Note: These statistics are published as Official Statistics. Users should be cautious making comparisons between local authorities, or across years due to changing reporting practices - see the methodology document for further information. Children looked after who were missing. Figures by duration of missing periods, placement from which the child went missing and age of child at start of missing incident. Data formerly in table G1.

The number of children missing education in England at any point in the academic year (where a child has multiple episodes missing education they are counted only once).

This data collection represents the empirical materials collected from the ESRC project 'Geographies of Missing People'. It comprises 45 interviews with people previously reported as missing, 9 charity workers, 23 police officers of various ranks and 25 families of missing people. We request that other researchers who wish to reuse our data get in touch to dialogue with the research team about how and why they want to reuse this data. The data is accessible with direct permission from the PI of the original ESRC award: Hester.parr@glasgow.ac.ukThis project seeks to understand the realities involved in 'going missing', and does so from multiple perspectives; using the voices and opinions of the police, families and returned missing people themselves. Qualitative data has been collected to shed light on this significant social (and spatial) problem and help us understand more about the nature of missing experiences for different groups. The purpose of the research project has been to understand more about how people go missing and how the police and families respond to such events (the geographies of searching). Such a focus holds value for both the police and families (the 'left behind') in that it updates and checks current knowledge about the likely spatial experiences of missing people. The project has recruited 45 people formally reported as missing to the project; 9 charity workers in the field of missing persons; 23 police officers of various ranks and 25 family members and these are held by the data archive service. Permission to access from Hester.parr@glasgow.ac.uk Interviews and focus groups. Sampling methods are profiled in the main reports lodged on www.geographiesofmissingpeople.org.uk

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.

Local authority level data for: 1. Children looked after on 31 March; 2. Children who started to be looked after during the year; 3. Children who ceased to be looked after during the year. Data includes numbers of looked after children, formerly in tables LAA1, LAC1, LAD1. Note: Figures for children looked after during the year can be found in the LA dataset on children who have gone missing or who are away from placement without authorisation. For some local authorities, the figures may be impacted by significant numbers of unaccompanied asylum seeking children.

Context

The early identification of students facing learning difficulties is one of the most critical challenges in modern education. Intervening effectively requires leveraging data to understand the complex interplay between student demographics, engagement patterns, and academic performance.

This dataset was created to serve as a high-quality, pre-processed resource for building machine learning models to tackle this very problem. It is a unique hybrid dataset, meticulously crafted by unifying three distinct sources:

The Open University Learning Analytics Dataset (OULAD): A rich dataset detailing student interactions with a Virtual Learning Environment (VLE). We have aggregated the raw, granular data (over 10 million interaction logs) into powerful features, such as total clicks, average assessment scores, and distinct days of activity for each student registration.

The UCI Student Performance Dataset: A classic educational dataset containing demographic information and final grades in Portuguese and Math subjects from two Portuguese schools.

A Synthetic Data Component: A synthetically generated portion of the data, created to balance the dataset or represent specific student profiles.

Data Unification and Pre-processing

A direct merge of these sources was not possible as the student identifiers were not shared. Instead, a strategy of intelligent concatenation was employed. The final dataset has undergone a rigorous pre-processing pipeline to make it immediately usable for machine learning tasks:

• Advanced Imputation: Missing values were handled using a sophisticated iterative imputation method powered by Gaussian Mixture Models (GMM), ensuring the dataset's integrity.

• One-Hot Encoding: All categorical features have been converted to a numerical format.

• Feature Scaling: All numerical features have been standardized (using StandardScaler) to have a mean of 0 and a standard deviation of 1, preventing model bias from features with different scales.

The result is a clean, comprehensive dataset ready for modeling.

File Information

Instance

Each row represents a student profile, and the columns are the features and the target.

Feature

Features include aggregated online engagement metrics (e.g., clicks, distinct activities), academic performance (grades, scores), and student demographics (e.g., gender, age band). A key feature indicates the original data source (OULAD, UCI, Synthetic).

Sensitive Information

The dataset contains no Personally Identifiable Information (PII). Demographic information is presented in broad, anonymized categories.

Key Columns:

Target Variable:

  had_difficulty: The primary target for classification. This binary variable has been engineered from the original final_result column of the OULAD dataset.

    1: The student either failed (Fail) or withdrew (Withdrawn) from the course.

    0: The student passed (Pass or Distinction).

Feature Groups:

  OULAD Aggregated Features (e.g., oulad_total_cliques, oulad_media_notas): Quantitative metrics summarizing a student's engagement and performance within the VLE.

  Academic Performance Features (e.g., nota_matematica_harmonizada): Harmonized grades from different data sources.

  Demographic Features (e.g., gender_*, age_band_*): One-hot encoded columns representing student demographics.

  Origin Features (e.g., origem_dado_OULAD, origem_dado_UCI): One-hot encoded columns indicating the original source of the data for each row. This allows for source-specific analysis.

(Note: All numerical feature names are post-scaling and may not directly reflect their original names. Please refer to the complete column list for details.)

Acknowledgements

This dataset would not be possible without the original data providers. Please acknowledge them in any work that uses this data:

OULAD Dataset: Kuzilek, J., Hlosta, M., and Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4. https://analyse.kmi.open.ac.uk/open_dataset

UCI Student Performance Dataset: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS. https://archive.ics.uci.edu/ml/datasets/student+performance

Inspiration

This dataset is perfect for a variety of predictive modeling tasks. Here are a few ideas to get you started:

Can you build a classification model to predict had_difficulty with high recall? (Minimizing the number of at-risk students we fail to identify).

• Which features are the most powerful predictors of student failure or withdrawal? (Feature Importance Analysis).

• Can you build separate models for each data origin (origem_dado_*) and compare ...

Abstract copyright UK Data Service and data collection copyright owner.The Crime Survey for England and Wales (CSEW) asks a sole adult, in a random sample of households, about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS) these variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range is also selected from the household and asked about their experience of crime, and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.The CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this. Further information may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page. Secure Access dataIn addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).New methodology for capping the number of incidents from 2017-18The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’. Extending the BCS to Children Following the 'Smith Review', 'Crime statistics: an independent review', the BCS has included children aged 10-15 years, through screening at sampled addresses. The British Crime Survey Experimental Data: Children Aged 10-15 Years, January-December, 2009 comprises the first set of BCS children's data to become available. The primary aim of extending the BCS to children is to provide estimates of the levels of crime experienced by children and their risk of victimisation. In addition to questions about experience of crime, the BCS children's survey also gathers information on a number of crime-related topics such as perceptions and attitudes to the police, anti-social behaviour, crime prevention and personal security. It is therefore envisaged that the BCS children's survey will also provide a rich source of data to assist in understanding the nature and circumstances of crimes experienced by children aged 10 to 15 years. Estimates published based on these data have been published as 'Experimental Statistics'. In accordance with the Code of Practice for Official Statistics, 'Experimental Statistics' are new official statistics undergoing evaluation and published to involve users and stakeholders in their development as a means to build in quality at an early stage. As such, these data are subject to future revision and change. Comparing the children's data with the adult BCS The BCS children's experimental dataset has been developed as an extension to the existing adult BCS to children aged 10 to 15 years. However, methodological differences between the adult and child BCS mean that direct comparisons cannot be made between the adult and child data. That said, while the questions asked and levels of detail collected differ between the data sources there is a common approach to the classification of incidents to offences in law. For further details of the methodology, see documentation. Findings publication The findings from the first set of children's data have been published as: Millard, B. and Flatley, J. (ed.) (2010) Experimental statistics on victimisation of children aged 10 to 15: findings from the British Crime Survey for the year ending December 2009 England and Wales, Home Office Statistical Bulletin 11/01, London: Home Office. Retrieved October 26, 2010 from http://www.homeoffice.gov.uk/rds/pdfs10/hosb1110.pdf Main Topics: The BCS children's data were collected using a shortened questionnaire based on the adult BCS questionnaire but restricted to incidents of personal victimisation in the 12 months prior to interview. Topics covered included: demographics, crime screener questions, perceptions of and attitudes to the police, anti-social behavious, crime prevention and security. A self-completion module covered internet use, personal security, school truancy, bullying, street gangs, drinking behaviour and cannabis use. Questions asked of adults about household crimes, such as burglary or vehicle-related crime, were not included in the child survey as this information was already captured from the adult interview. Users should also note that the self-completion module for the extension of the BCS to children collects sensitive data from those aged 10 to 15 and is not available to download. Multi-stage stratified random sample Face-to-face interview Computer-Assisted Personal Interviewing (CAPI) was used. Self-completion 2009 ADMINISTRATION OF J... ADOLESCENTS ADVICE AGE AGGRESSIVENESS ALCOHOL USE ANGER ANTISOCIAL BEHAVIOUR ASSAULT ATTITUDES BICYCLES BINGE DRINKING BULLYING BURGLARY CAMERAS CANNABIS CAR PARKING AREAS CHILDREN CHRONIC ILLNESS CLUBS COMMUNITIES COMMUNITY ACTION COMMUNITY BEHAVIOUR COMPUTERS COSTS COUNSELLING CRIME AND SECURITY CRIME PREVENTION CRIME VICTIMS CRIMINAL DAMAGE CRIMINAL INVESTIGATION CRIMINAL JUSTICE SY... CRIMINALS CULTURAL GOODS CULTURAL IDENTITY Crime and law enfor... DAMAGE DEBILITATIVE ILLNESS DISCIPLINE DOMESTIC RESPONSIBI... DRINKING BEHAVIOUR DRUG ABUSE ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL ATTENDANCE EDUCATIONAL ENVIRON... EDUCATIONAL PERSONNEL ELECTRONIC MAIL EMERGENCY AND PROTE... EMOTIONAL DISTURBANCES EMOTIONAL STATES EMPLOYEES EMPLOYMENT EMPLOYMENT HISTORY ETHNIC CONFLICT ETHNIC GROUPS EVERYDAY LIFE EXPOSURE TO NOISE England and Wales FAMILIES FAMILY MEMBERS FEAR FEAR OF CRIME FINANCIAL COMPENSATION FINANCIAL RESOURCES FRIENDS GENDER HARASSMENT HEADS OF HOUSEHOLD HEALTH HEALTH STATUS HOME OWNERSHIP HOSPITALIZATION HOURS OF WORK HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLDS HOUSING TENURE INDUSTRIES INJURIES INTERNET ACCESS INTERNET USE INTERPERSONAL COMMU... INTERPERSONAL CONFLICT INTERPERSONAL RELAT... JUVENILE DELINQUENCY LANDLORDS LAW ENFORCEMENT LEGAL PROCEDURE LEISURE TIME ACTIVI... LOCAL GOVERNMENT SE... LOCKS MARITAL STATUS MEDICAL CARE MOBILE PHONES NEIGHBOURHOODS NEIGHBOURS OFFENCES OFFENSIVE TELEPHONE... ONLINE SHOPPING PARENTS PERSONAL CONTACT PERSONAL FASHION GOODS PERSONAL IDENTIFICA... PERSONAL SAFETY POLICE OFFICERS POLICE SERVICES POLICING PUBLIC HOUSES PUBLIC TRANSPORT QUALIFICATIONS QUALITY OF LIFE RECIDIVISM REFUSE RENTED ACCOMMODATION RESIDENTIAL MOBILITY RESPONSIBILITY RISK ROBBERY SCHOOL DISCIPLINE SCHOOL PUNISHMENTS SECURITY SYSTEMS SELF EMPLOYED SIBLINGS SMOKING SOCIAL ACTIVITIES L... SOCIAL HOUSING SOCIAL PARTICIPATION SOCIAL SUPPORT SORROW STUDENT BEHAVIOUR STUDENTS Social behaviour an... THEFT TRAINING COURSES TRUANCY UNDERAGE DRINKING UNEMPLOYMENT UNWAGED WORKERS VAGRANTS VISITS PERSONAL VOLUNTARY WELFARE O... WEAPONS WORKPLACE YOUTH YOUTH CRIME YOUTH CULTURE YOUTH EMPLOYMENT YOUTH GANGS YOUTH UNEMPLOYMENT Youth

Dataset of interview and questionnaire data resulting from the age 24 wave with the original participants of the Manchester Language Study in adulthood (24 years of age). The Manchester Language Study is a longitudinal study of a national random sample of all children who were attending language units. The study covers a 20 year period. It began in 1995 when the children were 7 years of age. In this young adulthood phase we undertook interviews with 84 participants with a history of Language Impairment (LI) and a comparison group of 88 age-matched peers (AMP) who had no history of special educational needs or speech and language therapy provision. We also collected data via questionnaires from a close relative or friend they nominated themselves. Missing values(216) are dropouts from previous waves. The interviews were extensive covering personal and social functioning and societal engagement. The personal domain includes general health (weight, exercise, leisure, diet, smoking, alcohol, drugs), mental health (anxiety, depression) and educational/training qualifications. The social domain includes personal relationships (marital status, children, friendships, stable partnerships, parents, siblings) and social adjustment (aggression/criminality). Societal engagement includes employment (including occupational adjustment), independence (living context, transport, driving), finances (banking, financial literacy, debt, gambling, receipt of benefits) civic participation (voting, volunteering), TV viewing and new media use (computers, mobile phones). Research activity includes (1) the identification of the range and profile of personal, social and societal (PSS) functioning in young adults with a history of LI, (2) the examination of concurrent relationships among individuals’ attributes, environmental factors and PSS functioning leading to a number of discoveries, for example, the discovery that prosociality is one of the key protective factor associated with most areas of functioning in individuals with LI in young adulthood and (3) the identification of predictors of distinct development pathways of adjustment in social, emotional, behavioural and employment/education outcomes in young adulthood. Language impairment (LI) affects one in fifteen children in the UK. LI involves problems with talking and with understanding spoken language. These difficulties are usually not transient. However, there is limited information about how these children “turn out” in adulthood. This project aims to fill this knowledge gap. It is based on the Manchester Language Study, the largest UK study of individuals with a history of SLI. The original cohort was a random sample of all 7 year old children who were attending language units in England in 1995. These individuals participated in this project when they were aged between 23-25 years of age. A range of areas of functioning were examined in adulthood, in the personal, social and societal domains. For example: general health (exercise, diet), personal relationships, education, employment, finances, and civic participation (voting, volunteering). Quantitative as well as qualitative data was gathered via direct assessment, participants’ self-reports, reports from significant others, and consultation with national records. The project identifies pathways to positive adjustment (resilience) as well as risk pathways in adulthood. Structured interviews, questionnaire, psycholinguistic and psychometric assessments with 84 participants with a history of Language Impairment (LI) and a comparison group of 88 age-matched peers (AMP) who had no history of special educational needs or speech and language therapy provision. We developed a structured interview for the specific purposes of this phase of the Manchester Language Study. The interview had a number of sections with questions relevant to the areas examined. The questions and response options were taken from two main sources: a) national surveys that have been widely used and for which there are national statistics available for comparison purposes, for example, The Office for National Statistics, and b) scales that have been widely used in previous research with demonstrated reliability and validity, for example, The Beck Anxiety Inventory (BAI, Beck, A. T., & Steer, R. A. (1990). Beck Anxiety Inventory. London: Psychological Corporation) and the Rosenberg Self-esteem scale (Rosenberg, M. (1965). The measurement of self-esteem. Society and the Adolescent Self Image, 297, V307. Princeton, NJ: Princeton University Press).

SUMMARYTo be viewed in combination with the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.This dataset shows where there was no data* relating to one of more of the following factors:Obesity/inactivity-related illnesses (recorded at the GP practice catchment area level*)Adult obesity (recorded at the GP practice catchment area level*)Inactivity in children (recorded at the district level)Excess weight in children (recorded at the Middle Layer Super Output Area level)* GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices.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. This dataset identifies areas where data from 2019/20 was used, where one or more GPs did not submit data in either year (this could be because there are rural areas that aren’t officially covered by any GP practices), 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.Results of the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ analysis in these areas should be interpreted with caution, particularly if the levels of obesity, inactivity and associated illnesses appear to be significantly lower than in their immediate surrounding areas.Really small areas with ‘missing’ data were deleted, where it was deemed that missing data will not have impacted the overall analysis (i.e. where GP data was missing from really small countryside areas where no people live).See also Health and wellbeing statistics (GP-level, England): Missing data and potential outliers dataDATA 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.- National 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. - Active Lives Survey 2019: Sport and Physical Activity Levels amongst children and young people in school years 1-11 (aged 5-16). © Sport England 2020.- Active Lives Survey 2019: Sport and Physical Activity Levels amongst adults aged 16+. © Sport England 2020.- 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.- Administrative boundaries: Boundary-LineTM: Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.- MSOA boundaries: © Office for National Statistics licensed under the Open Government Licence v3.0. Contains OS data © Crown copyright and database right 2021.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; © Sport England 2020; © Office for National Statistics licensed under the Open Government Licence v3.0. Contains Ordnance Survey data © Crown copyright and database right 2021. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

People who are registered as deaf or hard of hearing that are also blind or partially sighted are recorded on the Register of Blind and Partially Sighted Persons (SSDA 902 form), unless stated these are excluded from this report. Data on these by category of disability is available here: http://data.london.gov.uk/dataset/number-registered-blind-and-partially-sighted-people-additional-disability-categor and by age here: http://data.london.gov.uk/dataset/number-registered-blind-and-partially-sighted-people-age-group-borough All ages total includes some cases where the age was not known. Therefore the age groups may not add to the total. Regional totals are estimated to take account of missing data. Dash ("-") means a local authority was unable to submit details on the number of people registered as being deaf and hard of hearing.

Availability of sophisticated statistical modelling for developing robust reference equations has improved interpretation of lung function results. In 2012, the Global Lung function Initiative(GLI) published the first global all-age, multi-ethnic reference equations for spirometry but these lacked equations for those originating from the Indian subcontinent (South-Asians). The aims of this study were to assess the extent to which existing GLI-ethnic adjustments might fit South-Asian paediatric spirometry data, assess any similarities and discrepancies between South-Asian datasets and explore the feasibility of deriving a suitable South-Asian GLI-adjustment.MethodsSpirometry datasets from South-Asian children were collated from four centres in India and five within the UK. Records with transcription errors, missing values for height or spirometry, and implausible values were excluded(n = 110).ResultsFollowing exclusions, cross-sectional data were available from 8,124 children (56.3% male; 5–17 years). When compared with GLI-predicted values from White Europeans, forced expired volume in 1s (FEV1) and forced vital capacity (FVC) in South-Asian children were on average 15% lower, ranging from 4–19% between centres. By contrast, proportional reductions in FEV1 and FVC within all but two datasets meant that the FEV1/FVC ratio remained independent of ethnicity. The ‘GLI-Other’ equation fitted data from North India reasonably well while ‘GLI-Black’ equations provided a better approximation for South-Asian data than the ‘GLI-White’ equation. However, marked discrepancies in the mean lung function z-scores between centres especially when examined according to socio-economic conditions precluded derivation of a single South-Asian GLI-adjustment.ConclusionUntil improved and more robust prediction equations can be derived, we recommend the use of ‘GLI-Black’ equations for interpreting most South-Asian data, although ‘GLI-Other’ may be more appropriate for North Indian data. Prospective data collection using standardised protocols to explore potential sources of variation due to socio-economic circumstances, secular changes in growth/predictors of lung function and ethnicities within the South-Asian classification are urgently required.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (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.For each of the above illnesses, the percentage of each MSOA’s population with that illness 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 patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness 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 each illness, within the relevant age range.For each illness, 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 that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn 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 predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). 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 adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.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 excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn 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 predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn 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 district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, 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 ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ 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, 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

The number of years of life lost by every 100,000 persons aged 0 to 19 dying from a condition which is usually treatable, measured in a way which allows for comparisons between populations with different age profiles and over time. Purpose To ensure that the NHS is held to account for doing all that it can to prevent amenable deaths. Deaths from causes considered ‘amenable’ to healthcare are premature deaths that should not occur in the presence of timely and effective healthcare. Current version updated: Nov-15 Next version due: To be confirmed

Abstract copyright UK Data Service and data collection copyright owner. The Department for Education (DfE) commissioned the Our Future study (also known as the Second Longitudinal Study of Young People in England (LSYPE2)) at the beginning of 2013. This is one of the largest and most challenging studies of young people ever commissioned and aims to build upon the Next Steps study (LSYPE1), which began in 2004, following young people from the age of 13/14 onwards (Next Steps is held at the UK Data Archive under SN 5545 (End User Licence) and SN 7104 (Secure Access)). The purposes of Our Future are:to follow a sample of young people through the final years of compulsory education; through their transition from compulsory education to other forms of education, training employment, and other activitiesto collect information about their career paths and about the factors affecting them; andto provide a strategic evidence base about the lives and experiences of young peopleIt is intended that Our Future will track a sample of over 13,000 young people from the age of 13/14 annually through to the age of 20 (seven waves). The study currently includes data from Wave 1 to 3 of Our Future. Face-to-face interviews with both the young people and their parents were conducted between April and September 2013 when the young people were 13/14 (in school Year 9) for Wave 1, between April and September 2014 when the young people were 14/15 (in Year 10) for Wave 2 and between April and September 2015 when the young people were 15/16 (in Year 11) for Wave 3. Besides the Safe Room Access version, a Secure Access version (SN 7838) is available. For the second edition (March 2018), data and documentation for Waves 2 and 3 were added to the study. Also included is a NPD linked data file containing linked pupil-level KS2 results and two datasets to support analysis with missing data for KS2 attainment for pupils who attended boycott schools in 2010. Further information is available in the User Guide. Main Topics: The Our Future survey covers a wide range of topics from the main parent, second parent and young person interviews, including:the young person's family backgroundparental socio-economic statuspersonal characteristicsattitudes, experiences and behavioursparental employmentincome and family environment as well as local deprivationthe school(s) the young person attends/has attendedthe young person's future plansThe Safe Room Access version includes a general survey data file that has similar variables to the End User Licence dataset, plus the majority of sensitive derived, sample, geodemographic and survey variables excluded from the End User Licence file. Some of the most sensitive variables remain anonymised in this file. This file is accompanied by three files of National Pupil Database (NPD) data, which exclude all sensitive variables:school-level census data about the school the young person attended, from 2006, 2010 and 2013, i.e. the years they completed Key Stage (KS) 1, KS2 and KS3; this also includes Ofsted ratings and geodemographic datapupil-level data about the young person's KS1 attainment, from 2006school-level data about the KS1 and KS4 levels of attainment in the school the young person attended, from 2006 and 2013, respectivelyIn addition, the Safe Room Access version also includes four datasets containing the most sensitive survey and geodemographic variables, covering:detailed characteristicsincomehealthcareThe survey data files in the Safe Room Access version include the detailed geographical variables Local Authority Districts and Super Output Areas (Lower Layer). In addition, the NPD files also include Parliamentary Constituencies and Wards (Census Area Statistics). The above data files are also included in the Secure Access version of Our Future (SN 7838). The Safe Room Access version also includes an additional data file not available elsewhere: a pupil-level NPD file, containing particularly sensitive information about the young person such as their ethnicity, free school meal status and Special Educational Needs status.

Percentage of 5 year olds with dental decay extending to the dentine layer which can be detected by visual observation aloneRationaleOral health is an integral part of overall health; when children are not healthy this affects their ability to learn, thrive and develop. This indicator therefore links to a key policy: Getting the Best Start in Life. Poor oral health is a priority under Best Start in Life, it was also a topic of a Health Select Committee inquiry, and the most common cause of hospital admission for 5 to 9 year olds. This indicator allows benchmarking of oral health of young children across England, and is an excellent proxy measure of assessing the impact of the commissioning of oral health improvement programmes on the local community. Dental caries is a synonymous term for tooth decay.Definition of numeratorNumber of 5 year olds in a given area with at least one tooth decayed, missing or filledDefinition of denominatorNumber of 5 year olds examined for a given areaCaveatsNot all local authorities have taken part in the survey. This means that for any child who has been examined whose LA of residence has not taken part in the survey, their figures will be included in national, regional, deprivation and ethnicity breakdowns, but will not appear in the local authority breakdown. Details are available at https://www.gov.uk/government/collections/oral-health#surveys-and-intelligence:-children

Abstract copyright UK Data Service and data collection copyright owner.The Crime Survey for England and Wales (CSEW) asks a sole adult, in a random sample of households, about their, or their household's, experience of crime victimisation in the previous 12 months. These are recorded in the victim form data file (VF). A wide range of questions are then asked covering demographics and crime-related subjects such as attitudes to the police and the criminal justice system (CJS) these variables are contained within the non-victim form (NVF) data file. In 2009, the survey was extended to children aged 10-15 years old; one resident of that age range is also selected from the household and asked about their experience of crime, and other related topics. The first set of children's data covered January-December 2009 and is held separately under SN 6601. From 2009-2010, the children's data cover the same period as the adult data and are included with the main study.The CSEW was formerly known as the British Crime Survey (BCS), and has been in existence since 1981. The 1982 and 1988 BCS waves were also conducted in Scotland (data held separately under SNs 4368 and 4599). Since 1993, separate Scottish Crime and Justice Surveys have been conducted. Up to 2001, the BCS was conducted biennially. From April 2001, the Office for National Statistics took over the survey and it became the CSEW. Interviewing was then carried out continually and reported on in financial year cycles. The crime reference period was altered to accommodate this. Further information may be found on the ONS Crime Survey for England and Wales web page and for the previous BCS, from the GOV.UK BCS Methodology web page. Secure Access dataIn addition to the main survey, a series of questions covering drinking behaviour, drug use, self-offending, gangs and personal security, and intimate personal violence (IPV) (including stalking and sexual victimisation) are asked of adults via a laptop-based self-completion module (questions may vary over the years). Children aged 10-15 years also complete a separate self-completion questionnaire. The questionnaires are included in the main documentation, but the data are only available under Secure Access conditions (see SN 7280), not with the main study. In addition, from 2011 onwards, lower-level geographic variables are also available under Secure Access conditions (see SN 7311).New methodology for capping the number of incidents from 2017-18The CSEW datasets available from 2017-18 onwards are based on a new methodology of capping the number of incidents at the 98th percentile. Incidence variables names have remained consistent with previously supplied data but due to the fact they are based on the new 98th percentile cap, and old datasets are not, comparability has been lost with years prior to 2012-2013. More information can be found in the 2017-18 User Guide (see SN 8464) and the article ‘Improving victimisation estimates derived from the Crime Survey for England and Wales’. The central aim of the first BCS was to estimate the incidence of victimisation of selected types of crime among the adult population over a given period, to describe the circumstances under which people became victims of crime and assess the consequences for them of becoming victims. The design of the survey drew very heavily on experience from previous victim surveys, particularly the U.S. National Crime Survey and victim surveys in Canada and the Netherlands. The design of the first BCS however, had some individual features arising from its particular objectives and the circumstances and constraints under which it was carried out. These features are described in more detail in the publication by Hough and Mayhew (1983), listed on the Publications page. This first sweep of the BCS was also conducted in Scotland, as well as in England and Wales. This study, SN 1869, includes only the data for England and Wales; the Scottish data are held separately under SN 4368. Users who need data for all three countries (Scotland, England and Wales) should order both datasets. For the third edition (December 2006), the depositor supplied a new version of the non-victim form data file, with many variable and value labels added where none previously existed. The victim form data file currently remains unlabelled. Main Topics: Respondents were asked a series of screening questions to establish whether or not they had been the victims of crime during the reference period, and a series of very detailed questions about the incidents they reported. Basic descriptive background information on the respondents and their households was also collected to allow analysis of the sorts of people who do and do not become victims. Other information collected was on fear of crime, contact with the police, lifestyle, and self-reported offending. Multi-stage stratified random sample Face-to-face interview Self-completion 1982 ADVICE AGE ALCOHOL USE ALCOHOLIC DRINKS ANXIETY ARREST ASSAULT ATTITUDES BICYCLES BUILDINGS BURGLARY CAR PARKING AREAS CHILDREN CLUBS COMMUTING CONDITIONS OF EMPLO... CONSUMER GOODS CONVENTIONAL WEAPONS COSTS CRIME AND SECURITY CRIME PREVENTION CRIME VICTIMS CRIMINAL DAMAGE CRIMINAL INVESTIGATION CRIMINALS CULTURAL GOODS Crime and law enfor... DISTANCE MEASUREMENT DOGS DOMESTIC APPLIANCES DOMESTIC RESPONSIBI... DOMESTIC SAFETY DRINKING OFFENCES DRIVING ECONOMIC ACTIVITY ECONOMIC VALUE EDUCATIONAL BACKGROUND EMOTIONAL STATES EMPLOYMENT ETHNIC GROUPS England and Wales FAMILY MEMBERS FEAR OF CRIME FINANCIAL COMPENSATION FIRE FRIENDS FULL TIME EMPLOYMENT GENDER GOVERNMENT ORGANIZA... HEADS OF HOUSEHOLD HOME OWNERSHIP HOUSEHOLD HEAD S EC... HOUSEHOLD HEAD S OC... HOUSEHOLD INCOME HOUSEHOLDS HOUSING HOUSING TENURE HUMAN BEHAVIOUR HUMAN SETTLEMENT INDUSTRIES INJURIES INSURANCE INTERPERSONAL CONFLICT INTERPERSONAL RELAT... INTRUDER ALARM SYSTEMS JOB DESCRIPTION JOB HUNTING JOB REQUIREMENTS JUDGMENTS LAW LANDLORDS LEAVE LEISURE TIME ACTIVI... LOCATION LOCKS MARITAL STATUS MEDICAL CARE MONEY MOTOR VEHICLES NEIGHBOURHOODS OCCUPATIONS OFFENCES OFFENSIVE TELEPHONE... PART TIME EMPLOYMENT PAYMENTS PERFORMING ARTS PERSONAL CONTACT PERSONAL SAFETY PHYSICIANS POLICE COMMUNITY RE... POLICE SERVICES POLICING PRISON SENTENCES PUNISHMENT RELIGIOUS ATTENDANCE RENTED ACCOMMODATION RESIDENTIAL MOBILITY RETIREMENT ROAD ACCIDENTS ROBBERY SATISFACTION SELF EMPLOYED SEXUAL ASSAULT SEXUAL OFFENCES SICK LEAVE SOCIAL ACTIVITIES L... SOCIAL HOUSING SOCIAL SUPPORT SPORT SPOUSES STRUCTURAL ELEMENTS... SUPERVISORS Social behaviour an... TAX EVASION TELEPHONES THEFT THEFT PROTECTION TIME TRAFFIC OFFENCES TRANSPORT TRAVEL TRESPASS UNEMPLOYED URBAN AREAS VISITS PERSONAL WAGES WALKING WITNESSES WORKERS WORKPLACE