Map shows affected population as at Nov 10th as percentage of baseline population (2020 projected) per Municipality.

Quarterly Percent Change in 3rd Month Employment Level Data 1990 - Present

  Over-the-year percent change in the third month's employment level of a given quarter (Rounded to the tenths place). County, state, and MSA level, by industry, yearly from 1990 - present. About the BLS Unemployment Data including Current Population Survey Demographic Breakdowns: Links to several different datasets, including Current Population Survey results showing seasonally adjusted unemployment data broken out by ethnicity and age, reason for unemployment, and duration of employment prior to unemployment for years including 2017-2019.  Other datasets show over-the-year percent change in the third month's employment level and taxable wages by industry for a given quarter at the County, State, and MSA level yearly from 1990 - present.
  Geography Level: State, County, MSAItem Vintage: 1990-Present
  Update Frequency: YearlyAgency: BLSAvailable File Type: Website link to CSV/Excel/Legacy Flat files download 

  Return to Other Federal Agency Datasets Page

Series Name: Proportion of the target population with access to pneumococcal conjugate 3rd dose (PCV3) (percent)Series Code: SH_ACS_PCV3Release Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programmeTarget 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for allGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of cancer (in persons of all ages). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to cancer (in persons of all ages).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (all ages) with cancer was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with cancer was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with cancer, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have cancerB) the NUMBER of people within that MSOA who are estimated to have cancerAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have cancer, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from cancer, and where those people make up a large percentage of the population, indicating there is a real issue with cancer within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of cancer, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of cancer.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.Population data: Mid-2019 (June 30) Population Estimates for Middle Layer Super Output Areas in England and Wales. © Office for National Statistics licensed under the Open Government Licence v3.0. © Crown Copyright 2020.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. © Crown Copyright 2020.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Thailand TH: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data was reported at 63.374 % in 2013. This records a decrease from the previous number of 97.285 % for 2011. Thailand TH: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data is updated yearly, averaging 96.597 % from Dec 2006 (Median) to 2013, with 4 observations. The data reached an all-time high of 97.658 % in 2009 and a record low of 63.374 % in 2013. Thailand TH: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Thailand – Table TH.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Abstract (en): The Public Use Microdata Sample (PUMS) 1-Percent Sample contains household and person records for a sample of housing units that received the "long form" of the 1990 Census questionnaire. Data items include the full range of population and housing information collected in the 1990 Census, including 500 occupation categories, age by single years up to 90, and wages in dollars up to $140,000. Each person identified in the sample has an associated household record, containing information on household characteristics such as type of household and family income. All persons and housing units in the United States. A stratified sample, consisting of a subsample of the household units that received the 1990 Census "long-form" questionnaire (approximately 15.9 percent of all housing units). 2006-01-12 All files were removed from dataset 85 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 83 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 82 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 81 and flagged as study-level files, so that they will accompany all downloads.2006-01-12 All files were removed from dataset 80 and flagged as study-level files, so that they will accompany all downloads.1998-08-28 The following data files were replaced by the Census Bureau: the state files (Parts 1-56), Puerto Rico (Part 72), Geographic Equivalency File (Part 84), and Public Use Microdata Areas (PUMAS) Crossing State Lines (Part 99). These files now incorporate revised group quarters data. Parts 201-256, which were separate revised group quarters files for each state, have been removed from the collection. The data fields affected by the group quarters data revisions were POWSTATE, POWPUMA, MIGSTATE and MIGPUMA. As a result of the revisions, the Maine file (Part 23) gained 763 records and Part 99 lost 763 records. In addition, the following files have been added to the collection: Ancestry Code List, Place of Birth Code List, Industry Code List, Language Code List, Occupation Code List, and Race Code List (Parts 86-91). Also, the codebook is now available as a PDF file. (1) Although all records are 231 characters in length, each file is hierarchical in structure, containing a housing unit record followed by a variable number of person records. Both record types contain approximately 120 variables. Two improvements over the 1980 PUMS files have been incorporated. First, the housing unit serial number is identified on both the housing unit record and on the person record, allowing the file to be processed as a rectangular file. In addition, each person record is assigned an individual weight, allowing users to more closely approximate published reports. Unlike previous years, the 1990 PUMS 1-Percent and 5-Percent Samples have not been released in separate geographic series (known as "A," "B," etc. records). Instead, each sample has its own set of geographies, known as "Public Use Microdata Areas" (PUMAs), established by the Census Bureau with assistance from each State Data Center. The PUMAs in the 1-Percent Sample are based on a distinction between metropolitan and nonmetropolitan areas. Metropolitan areas encompass whole central cities, Primary Metropolitan Statistical Areas (PMSAs), Metropolitan Statistical Areas (MSAs), or groups thereof, except where the city or metropolitan area contains more than 200,000 inhabitants. In that case, the city or metropolitan area is divided into several PUMAs. Nonmetropolitan PUMAs are based on areas or groups of areas outside the central city, PMSA, or MSA. PUMAs in this 1-Percent Sample may cross state lines. (2) The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

Ukraine UA: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data was reported at 50.961 % in 2013. This records a decrease from the previous number of 52.342 % for 2012. Ukraine UA: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data is updated yearly, averaging 51.652 % from Dec 2006 (Median) to 2013, with 4 observations. The data reached an all-time high of 52.513 % in 2006 and a record low of 50.897 % in 2011. Ukraine UA: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Map shows affected population as at 16:00 on Nov 19th as percentage of baseline population (2020 projected) per Municipality.

Philippines PH: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data was reported at 7.437 % in 2015. This records a decrease from the previous number of 9.143 % for 2013. Philippines PH: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data is updated yearly, averaging 7.437 % from Dec 2006 (Median) to 2015, with 3 observations. The data reached an all-time high of 9.143 % in 2013 and a record low of 5.659 % in 2006. Philippines PH: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Mexico Coverage: Unemployment Benefits & Active Labour Market Programs: % of Population: 3rd Quintile data was reported at 0.919 % in 2020. Mexico Coverage: Unemployment Benefits & Active Labour Market Programs: % of Population: 3rd Quintile data is updated yearly, averaging 0.919 % from Dec 2020 (Median) to 2020, with 1 observations. The data reached an all-time high of 0.919 % in 2020 and a record low of 0.919 % in 2020. Mexico Coverage: Unemployment Benefits & Active Labour Market Programs: % of Population: 3rd Quintile data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Social: Social Protection and Insurance. Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

This data collection contains detailed county and state-level ecological and descriptive data for the United States for the years 1790 to 2002. Parts 1-43 are an update to HISTORICAL, DEMOGRAPHIC, ECONOMIC, AND SOCIAL DATA: THE UNITED STATES, 1790-1970 (ICPSR 0003). Parts 1-41 contain data from the 1790-1970 censuses. They include extensive information about the social and political character of the United States, including a breakdown of population by state, race, nationality, number of families, size of the family, births, deaths, marriages, occupation, religion, and general economic condition. Parts 42 and 43 contain data from the 1840 and 1870 Censuses of Manufacturing, respectively. These files include information about the number of persons employed in various industries and the quantities of different types of manufactured products. Parts 44-50 provide county-level data from the United States Census of Agriculture for 1840 to 1900. They also include the state and national totals for the variables. The files provide data about the number, types, and prices of various agricultural products. Parts 51-57 contain data on religious bodies and church membership for 1906, 1916, 1926, 1936, and 1952, respectively. Parts 58-69 consist of data from the CITY DATA BOOKS for 1944, 1948, 1952, 1956, 1962, 1967, 1972, 1977, 1983, 1988, 1994, and 2000, respectively. These files contain information about population, climate, housing units, hotels, birth and death rates, school enrollment and education expenditures, employment in various industries, and city government finances. Parts 70-81 consist of data from the COUNTY DATA BOOKS for 1947, 1949, 1952, 1956, 1962, 1967, 1972, 1977, 1983, 1988, 1994, and 2000, respectively. These files include information about population, employment, housing, agriculture, manufacturing, retail, services, trade, banking, Social Security, local governments, school enrollment, hospitals, crime, and income. Parts 82-84 contain data from USA COUNTIES 1998. Due to the large number of variables from this source, the data were divided into into three separate data files. Data include information on population, vital statistics, school enrollment, educational attainment, Social Security, labor force, personal income, poverty, housing, trade, farms, ancestry, commercial banks, and transfer payments. Parts 85-106 provide data from the United States Census of Agriculture for 1910 to 2002. They provide data about the amount, types, and prices of various agricultural products. Also, these datasets contain extensive information on the amount, expenses, sales, values, and production of farms and machinery. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR -- https://doi.org/10.3886/ICPSR02896.v3. We highly recommend using the ICPSR version, as they made this dataset available in multiple data formats and updated the data through 2002.

Brazil BR: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data was reported at 28.970 % in 2020. This records a decrease from the previous number of 34.233 % for 2019. Brazil BR: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data is updated yearly, averaging 33.652 % from Dec 2006 (Median) to 2020, with 10 observations. The data reached an all-time high of 35.890 % in 2015 and a record low of 28.970 % in 2020. Brazil BR: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Social Protection and Insurance. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

SUMMARYIdentifies Middle Layer Super Output Areas (MSOAs) with the greatest levels of excess weight in Year 6 age children (three year average between academic years 2016/17, 2017/18, 2018/19).Although this layer is symbolised based on an overall score for excess weight, the underlying data, including the raw data for Year 6 children, is included in the dataset.ANALYSIS METHODOLOGYEach MSOA was given a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the NUMBER of Year 6 children with excess weight and;B) the PERCENTAGE of Year 6 children with excess weight.An average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of Year children with excess weight, compared to other MSOAs. In other words, those are areas where a large number of children have excess weight, and where those children make up a large percentage of the population of that age group, suggesting there is a real issue with childhood obesity in that area that needs addressing.DATA SOURCESNational Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEBased on data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. Data analysed and published by Ribble Rivers Trust © 2021.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline ("base case"). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer.

This data originates from the Public Health Outcomes tool currently presents data for available indicators for upper tier local authority levels, collated by Public Health England (PHE).

The data currently published here are the baselines for the Public Health Outcomes Framework, together with more recent data where these are available. The baseline period is 2010 or equivalent, unless these data are unavailable or not deemed to be of sufficient quality. The first data were published in this tool as an official statistics release in November 2012. Future official statistics updates will be published as part of a quarterly update cycle in August, November, February and May.

The definition, rationale, source information, and methodology for each indicator can be found within the spreadsheet.

Data included in the spreadsheet:

0.1i - Healthy life expectancy at birth
0.1ii - Life Expectancy at birth
0.1ii - Life Expectancy at 65
0.2i - Slope index of inequality in life expectancy at birth based on national deprivation deciles within England
0.2ii - Number of upper tier local authorities for which the local slope index of inequality in life expectancy (as defined in 0.2iii) has decreased
0.2iii - Slope index of inequality in life expectancy at birth within English local authorities, based on local deprivation deciles within each area
0.2iv - Gap in life expectancy at birth between each local authority and England as a whole
0.2v - Slope index of inequality in healthy life expectancy at birth based on national deprivation deciles within England
1.01i - Children in poverty (all dependent children under 20)
1.01ii - Children in poverty (under 16s)
1.02i - School Readiness: The percentage of children achieving a good level of development at the end of reception
1.02i - School Readiness: The percentage of children with free school meal status achieving a good level of development at the end of reception
1.02ii - School Readiness: The percentage of Year 1 pupils achieving the expected level in the phonics screening check
1.02ii - School Readiness: The percentage of Year 1 pupils with free school meal status achieving the expected level in the phonics screening check
1.03 - Pupil absence
1.04 - First time entrants to the youth justice system
1.05 - 16-18 year olds not in education employment or training
1.06i - Adults with a learning disability who live in stable and appropriate accommodation
1.06ii - % of adults in contact with secondary mental health services who live in stable and appropriate accommodation
1.07 - People in prison who have a mental illness or a significant mental illness
1.08i - Gap in the employment rate between those with a long-term health condition and the overall employment rate
1.08ii - Gap in the employment rate between those with a learning disability and the overall employment rate
1.08iii - Gap in the employment rate for those in contact with secondary mental health services and the overall employment rate
1.09i - Sickness absence - The percentage of employees who had at least one day off in the previous week
1.09ii - Sickness absence - The percent of working days lost due to sickness absence
1.10 - Killed and seriously injured (KSI) casualties on England's roads
1.11 - Domestic Abuse
1.12i - Violent crime (including sexual violence) - hospital admissions for violence
1.12ii - Violent crime (including sexual violence) - violence offences per 1,000 population
1.12iii- Violent crime (including sexual violence) - Rate of sexual offences per 1,000 population
1.13i - Re-offending levels - percentage of offenders who re-offend
1.13ii - Re-offending levels - average number of re-offences per offender
1.14i - The rate of complaints about noise
1.14ii - The percentage of the population exposed to road, rail and air transport noise of 65dB(A) or more, during the daytime
1.14iii - The percentage of the population exposed to road, rail and air transport noise of 55 dB(A) or more during the night-time
1.15i - Statutory homelessness - homelessness acceptances
1.15ii - Statutory homelessness - households in temporary accommodation
1.16 - Utilisation of outdoor space for exercise/health reasons
1.17 - Fuel Poverty
1.18i - Social Isolation: % of adult social care users who have as much social contact as they would like
1.18ii - Social Isolation: % of adult carers who have as much social contact as they would like
1.19i - Older people's perception of community safety - safe in local area during the day
1.19ii - Older people's perception of community safety - safe in local area after dark
1.19iii - Older people's perception of community safety - safe in own home at night
2.01 - Low birth weight of term babies
2.02i - Breastfeeding - Breastfeeding initiation
2.02ii - Breastfeeding - Breastfeeding prevalence at 6-8 weeks after birth
2.03 - Smoking status at time of delivery
2.04 - Under 18 conceptions
2.04 - Under 18 conceptions: conceptions in those aged under 16
2.06i - Excess weight in 4-5 and 10-11 year olds - 4-5 year olds
2.06ii - Excess weight in 4-5 and 10-11 year olds - 10-11 year olds
2.07i - Hospital admissions caused by unintentional and deliberate injuries in children (aged 0-14 years)
2.07i - Hospital admissions caused by unintentional and deliberate injuries in children (aged 0-4 years)
2.07ii - Hospital admissions caused by unintentional and deliberate injuries in young people (aged 15-24)
2.08 - Emotional well-being of looked after children
2.12 - Excess Weight in Adults
2.13i - Percentage of physically active and inactive adults - active adults
2.13ii - Percentage of active and inactive adults - inactive adults
2.14 - Smoking Prevalence
2.14 - Smoking prevalence - routine & manual
2.15i - Successful completion of drug treatment - opiate users
2.15ii - Successful completion of drug treatment - non-opiate users
2.17 - Recorded diabetes
2.18 - Alcohol related admissions to hospital
2.19 - Cancer diagnosed at early stage (Experimental Statistics)
2.20i - Cancer screening coverage - breast cancer
2.20ii - Cancer screening coverage - cervical cancer
2.21vii - Access to non-cancer screening programmes - diabetic retinopathy
2.22iii - Cumulative % of the eligible population aged 40-74 offered an NHS Health Check
2.22iv - Cumulative % of the eligible population aged 40-74 offered an NHS Health Check who received an NHS Health Check
2.22v - Cumulative % of the eligible population aged 40-74 who received an NHS Health check
2.23i - Self-reported well-being - people with a low satisfaction score
2.23ii - Self-reported well-being - people with a low worthwhile score
2.23iii - Self-reported well-being - people with a low happiness score
2.23iv - Self-reported well-being - people with a high anxiety score
2.24i - Injuries due to falls in people aged 65 and over (Persons)
2.24i - Injuries due to falls in people aged 65 and over (males/females)
2.24ii - Injuries due to falls in people aged 65 and over - aged 65-79
2.24iii - Injuries due to falls in people aged 65 and over - aged 80+
3.01 - Fraction of mortality attributable to particulate air pollution
3.02i - Chlamydia screening detection rate (15-24 year olds) - Old NCSP data
3.02ii - Chlamydia screening detection rate (15-24 year olds) - CTAD
3.03i - Population vaccination coverage - Hepatitis B (1 year old)
3.03i - Population vaccination coverage - Hepatitis B (2 years old)
3.03iii - Population vaccination coverage - Dtap / IPV / Hib (1 year old)
3.03iii - Population vaccination coverage - Dtap / IPV / Hib (2 years old)
3.03iv - Population vaccination coverage - MenC
3.03v - Population vaccination coverage - PCV
3.03vi - Population vaccination coverage - Hib / MenC booster (2 years old)
3.03vi - Population vaccination coverage - Hib / Men C booster (5 years)
3.03vii - Population vaccination coverage - PCV booster
3.03viii - Population vaccination coverage - MMR for one dose (2 years old)
3.03ix - Population vaccination coverage - MMR for one dose (5 years old)
3.03x - Population vaccination coverage - MMR for two doses (5 years old)
3.03xii - Population vaccination coverage - HPV
3.03xiii - Population vaccination coverage - PPV
3.03xiv - Population vaccination coverage - Flu (aged 65+)
3.03xv - Population vaccination coverage - Flu (at risk individuals)
3.04 - People presenting with HIV at a late stage of infection
3.05i - Treatment completion for TB
3.05ii - Incidence of TB
3.06 - NHS organisations with a board approved sustainable development management plan
4.01 - Infant mortality
4.02 - Tooth decay in children aged 5
4.03 - Mortality rate from causes considered preventable
4.04i - Under 75 mortality rate from all cardiovascular diseases
4.04ii - Under 75 mortality rate from cardiovascular diseases considered preventable
4.05i - Under 75 mortality rate from cancer
4.05ii - Under 75 mortality rate from cancer considered preventable
4.06i - Under 75 mortality rate from liver disease
4.06ii - Under 75 mortality rate from liver disease considered preventable
4.07i - Under 75 mortality rate from respiratory disease
4.07ii - Under 75 mortality rate from respiratory disease considered preventable
4.08 - Mortality from communicable diseases
4.09 - Excess under 75 mortality rate in adults with serious mental illness
4.10 - Suicide rate
4.11 - Emergency readmissions within 30 days of discharge from hospital
4.12i - Preventable sight loss - age related macular degeneration (AMD)
4.12ii - Preventable sight loss - glaucoma
4.12iii - Preventable sight loss - diabetic eye disease
4.12iv - Preventable sight loss - sight loss certifications
4.14i - Hip fractures in

The Landscape Project combines documented wildlife locations with NJDEP aerial photo-based Land Use/Land Cover (LULC) to delineate imperiled and special concern species habitat within New Jersey. Many species occurrence locations cannot be published because they may represent nest sites, roost sites, dens and other sites used by species that are vulnerable to human disturbance and, in some cases, susceptible to illegal collection. At the same time, wildlife moves, as individual animals use various habitat features within the landscape to fulfill their foraging, sheltering and breeding needs. Therefore, protecting individual occurrences or the area used by one individual is generally not sufficient to protect the local population. Landscape Project maps address these issues by displaying habitat patches that animals use and that are required to support local populations, rather than pinpointing exact locations of the most sensitive wildlife sites or simply protecting points where species happened to be observed at one point in time. Prior to combining species occurrence data with LULC data to form the habitat patches that make up the Species-Based Habitat layer, each dataset was generated according to a specific data development process.

Poland PL: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data was reported at 34.598 % in 2012. This records a decrease from the previous number of 36.258 % for 2011. Poland PL: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data is updated yearly, averaging 38.956 % from Dec 2005 (Median) to 2012, with 8 observations. The data reached an all-time high of 47.756 % in 2005 and a record low of 34.598 % in 2012. Poland PL: Coverage: Social Safety Net Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Poland – Table PL.World Bank.WDI: Social Protection. Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Sri Lanka LK: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data was reported at 5.755 % in 2012. This records an increase from the previous number of 4.761 % for 2009. Sri Lanka LK: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data is updated yearly, averaging 5.755 % from Dec 2006 (Median) to 2012, with 3 observations. The data reached an all-time high of 5.786 % in 2006 and a record low of 4.761 % in 2009. Sri Lanka LK: Coverage: Social Insurance Programs: 3rd Quintile: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank.WDI: Social Protection. Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

SUMMARYIdentifies Middle Layer Super Output Areas (MSOAs) with the greatest levels of excess weight in Reception age children (three year average between academic years 2016/17, 2017/18, 2018/19).Although this layer is symbolised based on an overall score for excess weight, the underlying data, including the raw data for Reception children, is included in the dataset.ANALYSIS METHODOLOGYEach MSOA was given a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the NUMBER of Reception children with excess weight and;B) the PERCENTAGE of Reception children with excess weight.An average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of Reception children with excess weight, compared to other MSOAs. In other words, those are areas where a large number of children have excess weight, and where those children make up a large percentage of the population of that age group, suggesting there is a real issue with childhood obesity in that area that needs addressing.DATA SOURCESNational Child Measurement Programme: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.COPYRIGHT NOTICEBased on data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021. Data analysed and published by Ribble Rivers Trust © 2021.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

The Landscape Project combines documented wildlife locations with NJDEP aerial photo-based Land Use/Land Cover (LULC) to delineate imperiled and special concern species habitat within New Jersey. Many species occurrence locations cannot be published because they may represent nest sites, roost sites, dens and other sites used by species that are vulnerable to human disturbance and, in some cases, susceptible to illegal collection. At the same time, wildlife moves, as individual animals use various habitat features within the landscape to fulfill their foraging, sheltering and breeding needs. Therefore, protecting individual occurrences or the area used by one individual is generally not sufficient to protect the local population. Landscape Project maps address these issues by displaying habitat patches that animals use and that are required to support local populations, rather than pinpointing exact locations of the most sensitive wildlife sites or simply protecting points where species happened to be observed at one point in time. Prior to combining species occurrence data with LULC data to form the habitat patches that make up the Species-Based Habitat layer, each dataset was generated according to a specific data development process.