an estimate of the proportion at national level at a given acceptable level of precision than that with a smaller variance. In order to avoid having varying sample sizes for given characteristics of people with disabilities under the study, the largest possible sample number of people with disabilities based on the largest possible variance that a proportion can have at a given level of precision under given sample design was calculated. The variance of a proportion being highest when the proportion equals 50%, the required sample number of disabled persons was calculated based on the assumption that the estimated proportion would take that value with a margin of error equal to plus or minus 3.5 percent at the 95 percent level of confidence. Since the sample, as will be illustrated later, was to be drawn in stages, the design effect was assumed to be equal to 2. The design effect is the effect on the variance of adopting a sampling procedure other than Simple Random Sampling (Bradley and South, 1981).The national sample size derived was made up of 1570 respondents.

The sampling frame that was utilized in this survey was obtained from the National Statistical Office (NSO). This frame was developed by NSO through the operations of the most recent population Census in Malawi conducted in 1998. Through a mapping exercise prior to the census, a total of 9206 Enumeration Areas were demarcated covering the whole country. The boundaries of these areas followed physical features such as rivers/streams, roads/paths, galleys, etc. and these enumeration areas were demarcated in such a way that during the census an enumerator would enumerate all the persons in a given enumeration area within maximum of 21 days. Each enumeration area is estimated to have approximately 300 households or an estimated 1,000 individuals. During the operations of the census, the number of persons as well as the number of households found to exist in each one of the enumeration areas was recorded. However, no list of names and location of the households within the respective enumeration areas were made. This was due to the problems which are inherent in Malawi as well as most developing countries in giving information leading to the location of a household especially in the rural areas. Malawi has a total of 28 Districts divided into Traditional Authorities (TAs). In rural areas, the Traditional Authority is the lowest units for which maps showing boundaries of the enumeration areas are available while in the cities areas called Wards are the lowest unit for which enumeration area maps are available.

Iit was calculated that a sample of 1570 persons with disabilities would be adequate to provide estimates of acceptable precision at the national level and the terms of reference dictated that there should be complete enumeration of all the people with disabilities in the sampled enumeration areas. The lowest level for which the available frame had information, as discussed above, was the enumeration area and the information comprised of only totals of persons and households. In addition, there was no information on the prevalence of persons with disabilities at the enumeration area level.

The study conducted by SINTEF Health Research and the University of Zimbabwe using the ICF definition of disability (Eide, Nhiwatiwa, Muderezi & Loeb, 2004) estimated the proportion of those disabled to be 1.9%; while the one conducted in Namibia (Eide, van Rooy & Loeb, 2003) estimated proportion of disabled in that country to be 1.6%. Lessons learnt from Namibia and Zimbabwe indicate, therefore, that utilizing the ICF definition, the prevalence of disabled persons in Malawi may be closer to the 2.9% estimate of 1983 (NSO, 1987). In the absence of information on the prevalence of disabled persons in Malawi at enumeration area level, it was assumed that the prevalence of disabled persons in each enumeration area would be 3%. Hence, in order to be able to sample and budget for the study, it was assumed that an enumeration area would contain on average 3% of its total number of households having at least a member with a disability. Based on this assumption and considering an average of approximately 300 households per enumeration area, it was calculated that the household with at least one disabled person would on average equal to 10 in an enumeration area. Considering the coverage of 1570 disabled persons, and that an enumeration area would contain on average 10 households with at least one disabled member, a sample of 157 enumeration areas were planned to be covered in the study within which all persons identified to have a disability were to be interviewed.

Each one of the districts (Likoma Island was excluded for logistical reasons) as well as each of the three cities in Malawi formed a stratum. The total sample of 157 enumeration areas was allocated to the respective strata in proportion to the population of the stratum and the distribution thereof. The selection of the allocated number of enumeration areas within each stratum was done with probability proportional to size prior to the commencement of the data collection exercise. The size measure was the human population of the enumeration areas as found in the 1998 population census.

Apart from enumerating all households having at least a person with a disability in a selected enumeration area (Cases) a similar number of households (designated as minimum 10 per enumeration area) without any disabled persons (Controls) should also be

Weighted unadjusted prevalence estimates by type of disability among persons with self-reported COPD, BRFSS 2016.

In 2022, it was estimated that 45.9 percent of those aged 75 years and older in the United States 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. with a disability as of 2022, by age.

Abstract

The major objective of the 2008 Tanzania Disability Survey was to determine the prevalence of disability in the country. The survey also intended to determine living conditions among people with activity limitations. It was anticipated that results generated from 2008 Tanzania Disability would contribute to the improvement of the living conditions among people with activity limitations in Tanzania; initiate a discussion on the concepts and understanding of “disability” and monitor the impact of government policies, programs and donor support on the wellbeing of the population with activity limitations.

The Disability Survey was carried out by the National Bureau of Statistics (NBS), in collaboration with the Office of the Chief Government Statistician, Zanzibar (OCGS) and the Ministry of Health and Social Welfare from the July 2008 to November 2008. This was a household based survey and it covered both Tanzania Mainland and Zanzibar. Information was collected from all selected households and individuals with and without disability and health difficulties.

The survey was funded by the Government of Tanzania and development partners through National Strategy for Growth and Poverty Reduction basket funding. Technical assistance was provided by Human Science Research Council of South Africa in collaboration with experts from higher education institutions in Tanzania.

Geographic coverage

National coverage, Tanzania Mainland and Zanzibar

Analysis unit

Individuals, Households, Children

Universe

Persons aged 15 years and above and Children aged 0-14 years from sampled households nationally

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was national representative and information was collected from all selected households and individuals with and without disability (measured as activity limitations).

The primary sampling unit for the survey was the census enumeration area (EA) and the ultimate sampling unit was the individual household members. The survey utilized a three-stage systematic stratified random sampling design involving clusters (EAs), households and individual household members.

The desired confidence level for the survey was 95 percent (za/2 with 1.96), with an error margin (E) of 2 percent in estimating the parameters. The expected prevalence (P) of mild and severe cases of disability was estimated to vary between 10 and 20 percent (P=0.15) of the country's population. Details on sampling procedures are attached as Annex II.

Sampling deviation

A total of 276 selected and 4 substitutes EAs were interviewed out of the selected 281. Only one EA in Dar es Salaam near the State House was not interviewed due to the construction of office premises in place of residential houses that existed during the 2002 Census.

Mode of data collection

Face-to-face [f2f]

Research instrument

Development of survey questionnaires started in 2006 with technical assistance from Human Science and Research Institute of South Africa. Three types of questionnaires were used. These were the household questionnaire that was administered to all selected households, and individual questionnaires that were administered to children aged 0 -14 years and adults aged 15 years and above. (Annex I).

Two pre-tests were conducted before the survey. Questionnaires were pre-tested on 25 households in Kibaha District in October 2006. The main objective of the pre - test was to test the way in which respondents understood and interpreted the main questions on difficulties used to identify the population with activity limitations.

Observations from this pre-test were used to improve the questionnaires before conducting a relatively bigger pilot survey in Dodoma Municipality in July 2007. A total of 18 interviewers and 4 supervisors were involved. Training was done in 5 days and data collection lasted for 10 days. A total of 154 households were successfully interviewed.

The household questionnaire was used to list all the usual members and visitors in each selected household. Basic information was collected on the characteristics of each person listed, including his/her relationship with the head of the household, age, sex, marital status, education and economic status. For children less than 18 years of age, survival status of the parents was also recorded. The household questionnaire also included questions on activity limitations of the respondent. These screening questions were used to determine persons with disabilities. All those who reported at least one activity limitation were further interviewed individually. Household questionnaires also collected information on household characteristics including main source of drinking water, toilet facilities, source of energy, building materials and possession of certain assets. Information on food security, use of mosquito nets and deaths of children less than five years of age was also collected.

The adult questionnaire was used to collect information from all person aged 15 years and above who were identified in the household questionnaire as having some form of disability.
The questionnaire collected information on the following topics: · Activity limitations and participation restrictions; · Environmental factors; · Awareness, need and receipt of services; · Education and employment; · Assistive devices and technology; · Accessibility in the home and surroundings; · Inclusion in family and social life; and · Health and general well-being.

The children questionnaire collected information from all children identified as having disabilities and collected more or less the same information as in the adult questionnaire.

Cleaning operations

Data processing was done centrally at NBS headquarters in Dar es Salaam. Data processing started concurrently with the fieldwork. The data processing personnel included supervisors and a questionnaire administrator, who ensured that the expected numbers of questionnaires from all clusters were received. There were also five office editors and ten data entrants. The CsPro computer package was used for data processing. The data entry and editing phase of the survey was completed in December 2008

Response rate

The total responding households were 6,882 out of the anticipated 7,025 with a population of more than 35,000 compared to the expected 32,000. The overall response rate for households was 98 percent.

Belgian National Burden of Disease Study

Estimates of the burden of disease

Causes of death

Our estimates are based on the official causes of death database compiled by Statbel. We first map the ICD-10 codes of the underlying causes of death to the Global Burden of Disease cause list, consisting of 131 unique causes of deaths. Next, we perform a probabilistic redistribution of ill-defined deaths to specific causes, to obtain a specific cause of death for each deceased person.

Years of Life Lost

In addition to counting the number of deaths, we also calculate Years of Life Lost (YLLs) as a measure of premature mortality. YLLs correspond to the life expectancy at the age of death, and therefore give a higher weight to deaths occurring at younger ages. We calculate YLLs using the Global Burden of Disease reference life table, which represents the theoretical maximum number of years that people can expect to live.

Prevalence

Our estimates are based on the GBD cause list for morbidity by IHME. We first select for each of the 38 causes, the most suitable local data source as described in the protocol. Next, we calculate the prevalence by year, region, age, and sex, to obtain a prevalence for each of the included diseases.

Years Lived with Disability

In addition to calculating the number of prevalent cases, we also calculate Years Lived with Disability (YLDs) as a measure of morbidity. YLDs are calculated as the product of the number of prevalent cases with the disability weight (DW), averaged over the different health states of the disease. The DWs reflect the relative reduction in quality of life, on a scale from 0 (perfect health) to 1 (death). We calculate YLDs using the Global Burden of Disease DWs.

Disability-Adjusted Life Years

Disability-Adjusted Life Years (DALYs) are a measure of overall disease burden, representing the healthy life years lost due to morbidity and mortality. DALYs are calculated as the sum of YLLs and YLDs for each of the considered diseases.

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 more information about service-connected disability status and ratings, see the Veterans Statistics webpage..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.

Local, state, tribal, and federal agencies use disability data to plan and fund programs for people with disabilities. Disability data helps communities enroll eligible households in programs designed to assist them such as health care programs and affordable housing programs. Disability data also helps local jurisdictions provide services that:Enable older adults to remain living safely in their homes and communities (Older Americans Act).Provide services and assistance to people with a disability, such as financial assistance with utilities (Low Income Home Energy Assistance Program)Disability data helps communities qualify for grants such as the Community Development Block Grant (CDBG) Program, the HOME Investment Partnership Program, the Emergency Solutions Grants (ESG) Program, the Housing Opportunities for Persons with AIDS (HOPWA) Program, and other local and federal programs.Disability data are also used to evaluate other government programs and policies to ensure that they fairly and equitably serve the needs of all groups, as well as enforce laws, regulations, and policies against discrimination.This map shows the count and prevalence of people with a disability. This includes people with a hearing difficulty, a vision difficulty, an ambulatory difficulty, a cognitive difficulty, a self-care difficulty, and an independent-living difficulty. The features in web map are symbolized using color and size to depict total population with a disability count (size of symbol) and prevalence (color of symbol). Web map is multi-scaled, and opens displaying counties. Zoom in to see tracts, zoom out to see states.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Prevalence and employment estimates for disabled and non-disabled people by different personal characteristics, UK, 2018.

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for the MSOAs of Leicester and compare this with Leicester overall statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsDisabilityThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by long-term health problems or disabilities. The estimates are as at Census Day, 21 March 2021.Definition: 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).This dataset includes details for Leicester MSOAs.