As of 10/22/2020, this dataset is no longer being updated and has been replaced with a new dataset, which can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 This dataset includes the average daily COVID-19 case rate per 100,000 population by town over the last two MMWR weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). These counts do not include cases among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities. This dataset will be updated weekly.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Analysis of ‘COVID-19 case rate per 100,000 population and percent test positivity in the last 7 days by town - ARCHIVE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ceb31b99-df28-4d47-bfc9-dd3ab1896172 on 26 January 2022.

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

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

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

This map service displays healthcare resources supply and demand per state, congressional district, and county in the United States. It shows the number of people per geography (state, congressional district and county), from the U.S. Census Bureau’s 2010 census, divided by the number of health care facilities (hospitals, medical centers, federally qualified health centers, and home health services), provided by the U.S. Department of Health Human Services. The health care system capacity is calculated as the number of facilities in the area multiplied by the national average (number of people per facility). The number of facilities of each type needed is calculated by dividing the area's population by the national average (number of people per facility). The facility surplus or need is calculated by subtracting the number of facilities needed (based on the population size) from the number of existing facilities. Number of hospital beds, accessibility and travel time are not considered in these calculations as this data is not available here.We recommend this service be viewed with a 40% transparency. Other data source include Data.gov._Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

Analysis of ‘COVID-19 case rate per 100,000 population and percent test positivity in the last 14 days by town’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d5e87e00-5f12-4c5e-9fb7-9718e5dbef35 on 13 February 2022.

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

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

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

BackgroundFor the oldest-old residents around their 90s living in facilities, quality end-of-life care is crucial. While an association between reduced food intake and death is known, specific patterns of intake changes before death are not well-documented.AimsThis study aims to classify food intake changes among residents in Japan’s special nursing homes during the 6 months before death, enabling precision care for each group using routinely recorded data.MethodsSixty-nine deceased older adults from five special nursing homes were studied over 3.5 years (January 2016 to June 2020). Criteria included: at least six months’ residency before death, ability to eat orally during the study period, and death within the facility. We created a time-series dataset for 69 participants, documenting their average weekly food intake (on a scale of 0-10). Subsequently, we used cluster analysis to identify clusters of change in the average weekly food intake from the 6 months before death.ResultsEligible residents’ mean age was 89.7 ±  6.7 years, and 79.7% were female. Cluster analysis classified 4 clusters of decline in food intake changes during the last 6 months before death: immediate decrease (n = 14); decrease from 1 month before death (n = 24); decrease from 3 months before death (n = 7); and gradual decrease for 6 months before death (n = 24).ConclusionThis study identified four groups of food intake prior to death. Recognizing food intake clusters in practical settings can help manage and provide appropriate end-of-life care in facilities with few medical providers but many care providers.

The average number of years care home residents aged 65 years and over are expected to live beyond their current age in England and Wales. Classified as Experimental Statistics.

Number of council-supported permanent admissions of younger adults (aged 18-64) to residential and nursing care divided by the size of the younger adult population (aged 18-64) in the area multiplied by 100,000. People counted as a permanent admission include: Residents where the local authority makes any contribution to the costs of care, no matter how trivial the amount and irrespective of how the balance of these costs are metSupported residents in: Local authority-staffed care homes for residential careIndependent sector care homes for residential careRegistered care homes for nursing careResidential or nursing care which is of a permanent nature and where the intention is that the spell of care should not be ended by a set date. For people classified as permanent residents, the care home would be regarded as their normal place of residence. Where a person who is normally resident in a care home is temporarily absent at 31 March (e.g. through temporary hospitalisation) and the local authority is still providing financial support for that placement, the person should be included in the numerator. Trial periods in residential or nursing care homes where the intention is that the stay will become permanent should be counted as permanent. Whether a resident or admission is counted as permanent or temporary depends on the intention of the placement at the time of admission. The transition from ASC-CAR to SALT resulted in a change to which admissions were captured by this measure, and a change to the measure definition. 12-week disregards and full cost clients are now included, whereas previously they were excluded from the measure. Furthermore, whilst ASC-CAR recorded the number of people who were admitted to residential or nursing care during the year, the relevant SALT tables record the number of people for whom residential/nursing care was planned as a sequel to a request for support, a review, or short-term support to maximise independence Only covers people receiving partly or wholly supported care from their Local Authority and not wholly private, self-funded care. Data source: SALT.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Care Homes provide a residential setting for people that require 24 hour care. The majority of Care Homes provide services for older people, but some offer services to Children and those with Mental or Sensory Impairments.

All Care Homes in the UK are registered, inspected and listed by the relevant authority, which in England and Wales is currently the Care Quality Commission (CQC) There are two main categories of care home; those which provide only personal care and those which also provide nursing care. In addition, some Care Homes provide specialist care, eg for Dementia or Terminal Illness

Care Homes are often run by groups. In these instances we provide the group name and details and record a link from each home to its parent organisation, but we list each home as separate entities due to each having their own considerations/services.

Type of ownership:

The database details the type of ownership of the Homes

Private Homes run by individuals, partnerships and public and private limited companies.

Voluntary Homes that are run by Charities such as The Leonard Cheshire Foundation or Mencap.

Public Homes that are run by Local Authorities and NHS Trusts

Number of beds:

We list the number of Beds for each organisation. The average size of home is approximately 20 beds, whilst only 10% have more than 50 beds. There are almost 3,000 homes with five or fewer beds. These usually provide very specific types of care, including provision for Care in the Community and, if privately owned, should not normally be regarded as commercial undertakings.

Abstract copyright UK Data Service and data collection copyright owner. The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included). The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. Data are revised on a wave by wave basis, as a result of backwards imputation from the current wave's data. These revisions are due to improvements in the imputation methodology.Note from the WAS team - November 2023:“The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates.” Survey Periodicity - "Waves" to "Rounds" Due to the survey periodicity moving from “Waves” (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019. A Secure Access version of the WAS, subject to more stringent access conditions, is available under SN 6709; it contains more detailed geographic variables than the EUL version. Users are advised to download the EUL version first (SN 7215) to see if it is suitable for their needs, before considering making an application for the Secure Access version.Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.Occupation data for 2021 and 2022 data files The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.Latest edition informationFor the 18th edition (May 2023), the inheritance variables 'ivalb1r7' and 'ivalb1r7_i' which had been omitted in error have been added. Main Topics: The WAS questionnaire is divided into two parts with all adults aged 16 years and over (excluding those aged 16 to 18 currently in full-time education) being interviewed in each responding household. Household schedule: This is completed by one person in the household (usually the head of household or their partner) and predominantly collects household level information such as the number, demographics and relationship of individuals to each other, as well as information about the ownership, value and mortgages on the residence and other household assets. Individual schedule: This is given to each adult in the household and asks questions about economic status, education and employment, business assets, benefits and tax credits, saving attitudes and behaviour, attitudes to debt, insolvency, major items of expenditure, retirement, attitudes to saving for retirement, pensions, financial assets, non-mortgage debt, investments and other income. Multi-stage stratified random sample Telephone interview Face-to-face interview 2006 2020 ADOPTION PAY AGE AIRCRAFT ASSETS ATTITUDES TO SAVING BANK ACCOUNTS BICYCLES BOATS BONDS BUSINESS OWNERSHIP BUSINESS RECORDS BUSINESSES CARAVANS CARE OF DEPENDANTS CARERS BENEFITS CARS CHILD BENEFITS CHILD SUPPORT PAYMENTS CHILD TRUST FUNDS COHABITING COMMERCIAL BUILDINGS COST OF LIVING COSTS CREDIT CARD USE DEBILITATIVE ILLNESS DEBTS DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL STATUS EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ESTATES ETHNIC GROUPS FAMILY BENEFITS FAMILY INCOME FAMILY MEMBERS FINANCIAL ADVICE FINANCIAL COMPENSATION FINANCIAL DIFFICULTIES FINANCIAL SERVICES FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GIFTS Great Britain HEALTH HEALTH STATUS HIRE PURCHASE HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOUSE PRICES HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S SO... HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING AGE HOUSING ECONOMICS HOUSING FINANCE HOUSING TENURE ILL HEALTH INCOME INCONTINENCE INFORMAL CARE INHERITANCE INSOLVENCIES INSURANCE CLAIMS INTELLECTUAL IMPAIR... INVESTMENT Income JOB HUNTING JOB SEEKER S ALLOWANCE LAND OWNERSHIP LANDLORDS LOANS Labour and employment MAIL ORDER SERVICES MARITAL STATUS MATERNITY BENEFITS MATERNITY PAY MATHEMATICS MOBILE HOMES MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES MOTOR VEHICLE VALUE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS OLD AGE BENEFITS ONE PARENT FAMILIES OVERDRAFTS PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PATERNITY BENEFITS PATERNITY PAY PENSION BENEFITS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL FINANCE MA... PHYSICAL MOBILITY PLACE OF BIRTH PRIVATE PENSIONS PRIVATE PERSONAL PE... PROFIT SHARING PROFITS QUALIFICATIONS REDUNDANCY PAY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RETIREMENT AGE SAVINGS SAVINGS ACCOUNTS AN... SECOND HOMES SELF EMPLOYED SELLING SHARED HOME OWNERSHIP SHARES SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPOUSES STAKEHOLDER PENSIONS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENT LOANS SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS TAX RELIEF TENANTS HOME PURCHA... TIED HOUSING TOP MANAGEMENT TRANSPORT FARES TRUSTS UNEARNED INCOME UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WEALTH WILLS WINNINGS WORKPLACE property and invest...

Abstract copyright UK Data Service and data collection copyright owner. The Wealth and Assets Survey (WAS) is a longitudinal survey, which aims to address gaps identified in data about the economic well-being of households by gathering information on level of assets, savings and debt; saving for retirement; how wealth is distributed among households or individuals; and factors that affect financial planning. Private households in Great Britain were sampled for the survey (meaning that people in residential institutions, such as retirement homes, nursing homes, prisons, barracks or university halls of residence, and also homeless people were not included).The WAS commenced in July 2006, with a first wave of interviews carried out over two years, to June 2008. Interviews were achieved with 30,595 households at Wave 1. Those households were approached again for a Wave 2 interview between July 2008 and June 2010, and 20,170 households took part. Wave 3 covered July 2010 - June 2012, Wave 4 covered July 2012 - June 2014 and Wave 5 covered July 2014 - June 2016. Revisions to previous waves' data mean that small differences may occur between originally published estimates and estimates from the datasets held by the UK Data Service. These revisions are due to improvements in the imputation methodology.Note from the WAS team - November 2023:"The Office for National Statistics has identified a very small number of outlier cases present in the seventh round of the Wealth and Assets Survey covering the period April 2018 to March 2020. Our current approach is to treat cases where we have reasonable evidence to suggest the values provided for specific variables are outliers. This approach did not occur for two individuals for several variables involved in the estimation of their pension wealth. While we estimate any impacts are very small overall and median pension wealth and median total wealth estimates are unaffected, this will affect the accuracy of the breakdowns of the pension wealth within the wealthiest decile, and data derived from them. We are urging caution in the interpretation of more detailed estimates."Survey Periodicity - "Waves" to "Rounds"Due to the survey periodicity moving from "Waves" (July, ending in June two years later) to “Rounds” (April, ending in March two years later), interviews using the ‘Wave 6’ questionnaire started in July 2016 and were conducted for 21 months, finishing in March 2018. Data for round 6 covers the period April 2016 to March 2018. This comprises of the last three months of Wave 5 (April to June 2016) and 21 months of Wave 6 (July 2016 to March 2018). Round 5 and Round 6 datasets are based on a mixture of original wave-based datasets. Each wave of the survey has a unique questionnaire and therefore each of these round-based datasets are based on two questionnaires. While there may be some changes in the questionnaires, the derived variables for the key wealth estimates have not changed over this period. The aim is to collect the same data, though in some cases the exact questions asked may differ slightly. Detailed information on Moving the Wealth and Assets Survey onto a financial years’ basis was published on the ONS website in July 2019.Further information and documentation may be found on the ONS Wealth and Assets Survey webpage. Users are advised to the check the page for updates before commencing analysis.Users should note that issues with linking have been reported and the WAS team are currently investigating.Secure Access WAS dataThe Secure Access version of the WAS includes additional, detailed geographical variables not included in the End User Licence (EUL) version (SN 7215). These include:WardsParliamentary Constituency Areas for Wave 1 onlyCensus Output AreasLower Layer Super Output AreasLocal AuthoritiesLocal Education AuthoritiesProspective users of the Secure Access version of the WAS will need to fulfil additional requirements, including completion of face-to-face training, and agreement to the Secure Access User Agreement and Licence Compliance Policy, in order to obtain permission to use that version (see 'Access' section below). Users are therefore strongly encouraged to download the EUL version (SN 7215) to see if it contains sufficient detail for their needs, before considering making an application for the Secure Access version.Latest Edition InformationFor the ninth edition (October 2022), the Round 7 person and household data have been updated. The Round 7 Wave 1 Variable Catalogue Excel file has also been updated. Main Topics: The WAS questionnaire was divided into two parts with all adults aged 16 years and over (excluding those aged 16 to 18 currently in full-time education) being interviewed in each responding household. Household schedule: This was completed by one person in the household (usually the head of household or their partner) and predominantly collected household level information such as the number, demographics and relationship of individuals to each other, as well as information about the ownership, value and mortgages on the residence and other household assets. Individual schedule: This was given to each adult in the household and asked questions about economic status, education and employment, business assets, benefits and tax credits, saving attitudes and behaviour, attitudes to debt, insolvency, major items of expenditure, retirement, attitudes to saving for retirement, pensions, financial assets, non-mortgage debt, investments and other income. Multi-stage stratified random sample Face-to-face interview 2006 2020 ADOPTION PAY AGE AIRCRAFT ALIMONY ASSETS ATTITUDES TO SAVING BANK ACCOUNTS BEDROOMS BICYCLES BOATS BONDS BUSINESS OWNERSHIP BUSINESS RECORDS BUSINESSES CARAVANS CARE OF DEPENDANTS CARERS BENEFITS CARS CHILD BENEFITS CHILD SUPPORT PAYMENTS CHILD TRUST FUNDS COHABITING COMMERCIAL BUILDINGS COST OF LIVING COSTS CREDIT CARD USE DEBILITATIVE ILLNESS DEBTS DISABILITIES EARLY RETIREMENT ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL COURSES EDUCATIONAL FEES EDUCATIONAL GRANTS EDUCATIONAL STATUS EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY EMPLOYMENT PROGRAMMES ENDOWMENT ASSURANCE ESTATES ETHNIC GROUPS EXPENDITURE FAMILY BENEFITS FAMILY INCOME FAMILY MEMBERS FINANCIAL ADVICE FINANCIAL COMPENSATION FINANCIAL DIFFICULTIES FINANCIAL SERVICES FREQUENCY OF PAY FRINGE BENEFITS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER GIFTS Great Britain HEALTH HEALTH STATUS HIRE PURCHASE HOME BUILDINGS INSU... HOME BUYING HOME CONTENTS INSUR... HOME OWNERSHIP HOUSE PRICES HOUSEHOLD BUDGETS HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S SO... HOUSEHOLD INCOME HOUSEHOLDERS HOUSEHOLDS HOUSING HOUSING AGE HOUSING ECONOMICS HOUSING FINANCE HOUSING TENURE ILL HEALTH INCOME INCOME TAX INCONTINENCE INFORMAL CARE INHERITANCE INSOLVENCIES INSURANCE CLAIMS INTELLECTUAL IMPAIR... INTEREST FINANCE INVESTMENT Income JOB HUNTING JOB SEEKER S ALLOWANCE LAND OWNERSHIP LAND VALUE LANDLORDS LIFE INSURANCE LOANS Labour and employment MAIL ORDER SERVICES MARITAL STATUS MATERNITY BENEFITS MATERNITY PAY MATHEMATICS MOBILE HOMES MORTGAGE ARREARS MORTGAGE PROTECTION... MORTGAGES MOTOR VEHICLE VALUE MOTOR VEHICLES MOTORCYCLES OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OCCUPATIONS OLD AGE BENEFITS ONE PARENT FAMILIES OVERDRAFTS PART TIME EMPLOYMENT PARTNERSHIPS BUSINESS PATERNITY BENEFITS PATERNITY PAY PENSION BENEFITS PENSION CONTRIBUTIONS PENSIONS PERSONAL DEBT REPAY... PERSONAL FINANCE MA... PHYSICAL MOBILITY PLACE OF BIRTH PRIVATE PENSIONS PRIVATE PERSONAL PE... PROFIT SHARING PROFITS QUALIFICATIONS REDUNDANCY PAY RELIGIOUS AFFILIATION RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RENTS RESIDENTIAL BUILDINGS RETIREMENT RETIREMENT AGE ROYALTIES SAVINGS SAVINGS ACCOUNTS AN... SECOND HOMES SELF EMPLOYED SELLING SHARED HOME OWNERSHIP SHARES SICK PAY SICKNESS AND DISABI... SOCIAL HOUSING SOCIAL SECURITY SOCIAL SECURITY BEN... SOCIO ECONOMIC STATUS SPOUSES STAKEHOLDER PENSIONS STATE RETIREMENT PE... STATUS IN EMPLOYMENT STUDENT LOANS SUBSIDIARY EMPLOYMENT SUPERVISORY STATUS SURVIVORS BENEFITS TAX RELIEF TAXATION TENANTS HOME PURCHA... TIED HOUSING TOP MANAGEMENT TRANSPORT FARES TRUSTS UNEARNED INCOME UNEMPLOYED UNFURNISHED ACCOMMO... UNWAGED WORKERS WAGES WAR VETERANS BENEFITS WEALTH WILLS WINNINGS WORKPLACE property and invest...

This dataset, released December 2017, contains data relating to the Home and Community Care Program 2014-2015 where the number of: Clients living alone, Clients with carer, Clients with co-resident carer, Indigenous clients (as a proportion of total clients), Indigenous clients (as a proportion of the Indigenous population), Non-English speaking clients, Total clients, Allied health care instances at home, Allied health care instances at centre, Care received in support instances, Case management instances, Centre based day care instances, Client care coordination instances, Domestic assistance instances, Home maintenance and modification instances, Meals at centre plus meals at home instances, Nursing care at centre plus nursing care at home instances, Personal care instances, Respite care instances, Social support instances, Transport instances, Total instances of assistance. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU using data from the Australian Institute of health and Welfare, 2014/15; and the average of the ABS Estimated Resident Population, 30 June 2014 and 30 June 2015. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

The Department of Health Care Services (DHCS) Long-Term Services and Supports (LTSS) Data Dashboard is an initiative of the Home and Community Based Services Spending Plan. The initiative's primary goal is to create a public-facing LTSS data dashboard to track demographic, utilization, quality, and cost data related to LTSS services. This dashboard will link statewide long-term care and home and community-based services (HCBS) data with the goal of increased transparency to make it possible for regulators, policymakers, and the public to be informed while the state continues to expand, enhance, and improve the quality of LTSS in all home, community, and congregate settings.

The first iteration of the LTSS Dashboard was released in December 2022 as an Open Data Portal file with 40 measures pertaining to LTSS beneficiaries, which includes ten different demographics, plan-related dimensions, and dual stratification. The December 2023 Data Release includes 16 new measures on the Medi-Cal LTSS Dashboard and Open Data Portal (Select “View Underlying Data”); and additional measures and dimensions, including dual stratification, will be added to the Open Data Portal in 2024.

Note: The LTSS Dashboard measures are based on certified eligible beneficiaries who were enrolled in Medi-Cal for one or more months during the reporting interval. Most of the DHCS LTSS dashboard measures report the annual number of certified eligible Medi-Cal beneficiaries who have used LTSS services within a year. Other departments may report on these programs differently. For example, the Department of Social Services (CDSS) reports monthly IHSS recipient/consumer counts. The California Department of Aging (CDA) reports monthly CBAS Medi-Cal participants. DHCS’ annual utilization / enrollment counts of IHSS and CBAS beneficiaries are larger than CDSS/CDA's monthly counts because of data source differences and new enrollment or program attrition over time. Monthly snap-shot measures (average monthly utilization) for IHSS and CBAS have been added to the LTSS Dashboard to align with CDSS and CDA monthly reporting.

Refer to the LTSS-Dashboard (ca.gov) program page for: 1) a Fact Sheet with highlights from the initial data release including changes over time in use of Home and Community-Based Services as well as select demographic information; 2) the Measure Specifications document – that describes business rules and inclusion/exclusion criteria related to age groups, plan types, aid code, geographic, or other important program/waiver-specific eligibility criteria; and 3) User guide – that shows how to navigate the Open Data Portal data file with specific examples.

This table provides estimated data for the second quarter of 2023 on the average and estimated standard deviation of the weekly hours spent caring for persons with disabilities or limitations living at home in the population aged 18 and over in the Canary Islands caring for them by sex and age group.

This dataset, released December 2017, contains data relating to the Home and Community Care Program 2014-2015 where the number of: Clients living alone, Clients with carer, Clients with co-resident …Show full descriptionThis dataset, released December 2017, contains data relating to the Home and Community Care Program 2014-2015 where the number of: Clients living alone, Clients with carer, Clients with co-resident carer, Indigenous clients (as a proportion of total clients), Indigenous clients (as a proportion of the Indigenous population), Non-English speaking clients, Total clients, Allied health care instances at home, Allied health care instances at centre, Care received in support instances, Case management instances, Centre based day care instances, Client care coordination instances, Domestic assistance instances, Home maintenance and modification instances, Meals at centre plus meals at home instances, Nursing care at centre plus nursing care at home instances, Personal care instances, Respite care instances, Social support instances, Transport instances, Total instances of assistance. The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU using data from the Australian Institute of health and Welfare, 2014/15; and the average of the ABS Estimated Resident Population, 30 June 2014 and 30 June 2015. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SR" - Indirectly age-standardised ratio. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

The national study SNAC - The Swedish National Study on Aging and Care, includes four participating areas: SNAC-Blekinge, SNAC Kungsholmen, SNAC Nordanstig and SNAC Skåne (GÅS). In all four areas, a research centre conducts a population study and a health care system study. (Metadata related to the main study SNAC and the other participating areas can be found under the Related studies tab). SNAC-K Kungsholmen SNAC-K is conducted by the Stockholm Gerontology Research Center in collaboration with Aging Research Center (ARC), Karolinska Institutet. SNAC-K population study: The population study consists of a clinical examination of persons over 60 years, who live in the area of Kungsholmen/Essingeöarna. The baseline data collection includes information on present status and past events. The information has been collected through interviews, clinical examinations, and testing. All staff (nurses, psychologists, and physicians) has been trained for data collection. Each subject has been examined for six hours on average; two hours for the social interview and the assessment of physical functioning (performed by a nurse); two hours for clinical examination, including geriatric, neurological and psychiatric assessment (performed by a physician); and two hours for cognitive assessment (performed by a psychologist). SNAC-K care system study: The care system data collection consists of continuous recording of the provision of public eldercare for persons over 65 years. For 2004-2020, data comprise all recipients of municipal eldercare in the district of Kungsholmen. Starting in 2015, data comprise all recipients of municipal eldercare in the whole municipality of Stockholm. Data are based on individual assessments made by the municipal need assessors for each decicison regarding the provision of eldercare services. Data include information about the type and amount of care and services granted, as well as information on need indicators (e.g., disability,physical function, cognitive impairment, mental health, living situation, housing). For specific research questions, data from the care system study can be complemented with register data on health care consumption provided by the Region of Stockholm (VAL-databas). The care system perspective and the population perspective are joined through those elderly persons who participate in both parts of the study. Purpose: Population study: The purpose is to study the transition from normal aging to morbidity and impaired functional ability by identify how social and biological factors, and the environment, affect older people's health, functional ability and life expectancy. The intention is to study the positive and negative events in life that may be relevant to aging. Care system study: The aim of SNAC-K care system study is to continuously monitor the allocation of public eldercare in relation to need indicators. Collected data can be used as a basis for planning, resource allocation and evaluation of the provision of eldercare services and health care among older adults. Available data can also be used in research and development around the issues of the provision of social and heath care. The connection to the SNAC-K population study gives a unique opportunity for comparisons between care recipients and non-recipients. At the baseline study, in 2001-2004, 739 60-year olds participated. The population was followed up in 2007-2009, when 608 individuals participated. Further follow-up is ongoing in 2013-2015. For more information please visit: https://www.snac-k.se/for-researchers/data-description/ https://www.snac-k.se/for-researchers/code-books/ Den nationella äldrestudien SNAC - The Swedish National Study on Aging and Care, innefattar fyra deltagande områden: SNAC-Blekinge, SNAC-Kungsholmen, SNAC-Nordanstig och SNAC-Skåne (GÅS). Vid samtliga fyra områden finns ett forskningscentrum som bedriver en befolkningsstudie och dels en vårdsystemstudie. Under 'Relaterade studier' finns beskrivning om huvudstudien SNAC, samt specifik studiebeskrivning för respektive delstudie inom SNAC. SNAC-K Kungsholmen I Stockholm svarar Äldrecentrum för studien SNAC-K. Den genomförs i Kungsholmens stadsdel som omfattar Kungsholmen och Essingeöarna. Arbetet bedrivs tillsammans med Aging Research Center (ARC). Befolkningsdel: Datainsamlingen i befolkningsdelen avser uppföljning av hälsa, sjukdom, funktionsförmåga, sociala förhållanden och vårdbehov genom upprepade undersökningar, intervjuer, enkäter etc. Denna information kompletteras med olika slag av register data. Datainsamlingen sker genom att deltagarna får träffa en sjuksköterska, en läkare och en psykolog. Vårdsystemdel: Datainsamlingen i vårdsystemdelen består av en fortlöpande kartläggning av biståndsbedömda behov och beviljade insatser från äldreomsorgen för personer över 65 år. För åren 2004-2020 omfattas samtliga omsorgstagare boende på Kungsholmen. Fr.o.m. 2015 omfattas samtliga omsorgstagare i hela Stockholms kommun. För specifika frågeställningar kompletteras data med uppgifter från Region Stockholms patientregister (VAL-databasen). Vårdsystemdelen och befolkningsdelen förenas genom de personer som deltar i båda delarna av SNAC-studien. Syfte: Inom ramen för SNAC har befolkningsdelen i delstudien SNAC-K speciellt inriktats på demens, multisjuklighet samt fysisk och mental funktionsförmåga. Syftet med studien är bland annat att studera övergången från normalt åldrande till sjuklighet och nedsatt funktionsförmåga genom att kartlägga hur sociala och biologiska faktorer, samt miljön, inverkar på de äldres hälsa och funktionsförmåga och förväntad livslängd. Avsikten är att studera negativa och positiva händelser under livet som kan ha betydelse för åldrandet. Syftet med SNAC-K vårdsystemdelen är att över tid studera olika perspektiv på jämlik och behovsstyrd äldreomsorg för personer 65 år och äldre. Detta sker geom att beskriva och analysera hur behov av bistånd bedöms och beviljas enligt Socialtjänstlagen (SoL) bland personer 65 år och äldre. Syftet innefattar även att analysera hur äldreomsorgens insatser samvarierar med konsumtion av hälso- och sjukvård och med stöd och hjälp från anhöriga (informell omsorg), och hur detta förändras över tid. Insamlade data kan användas som underlag för planering, resursfördelning och utvärdering av vården och omsorgen av de äldre. Tillgängliga data kan också användas i forsknings- och utvecklingsarbete kring frågor om vård och omsorg. Genom att kombinera data från befolkningsdelen och vårdsystemdelen ges unika möjligheter att göra jämförelser mellan dem som har och dem som inte har insatser från kommunens äldreomsorg. Vid baslinjeundersökningen, som genomfördes mellan åren 2001-2004, deltog 739 60-åringar. Populationen med 60-åringar har därefter följts upp mellan åren 2007-2009, då 608 individer deltog. Ytterligare uppföljning pågår 2013 - 2015. För mer information vänligen se: https://www.snac-k.se/for-researchers/data-description/ och https://www.snac-k.se/for-researchers/code-books/ The population study: The SNAC-K population consists of a random sample of individuals aged 60˗104 years living both at home and in institutions in Kungsholmen, Stockholm in the central part of Sweden. The random sample was stratified by age cohort and year of assessment and an oversampling of those aged 60 years respectively > 81 years of age was conducted for all the SNAC studies. In SNAC-K, eleven age cohorts were chosen (60, 66, 72, 78, 81, 84, 87, 90, 93, 96, and 99) with six year intervals for the younger cohorts and three years for the older cohorts (≥78 years). During the baseline examination in 2001-04, 3363 individuals were included (response rate 73.3%). Participants who are 78 years of age or older are followed up every three years, while for those aged 60 to 72 years, follow-up will take place every six years. Data have been collected at seven waves over a total of 20 years and is ongoing. The care system study: The care system study includes all eldercare recipients 65 years or older, for the years 2004-2020 in the district of Kungsholmen (annually ~1200-1800 individuals) and from 2015 and onwards in the whole municipality of Stockholm (annually ~21000 individuals). Befolkningsdelen: Ett urval av 3500 personer som är folkbokförda på Kungsholmen kallas när de fyller 60, 66, 72, 78, 81, 84, 87, 90, 93 eller 96 år. Dessa personer följs regelbundet - de yngre vart sjätte år och de äldre vart tredje. Vart sjätte år läggs en ny grupp 60-åringar till studiepopulationen. En första undersökning (baseline) genomfördes mellan åren 2001-2004, då 3363 personer deltog. Vårdsystemdelen: Vårdsystemdelen omfattar samtliga personer 65 år och äldre som beviljats insatser från den kommunala äldreomsorgen. För åren 2004-2020 inkluderas personer bosatta i stadsdelen Kungsholmen (årligen ca. 1200-1800 personer), fr.o.m. 2015 samtliga i Stockholms kommun (årligen ca. 21000 personer).

Abstract copyright UK Data Service and data collection copyright owner. The United Kingdom Study of Abuse and Neglect of Older People, carried out by the National Centre for Social Research (NatCen) and King’s College London, was commissioned by Comic Relief and the Department of Health. The survey included people aged 66 and over living in private households (including sheltered accommodation). Interviews lasted an average of 50 minutes and were conducted face to face using computer-assisted personal interviewing (CAPI), with a self-completion component for the most sensitive questions on sexual abuse. Respondents from government commissioned health surveys were followed up to obtain large, nationally representative random probability samples. In Wales, no such follow-up sample was available, so a random probability sample was selected from the comprehensive postcode address file. The overall response rate was 65%. The aim of the survey was to provide nationally representative prevalence estimates of elder abuse and neglect in the community. Five types of mistreatment were focused on; financial, physical, psychological and sexual abuse, and neglect. Main Topics: The research primarily focused on mistreatment which occurred within a relationship where there could reasonably be an expectation of trust – this included family members, close friends, and care workers. However, data was also gathered about mistreatment involving a whole range of perpetrators. As well as collecting information on mistreatment, the questionnaire covered a range of topics about older people's health and wellbeing, for example, social contact, general health, long-term illness, mental health, and economic status. Simple random sample Multi-stage stratified random sample Face-to-face interview Self-completion 2006 AGE AGEING BEDROOMS CARE OF THE ELDERLY CHILD BENEFITS CHRONIC ILLNESS CRIME VICTIMS DAY CARE DEBILITATIVE ILLNESS DOMESTIC VIOLENCE DRIVING LICENCES ECONOMIC ACTIVITY EDUCATIONAL BACKGROUND EDUCATIONAL CERTIFI... ELDER ABUSE ELDERLY EMOTIONAL STATES EMPLOYEES ETHNIC GROUPS Elderly FAMILY MEMBERS FINANCIAL CRIME FRIENDS FULL TIME EMPLOYMENT FURNISHED ACCOMMODA... GENDER General health and ... HEALTH HEALTH CONSULTATIONS HOME HELP HOME OWNERSHIP HOSPITAL OUTPATIENT... HOSPITALIZATION HOUSEHOLDS HOUSING BENEFITS HOUSING TENURE ILL HEALTH INCOME INFORMAL CARE JOB SEEKER S ALLOWANCE LANDLORDS LEISURE TIME ACTIVI... MEALS ON WHEELS MEDICINAL DRUGS MENTAL HEALTH MOBILITY SCOOTERS NATIONAL IDENTITY NEGLIGENCE LAW NEIGHBOURS OCCUPATIONAL PENSIONS OCCUPATIONAL QUALIF... OLD AGE OLD PEOPLE S CLUBS PART TIME EMPLOYMENT PENSIONS PERSONAL CONTACT PRESCRIPTION DRUGS PRIVATE PENSIONS PUBLIC TRANSPORT QUALIFICATIONS RENTED ACCOMMODATION RESIDENTIAL CARE OF... RETIREMENT SELF EMPLOYED SOCIAL ACTIVITIES L... SOCIAL CLASS SOCIAL INTERACTION SOCIAL SECURITY BEN... SOCIAL WELFARE SERV... STATE RETIREMENT PE... STATUS IN EMPLOYMENT SUPERVISORY STATUS Specific social ser... TELEPHONE HELP LINES TIED HOUSING UNFURNISHED ACCOMMO... United Kingdom

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding. The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine. The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables. MAIN RESULTS Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level. Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time. Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion. Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively). HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing. Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis. Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care. Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women. Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

This table provides estimated data for the second quarter of 2023 on the average and estimated standard deviation of the weekly hours dedicated to household care tasks in the population aged 18 and over in the Canary Islands that deals with these tasks. The information is disaggregated territorially at the level of Canary Islands.