These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for deprivation. This is because adjusting for deprivation might create the impression that a higher death rate for those who are more deprived is acceptable. Patient records are assigned to 1 of 5 deprivation groups (called quintiles) using the Index of Multiple Deprivation (IMD). The deprivation quintile cannot be calculated for some records e.g. because the patient's postcode is unknown or they are not resident in England. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI belonging to each deprivation quintile are produced to support the interpretation of the SHMI. Notes: 1. On 1st January 2025, North Middlesex University Hospital NHS Trust (trust code RAP) was acquired by Royal Free London NHS Foundation Trust (trust code RAL). This new organisation structure is reflected from this publication onwards. 2. There is a shortfall in the number of records for Northumbria Healthcare NHS Foundation Trust (trust code RTF), The Rotherham NHS Foundation Trust (trust code RFR), The Shrewsbury and Telford Hospital NHS Trust (trust code RXW), and Wirral University Teaching Hospital NHS Foundation Trust (trust code RBL). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 3. A number of trusts are now submitting Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS) rather than the Admitted Patient Care (APC) dataset. The SHMI is calculated using APC data. Removal of SDEC activity from the APC data may impact a trust’s SHMI value and may increase it. More information about this is available in the Background Quality Report. 4. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of this page.

This release provides insights into self-reported health in England and Wales in 2021, broken down by age and sex. Key findings are presented at country, regional and local authority level. Additional analyses compare general health to the 2011 Census and examines the relationship between deprivation and health at a national decile (England) or quintile (Wales) level can be found here.

In 2021 and 2011, people were asked “How is your health in general?”. The response options were:

• Very good
• Good
• Fair
• Bad
• Very bad

Age specific percentage

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

Age-standardised percentage

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

Details on usage of Age-standardised percentage can be found here

Count

The count is the number of usual residents by general health status from very good to very bad, sex, age group and geographic breakdown. To ensure that individuals cannot be identified in the data, counts and populations have been rounded to the nearest 5, and counts under 10 have not been included..

General health

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

Index of Multiple Deprivation and Welsh Index of Multiple Deprivation

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

Population

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

Usual resident

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

Multiple regression model of relationship between number of hospital readmissions due to diabetes, IMD quintile and ethnicity.

Mean obesity prevalence (standard deviation) in the pre- and post-announcement periods of the UK SDIL, by school class, sex, and IMD quintiles.

Multiple regression model of relationship between hospital admission due to diabetes, IMD quintile and ethnicity.

This dataset provides detailed information on the 2019 Index of Multiple Deprivation (IMD) for Birmingham, UK. The data is available at the postcode level and includes the Lower Layer Super Output Area (LSOA) information.Data is provided at the LSOA 2011 Census geography.The decile score ranges from 1-10 with decile 1 representing the most deprived 10% of areas while decile 10 representing the least deprived 10% of areas.The IMD rank and decile score is allocated to the LSOA and all postcodes within it at the time of creation (2019).Note that some postcodes cross over LSOA boundaries. The Office for National Statistics sets boundaries for LSOAs and allocates every postcode to one LSOA only: this is the one which contains the majority of residents in that postcode area (as at 2011 Census).

The English Indices of Deprivation 2019 provide a comprehensive measure of relative deprivation in small areas across England. The Income Deprivation dataset is a key component of this index, capturing the proportion of the population experiencing deprivation due to low income. This dataset includes indicators such as the number of people receiving income support, jobseeker's allowance, and other income-related benefits. It is used to identify areas with high levels of income deprivation, informing policy decisions and resource allocation to address socio-economic inequalities.

The Smart Energy Research Lab (SERL) Observatory facilitates a broad range of energy demand research and is a unique data resource for research where access to high resolution, large scale energy data linked to relevant contextual data is required. Further information about SERL can be found on the Smart Energy Research Lab website.

This dataset of aggregated statistics is available under standard Safeguarded (End User Licence) access conditions. It contains over 2.5 million rows of data and describes domestic gas and electricity energy use in Great Britain 2020-2023 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. This aggregated dataset can be used, for example, to show how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day); and can be broken down by occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

Secure Access data
A more detailed set of SERL data, including smart meter data and additional contextual data, is available under restricted Secure access conditions under SN 8666: Smart Energy Research Lab Observatory Data: Secure Access. It is a longitudinal dataset containing records from August 2019, with updates provided to researchers on a (roughly) quarterly basis. Users should download this safeguarded access statistical study first to see whether it is suitable for their needs before considering an application for the Secure dataset.

The second edition (May 2024) includes summaries of daily average energy use in a data file for 2020-2023, and summaries of half-hourly average energy use in four data files for 2020-2023, as well as an accompanying technical document.

Descriptive statistics and model estimates for the change in monthly employee pay and employee status attributable to having had an endometriosis diagnosis in an NHS hospital between April 2016 and December 2022, compared with the two-year period before diagnosis. Includes breakdowns by age group, ethnic group, Index of Multiple Deprivation (IMD) quintile group and region.

This is a set of aggregated data tables that underly the key figures in the SERL stats report "Smart Energy Research Lab: Energy use in GB domestic buildings 2021" (Volume 1). The report describes domestic gas and electricity energy use in Great Britain in 2021 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB population in terms of region and Index of Multiple Deprivation (IMD) quintile. The report shows how residential energy use in GB varies over time (monthly over the year and half-hourly over the course of the day), with occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), by type of heating system, presence of solar panels and of electric vehicles, and by weather, region and IMD quintile.

Short stature clusters in England, 2006–2007 to 2018–2019, adjusted for ethnicity (n = 5,765,707).

In 2019, people from most ethnic minority groups were more likely than White British people to live in the most deprived neighbourhoods.

Abstract copyright UK Data Service and data collection copyright owner. The Smart Energy Research Lab (SERL) delivers a unique energy data resource to the UK research community that enables a broad range of multi-disciplinary, socio-technical research relating to energy consumption in domestic buildings. The SERL Observatory is transforming Great Britain's energy research through the long-term provision of high quality, high-resolution energy data that provides a reliable evidence base for intervention, observational and longitudinal studies across the socio-technical spectrum. The goals of the Smart Energy Research Lab are to provide: A trusted data resource for researchers to utilise large-scale, high-resolution energy data An effective mechanism for collecting and linking energy data with other contextual dataHigh quality data management to ensure fit-for-purpose data are provisioned to researchers Participant recruitment began in August 2019. Approximately 1,700 participants were recruited from central and southern England and from Wales as part of a pilot study that tested different recruitment strategies. The second recruitment wave took place in August-September 2020, and the third wave at the start of 2021. SERL recruited over 13,000 households which are regionally representative across England, Scotland and Wales. Recruitment is also designed to be representative of each Index of Multiple Deprivation (IMD) quintile; an area-based relative measure of deprivation. For the latest edition (released in May 2024), all SERL data up to and including 31st December 2023 were made available. Users should note that this is the 6th edition of SERL data that has been released, though the citation may refer to the 7th edition. All code provided with the data is now managed on the SERL GitHub website. Smart meter data: Daily and half-hourly energy (electricity and gas) consumption dataTariff dataAdditional smart meter technical data Contextual data: SERL survey (initial) completed by participant households on sign-up providing data on household information and building characteristics.Energy Performance Certificate (EPC) dataWeather dataSERL Covid-19 survey: sent to wave 1 participants in May 2020 to understand their circumstances during the first lockdownSERL survey (Follow-up 2023): sent to all active participants in early 2023 to investigate the impact of rapidly rising energy costs in 2022/2023 SERL data will be updated and made available to researchers on a quarterly basis. SERL is an evolving data resource and thus new editions of the data might include: additional records – more smart meter data, since the previous editionadditional participants – more participants recruited since the previous releaseadditional variables – where new variables become available to SERL Further information about SERL can be found on serl.ac.uk and in the associated documentation. The 'Key Documents' section of the SERL website, which links to all publications that use SERL data, can be found at serl.ac.uk/key-documents. If you do not see your SERL-data publication listed, please contact the SERL team via info@serl.ac.uk. For the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 data users should note that neither the European Commission nor the European Centre for Medium-Range Weather Forecasts will be held responsible for any use that may be made of the Copernicus information or data it contains. The Energy Performance of Buildings Data is also included and users must read and abide by the Copyright Information Notice, provided by the Department for Levelling Up, Housing and Communities, that covers the use of Royal Mail information and non-address data provided under the Open Government Licence v3.0.For the latest edition (released in November 2024), all SERL smart meter and climate data have been updated to June 2024. Users should note that this is the 7th edition of SERL data that has been released, though the citation may refer to the 8th edition. Main Topics: The SERL Observatory panel provides data primarily relating to energy demand and consumption in domestic buildings in Great Britain.

GENERAL INFORMATIONThe dataset represents supporting data for the research findings of the paper accepted for AIED'21 conference: http://oro.open.ac.uk/76042/ SHARING/ACCESS INFORMATIONLinks to publications that cite or use the data: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.Was data derived from another source? Yes - the data was derived from the internal OU data Recommended citation for this dataset: Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.DATA & FILE OVERVIEWThe dataset contains coefficients of a logistic and linear regression that was used to model 3 student outcomes in 3 STEM courses - 1) completion, 2) passing and 3) overall score. The results are split into four tabs1. Regression BetasBets coefficients and the Standard Error for each variable student outcome , i.e. - completion: comp_B comp_SE - passing: pass_B pass_SE - overall score: score_B score_SE 2. LogReg Marginal Effectsthe marginal effect coefficients for the two dichotomous outcomes from the previous tab (completion and passing) More information about the marginal effects: https://www.statisticshowto.com/marginal-effects/3. Reg_BAME - These are the regression coefficients reported in the in the first tab, for the same outcomes (i.e. completion/passing/overall score), but disaggregated by whether the student is identified as BAME or not. Note that the analysis does not contain the 'BAME' coefficients, because it would be constant4. Red_IMDSimilarly as for BAME (point 3), these are regression coefficients disaggregated by IMD quintiles. IMD_Missing is a special category capturing the students without any IMD, i.e. international students.Regression coefficient variablesThe variables entering the regressions can be split into three categories and the intercept(1) Student level - age - banded into age_60, age_MISSING (reference category: age_[21-24]) - gender - gender_F (reference category Gender_M) - an indicator of linked qualification - linked_qual (reference category: linked_qual =False) - declared disability - disability (reference category: disability=False) - caring responsibility carer_NO, carer_YES (reference category: carer_MISSING) - flag whether the student is new at the OU - is_new (reference category: is_new=False) - highest previous education - ed_NoFormal, ed_HE_Qual, ed_PostGrad (reference category: ed_A Level/Equivalent) - average previous score - discretised into prev_score_LOW, prev_score_MOD, prev_score_VERY_HIGH (avg.prev.score=MISSING, i.e. the student did not study any previous course) these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have values of the average score. - number of other credits studied - banded as credits_other_[1-60], credits_other_>=61 (reference category: credits_other=0) - number of previous attempts of the course - prev_attempt_=1, prev_attempt >1 (reference category: prev_attempt_0) - IMD (Index of Multiple Deprivation) - banded into quintiles, i.e. imd_=81 imd_MISSING (reference category: imd_[41-60]) - whether the student is identified as BAME - BAME_YES (reference category: BAME_NO) - Membership in the intervention group - group_INT (reference category: group_INT=0) (2) Teacher level - no. of students the teacher is responsible for - stud_in_group - avg. student pass rate in the previous years they were teaching - tut_pr_pass_LOW, tut_pr_pass_HIGH, tut_pr_pass_VERY_HIGH, tut_pr_pass_MISSING (reference category: tut_pr_pass_MOD) - these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have different pass rates (3) Course level - dummy variable encoded as - course_1, course_2 (reference category: course_3)(4) intercept

Absolute and relative changes in prevalence of obesity (95% CIs), compared to the counterfactual1, in reception and year 6 boys and girls, by IMD at 19 months post-implementation of the UK SDIL.

JPEG file: supplementary graph derived from the same large dataset as the analysis reported in the cited journal article. TXT file: data for this graph, and reference for the journal article. This graph relates to a journal article that can be viewed at: http://dx.doi.org/10.1136/jech-2018-21045 (see Related Items). We report a nearly five-fold disparity in risk of Unexplained Death in Infancy (UDI) across ethnic groups in England and Wales, and demonstrate that this disparity is not explained by deprivation. Formal adjustment for deprivation (IMD quintiles) does not even slightly reduce the ethnic variation (see Table 2 of the cited paper). A simple scatter plot of ethnic groups illustrates the lack of a relationship between deprivation and risk, with a virtually horizontal overall trend line (as shown in this Dataset). For example, Black Caribbean babies have nearly triple the UDI risk of Black African babies, but similar levels of deprivation. The Indian, Pakistani and Bangladeshi ethnic groups each have around half the risk of White British babies; the White British and Indian groups have similar (relatively low) levels of deprivation, and the Pakistani and Bangladeshi groups are the most deprived in England and Wales. In the cited paper we discuss various potential mediators of the ethnic differences, including sleep practices, breastfeeding and tobacco use, based on the ethnic-specific prevalence of these factors in prior survey data. We suggest that careful comparison of ethnic patterns of exposure and outcome might lead to a better understanding of the aetiology of these very distressing deaths.

See Text S1, Table K for weighted averages of risk factor levels for each deprivation quintile, 2000 and 2007.aEngland average weighted by 2007 population distribution in 10-y age bands.IMD, index of multiple deprivation.

This dataset comprises sensor data collected from Smart Citizen Kits (SCK) sensors, along with survey data conducted by the Bureau of Investigative Journalism (TBIJ). The unique ID is utilized as a linking mechanism between the SCK sensor data and the TBIJ survey data.

The SCK (Smart Citizen Kits) sensors captured a range of environmental parameters in homes, including air temperature, relative humidity, air quality, noise condition and light condition. SCK was calibrated in the lab-based environment by the sensor manufacturer first and further corrected based on its operational mode. Further validation procedures were implemented to ensure the accuracy and quality of the air temperature data and relative humidity data by comparing records between survey sensors and commercial sensors (HOBO MX1101 Wireless Temperature and Humidity Data Logger - Optional Remote Monitoring).

Regarding the TBIJ survey data, only housing tenure, housing types, and self-reported housing conditions from the survey data are included in this dataset.

Index of Multiple Deprivation (IMD) quintiles and existing Energy Performance Certificate (EPC) data are also included in this dataset, based on the participants’ home addresses and Unique ID, providing deprivation level of neighbourhoods and energy conditions of homes. https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019">IMD data was obtained from UK government and the IMD quintile was calculated based on the IMD decile. The EPC data was collected from https://epc.opendatacommunities.org/login">Energy Performance of Buildings Search Results

There is a further data collection which contains additional details on survey households’ building conditions, including building insulations and building age, linked and processed from the open EPC dataset. That is available under https://data.ubdc.ac.uk/dataset/sensor-safeguarded#repeated_field-1">licence

If you want to link the indoor sensor measurement with the outdoor climate conditions, the nearest local weather station is https://wow.metoffice.gov.uk/observations/details/2024022266xn7wqtore67kyhyytrteyxba">LIMBO from the UK Met Office

Some initial analysis from this dataset can be found in our https://github.com/congying-hu/SensorEnhancedSurveyHeatInvestigation?tab=readme-ov-file">GitHub repository

Comparison of patient characteristics by IMD quintile.