Abstract

ADI: An index of socioeconomic status for communities. Dataset ingested directly from BigQuery.

Documentation

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.

The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.

Dataset source: https://help.broadstreet.io/article/adi/

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.Much of the ADI research and popularity would not be possible without the excellent work of Dr. Amy Kind and colleagues at HIPxChange and at The University of Wisconsin Madison.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery. Weitere Informationen

Pobal HP Deprivation Index at Electoral Districts (ED) level. This index data is calculated as the ED level aggregates from the 2006 and 2011 Census Small Area (SA) level data, which is available from http://maps.pobal.ie/ . The index is based on the combination of three dimensions of relative affluence and deprivation: Demographic Profile, Social Class Composition and Labour Market Situation. For more information see http://trutzhaase.eu/

We extend our previous work with the Yost Index by adding 90% confidence intervals to the index values. These were calculated using the variance replicate estimates published in association with the American Community Survey of the United States Census Bureau.

In the file yost-tract-2015-2019.csv, the data fields consists of 11-digit geographic ID built from FIPS codes (2 digit state, 3 digit county, 6 digit census tract); Yost index, 90% lower confidence interval; 90% upper confidence interval. Data is provided for 72,793 census tracts for which sufficient data were available. The Yost Index ranges from 1 (lowest socioeconomic position) to 100 (highest socioeconomic position).

For those only interested in using the index as we have calculated it, the file yost-tract-2015-2019 is the only file you need. The other 368 files here are provided for anyone who wishes to replicate our results using the R program yost-conf-intervals.R. The program presumes the user is running Windows machine and that all files reside in a folder called C:/yostindex. The R program requires a number of packages, all of which are specified in lines 10-22 of the program.

Details of this project were published in Boscoe FP, Liu B, LaFantasie J, Niu L, Lee FF. Estimating uncertainty in a socioeconomic index derived from the American Community Survey. SSM-Population Health 2022; 18: 101078. Full text

Additional years of data following this format are planned to be added to this repository in time.

The Townsend Deprivation Index is a measure of material deprivation first introduced by Peter Townsend in 1987. A Townsend score can be calculated using a combination of four census variables for any geographical area (provided census data is available for that area). The measure has been widely used in research for health, education and crime to establish whether relationships exist with deprivation. The Townsend scores below were calculated for the UK based on data from the 2011 Census and include a discussion with geographical visualisations of the findings.

Patient postcode linked measures are available for patients in English practices that have consented to participate in the linkage scheme. Data are linked via Lower Super Output Area (LSOA), Super Output Area (SOA) in Northern Ireland and datazone (DZ) in Scotland. The latest available patient postcode of residence is mapped to an LSOA boundary. The LSOA of residence then allows linkage to the following LSOA-level deprivation measures: 2004 English Index of Multiple Deprivation; 2007 English Index of Multiple Deprivation; 2010 English Index of Multiple Deprivation; 2015 English Index of Multiple Deprivation (composite and individual domains); Townsend Deprivation Index: calculated using unadjusted 2001 census data; Carstairs Index using 2011 census data.

Data are provided as quintiles, deciles or twentiles of the deprivation score to prevent disclosure of patient location. In order to prevent the possibility of deductive disclosure of a patients’ area of residence, researchers will only be provided with one of the above linked datasets for any one study.

The Canadian Index of Multiple Deprivation (CIMD) is an area-based index which used 2016 Census of Population microdata to measure four key dimensions of deprivation at the dissemination area (DA)-level: residential instability, economic dependency, situational vulnerability and ethno-cultural composition. Using factor analysis, DA-level factor scores were calculated for each dimension. Within a dimension, ordered scores were assigned a quintile value, 1 through 5, where 1 represents the least deprived and 5 represents the most deprived. The CIMD allows for an understanding of inequalities in various measures of health and social well-being. While it is a geographically-based index of deprivation and marginalization, it can also be used as a proxy for an individual. The CIMD has the potential to be widely used by researchers on a variety of topics related to socio-economic research. Other uses for the index may include: policy planning and evaluation, or resource allocation.

This dataset contains the main ranking for the Index of Multiple Deprivation, 2010. This dataset puts the 32,482 LSOAs into a rank order based on their 2010 IMD score. A rank of 1 is the most deprived.

The English Indices of Deprivation provide a relative measure of deprivation at small area level across England. Areas are ranked from least deprived to most deprived on seven different dimensions of deprivation and an overall composite measure of multiple deprivation. Most of the data underlying the 2010 indices are for the year 2008.

The indices have been constructed by the Social Disadvantage Research Centre at the University of Oxford for the Department for Communities and Local Government. All figures can only be reproduced if the source (Department for Communities and Local Government, Indices of Deprivation 2010) is fully acknowledged.

The domains used in the Indices of Deprivation 2010 are: income deprivation; employment deprivation; health deprivation and disability; education deprivation; crime deprivation; barriers to housing and services deprivation; and living environment deprivation. Each of these domains has its own scores and ranks, allowing users to focus on specific aspects of deprivation.

Because the indices give a relative measure, they can tell you if one area is more deprived than another but not by how much. For example, if an area has a rank of 40 it is not half as deprived as a place with a rank of 20.

The Index of Multiple Deprivation was constructed by combining scores from the seven domains. When comparing areas, a higher deprivation score indicates a higher proportion of people living there who are classed as deprived. But as for ranks, deprivation scores can only tell you if one area is more deprived than another, but not by how much.

This dataset was created from a spreadsheet provided by the Department of Communities and Local Government, which can be downloaded here.

The method for calculating the IMD score and underlying indicators is detailed in the report 'The English Indices of Deprivation 2010: Technical Report'.

The data is represented here as Linked Data, using the Data Cube ontology.

IntroductionGiven limited evidence of previous studies, we evaluated the role of environmental justice (EJ) burden (i.e., a neighborhood characterized by both increased environmental burden and socioeconomic deprivation) in Black-White disparities in spontaneous preterm birth (sPTB) in Harris County, Texas and compared results that evaluated neighborhood-level socioeconomic deprivation alone.MethodsWe conducted a retrospective analysis using PeriBank, a database and biospecimen repository of gravidae giving birth at two hospitals in the Texas Medical Center. We included 3,703 non-Hispanic Black and 5,475 non-Hispanic white gravidae who were U.S.-born, delivered from August 2011-December 2020, and resided in Harris County, TX. We used data from the U.S. EPA EJScreen to characterize the EJ burden of participant's zip code of residence from fine particulate matter (PM2.5), ozone, and proximity to National Priorities List (NPL) sites and calculated zip-code level Area Deprivation Index (ADI). We assessed the contribution of neighborhood-level variables to the Black-White disparity in sPTB by evaluating attenuation of the odds ratio (OR) representing the effect of race in multivariable logistic regression models, controlling for individual-level characteristics. We also conducted race-stratified analyses between each neighborhood variable and sPTB. Exposure indices were treated as continuous variables; in stratified models, ORs and 95% Confidence Intervals (CIs) are presented per 10-unit increase in the neighborhood variable.ResultsAccounting for individual-level variables, Black gravidae had 79% higher odds of sPTB than white gravidae (OR = 1.79, 95%CI = 1.32, 2.44); the disparity was moderately attenuated when accounting for EJ burden or ADI (ORs ranged from 1.58 to 1.69). Though we observed no association between any of the EJ burden indices and sPTB among white gravidae, we found increased risks among Black gravidae, with ORs of similar magnitude for each EJ variable. For example, Black gravidae experienced 17% increased odds of sPTB associated with a 10-unit increase in the EJ burden index for PM2.5 (OR = 1.17, 95%CI = 0.97, 1.40). No racial differences were observed in the association of ADI with sPTB.DiscussionThough we observed limited evidence of the contribution of living in EJ neighborhoods to the Black-White disparity in sPTB, our study suggests living in an EJ neighborhood may differentially impact Black and white gravidae.

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.Much of the ADI research and popularity would not be possible without the excellent work of Dr. Amy Kind and colleagues at HIPxChange and at The University of Wisconsin Madison.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery. En savoir plus

The deprivation index was designed in the late 1990s in order to measure the deprivation of Quebecers on a small geographic scale. It is used for the purposes of researching and monitoring trends on social inequalities in health, developing policies and programs, allocating resources, and evaluating services. It is composed of a material dimension and a social dimension that can be analyzed separately or in combination. The index includes six indicators, all from the 2021 census and calculated on the basis of dissemination areas (DAs). The geographic file includes the national (province of Quebec), regional (health regions (RSS)), and local (territorial service networks (RTS), local service networks (RLS) and local community service centers (CLSC)) versions of the deprivation index. In cases where a broadcast area (AD) straddles two territories (in the RTS, RLS and CLSC versions of the file), it is the AD with the largest proportion of the population that determines which RTS, RLS or CLSC is selected in order to have a single deprivation index value per AD for mapping. All results by AD are available in the equivalence table on the Web site of the National Institute of Public Health of Quebec (INSPQ). For more information on the deprivation index, you can consult The Material and Social Deprivation Index: in brief.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Score for each LSOA in the Living Environment Deprivation domain.

The English Indices of Deprivation provide a relative measure of deprivation at small area level across England. Areas are ranked from least deprived to most deprived on seven different dimensions of deprivation and an overall composite measure of multiple deprivation. Most of the data underlying the 2010 indices are for the year 2008.

The indices have been constructed by the Social Disadvantage Research Centre at the University of Oxford for the Department for Communities and Local Government. All figures can only be reproduced if the source (Department for Communities and Local Government, Indices of Deprivation 2010) is fully acknowledged.

The domains used in the Indices of Deprivation 2010 are: income deprivation; employment deprivation; health deprivation and disability; education deprivation; crime deprivation; barriers to housing and services deprivation; and living environment deprivation. Each of these domains has its own scores and ranks, allowing users to focus on specific aspects of deprivation.

Because the indices give a relative measure, they can tell you if one area is more deprived than another but not by how much. For example, if an area has a rank of 40 it is not half as deprived as a place with a rank of 20.

The Index of Multiple Deprivation was constructed by combining scores from the seven domains. When comparing areas, a higher deprivation score indicates a higher proportion of people living there who are classed as deprived. But as for ranks, deprivation scores can only tell you if one area is more deprived than another, but not by how much.

This dataset was created from a spreadsheet provided by the Department of Communities and Local Government, which can be downloaded here.

The method for calculating the IMD score and underlying indicators is detailed in the report 'The English Indices of Deprivation 2010: Technical Report'.

The data is represented here as Linked Data, using the Data Cube ontology.

The English Indices of Deprivation 2019 use 39 separate indicators, organised across seven distinct domains of deprivation which can be combined, using appropriate weights, to calculate the Index of Multiple Deprivation 2019 (IMD 2019). This is an overall measure of multiple deprivation experienced by people living in an area and is calculated for every Lower layer Super Output Area (LSOA) in England. The IMD 2019 can be used to rank every LSOA in England according to their relative level of deprivation.

Column

Full Column

LSOA01CD

LSOA code (2011)

LSOA01NM

LSOA name (2011)

LADcd

Local Authority District code (2019)

LADnm

Local Authority District name (2019)

IMDScore

Index of Multiple Deprivation (IMD) Score

IMDRank0

Index of Multiple Deprivation (IMD) Rank (where 1 is most deprived)

IMDDec0

Index of Multiple Deprivation (IMD) Decile (where 1 is most deprived 10% of LSOAs)

IncScore

Income Score (rate)

IncRank

Income Rank (where 1 is most deprived)

IncDec

Income Decile (where 1 is most deprived 10% of LSOAs)

EmpScore

Employment Score (rate)

EmpRank

Employment Rank (where 1 is most deprived)

EmpDec

Employment Decile (where 1 is most deprived 10% of LSOAs)

EduScore

Education, Skills and Training Score

EduRank

Education, Skills and Training Rank (where 1 is most deprived)

EduDec

Education, Skills and Training Decile (where 1 is most deprived 10% of LSOAs)

HDDScore

Health Deprivation and Disability Score

HDDRank

Health Deprivation and Disability Rank (where 1 is most deprived)

HDDDec

Health Deprivation and Disability Decile (where 1 is most deprived 10% of LSOAs)

CriScore

Crime Score

CriRank

Crime Rank (where 1 is most deprived)

CriDec

Crime Decile (where 1 is most deprived 10% of LSOAs)

BHSScore

Barriers to Housing and Services Score

BHSRank

Barriers to Housing and Services Rank (where 1 is most deprived)

BHSDec

Barriers to Housing and Services Decile (where 1 is most deprived 10% of LSOAs)

EnvScore

Living Environment Score

EnvRank

Living Environment Rank (where 1 is most deprived)

EnvDec

Living Environment Decile (where 1 is most deprived 10% of LSOAs)

IDCScore

Income Deprivation Affecting Children Index (IDACI) Score (rate)

IDCRank

Income Deprivation Affecting Children Index (IDACI) Rank (where 1 is most deprived)

IDCDec

Income Deprivation Affecting Children Index (IDACI) Decile (where 1 is most deprived 10% of LSOAs)

IDOScore

Income Deprivation Affecting Older People (IDAOPI) Score (rate)

IDORank

Income Deprivation Affecting Older People (IDAOPI) Rank (where 1 is most deprived)

IDODec

Income Deprivation Affecting Older People (IDAOPI) Decile (where 1 is most deprived 10% of LSOAs)

CYPScore

Children and Young People Sub-domain Score

CYPRank

Children and Young People Sub-domain Rank (where 1 is most deprived)

CYPDec

Children and Young People Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

ASScore

Adult Skills Sub-domain Score

ASRank

Adult Skills Sub-domain Rank (where 1 is most deprived)

ASDec

Adult Skills Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

GBScore

Geographical Barriers Sub-domain Score

GBRank

Geographical Barriers Sub-domain Rank (where 1 is most deprived)

GBDec

Geographical Barriers Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

WBScore

Wider Barriers Sub-domain Score

WBRank

Wider Barriers Sub-domain Rank (where 1 is most deprived)

WBDec

Wider Barriers Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

IndScore

Indoors Sub-domain Score

IndRank

Indoors Sub-domain Rank (where 1 is most deprived)

IndDec

Indoors Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

OutScore

Outdoors Sub-domain Score

OutRank

Outdoors Sub-domain Rank (where 1 is most deprived)

OutDec

Outdoors Sub-domain Decile (where 1 is most deprived 10% of LSOAs)

TotPop

Total population: mid 2015 (excluding prisoners)

DepChi

Dependent Children aged 0-15: mid 2015 (excluding prisoners)

Pop16_59

Population aged 16-59: mid 2015 (excluding prisoners)

Pop60+

Older population aged 60 and over: mid 2015 (excluding prisoners)

WorkPop

Working age population 18-59/64: for use with Employment Deprivation Domain (excluding prisoners)

The Canadian Index of Multiple Deprivation (CIMD) is an area-based index which used 2021 Census of Population microdata to measure four key dimensions of deprivation at the dissemination area (DA)-level: residential instability, economic dependency, situational vulnerability and ethno-cultural composition. Using factor analysis, DA-level factor scores were calculated for each dimension. Within a dimension, ordered scores were assigned a quintile value, 1 through 5, where 1 represents the least deprived and 5 represents the most deprived. The CIMD allows for an understanding of inequalities in various measures of health and social well-being. While it is a geographically-based index of deprivation and marginalization, it can also be used as a proxy for an individual. The CIMD has the potential to be widely used by researchers on a variety of topics related to socio-economic research. Other uses for the index may include: policy planning and evaluation, or resource allocation. *** Correction October 22, 2024 *** A correction has been made to the variables in the following downloadable 2021 CIMD index datasets : Canada, Atlantic, Quebec, Ontario, Prairies, and British-Columbia. This correction impacts all the data in these datasets.

Topicality: 2018 and 2023Projection: New Zealand Transverse Mercator (NZTM)This dataset provides a curated selection of key variables from the 2018 and 2023 Censuses, focusing on the most critical measures for individuals, dwellings, households, and families. It is designed to provide essential insights for high-level analysis and decision-making.Data is available at the Statistical Area 1 (SA1) level, with additional higher geographies for 2023, including:Statistical Area 2 (SA2)Statistical Area 3 (SA3)Urban/Rural areasTerritorial AuthoritiesRegional CouncilsThe data is sourced from the 2023 Census data published by Statistics New Zealand (StatsNZ) and Index of Multiple Deprivation by the Ministry of Health/University of Otago.Lookup Tables:Download lookup file for 'Individual part 1' topic from Stats NZ ArcGIS Online or Stats NZ geographic data service.Download lookup file for 'Individual part 2' topic from Stats NZ ArcGIS Online or Stats NZ geographic data service.Download lookup file for 'Dwellings' topic from Stats NZ ArcGIS Online or Stats NZ geographic data service. Download lookup file for 'Households' topic from Stats NZ ArcGIS Online or Stats NZ geographic data service.Download lookup file for 'Family' topic from Stats NZ ArcGIS Online or Stats NZ geographic data service. This layer is offered by Eagle Technology (Official Esri Distributor). Eagle Technology offers services that can be used in the ArcGIS platform. The Content team at Eagle Technology updates the layers on a regular basis and regularly adds new content to the Living Atlas. By using this content and combining it with other data you can create new information products quickly and easily.If you have any questions or comments about the content, please let us now at livingatlas@eagle.co.nz.NotesDeprivation IndexThe NZDep Index measures relative socioeconomic deprivation for geographic areas, not individuals. The specific indicators and boundaries used to calculate NZDep can change between Censuses. As a result, some deprivation index values may appear as null.Data Values-997 and -999 values from the Stats NZ dataset, previously used to represent unavailable or confidential data, have been replaced with 0 to improve compatibility with Smart Mapping and Policy Mapping workflows. Users should note that while this change supports data processing, 0 may represent either valid data or a placeholder for unavailable or confidential values, depending on the context.

The Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., 2000-2016, v1.0 data set contains daily and annual concentration predictions for Fine Particulate Matter (PM2.5), Ozone (O3), and Nitrogen Dioxide (NO2) pollutants at ZIP Code-level for the years 2000 to 2016. Ensemble predictions of three machine-learning models were implemented (Random Forest, Gradient Boosting, and Neural Network) to estimate the daily PM2.5, O3, and NO2 at the centroids of 1km x 1km grid cells across the contiguous U.S. for 2000 to 2016. The predictors included air monitoring data, satellite aerosol optical depth, meteorological conditions, chemical transport model simulations, and land-use variables. The ensemble models demonstrated excellent predictive performance with 10-fold cross-validated R-squared values of 0.86 for PM2.5, 0.86 for O3, and 0.79 for NO2. These high-resolution, well-validated predictions allow for estimates of ZIP Code-level pollution concentrations with a high degree of accuracy. For general ZIP Codes with polygon representations, pollution levels were estimated by averaging the predictions of grid cells whose centroids lie inside the polygon of that ZIP Code; for other ZIP Codes such as Post Offices or large volume single customers, they were treated as a single point and predicted their pollution levels by assigning the predictions using the nearest grid cell. The polygon shapes and points with latitudes and longitudes for ZIP Codes were obtained from Esri and the U.S. ZIP Code Database and were updated annually. The data include about 31,000 general ZIP Codes with polygon representations, and about 10,000 ZIP Codes as single points. The aggregated ZIP Code-level, daily predictions are applicable in research such as environmental epidemiology, environmental justice, health equity, and political science, by linking with ZIP Code-level demographic and medical data sets, including national inpatient care records, medical claims data, census data, U.S. Census Bureau American CommUnity Survey (ACS), and Area Deprivation Index (ADI). The data are particularly useful for studies on rural populations who are under-represented due to the lack of air monitoring sites in rural areas. Compared with the 1km grid data, the ZIP Code-level predictions are much smaller in size and are manageable in personal computing environments. This greatly improves the inclusion of scientists in different fields by lowering the key barrier to participation in air pollution research. The Units are ug/m^3 for PM2.5 and ppb for O3 and NO2.

The Ministry of Education (MEQ) annually calculates two deprivation indices for the 69 school service centers and linguistic school boards: • the Socio-economic Environment Index (IMSE), which consists of the proportion of families with children whose mothers do not have a diploma, certificate or degree (which represents two thirds of the weight of the index) and the proportion of households whose parents were not employed during the week of reference of the Canadian census (which represents a third of the weight of the index). • The Low Income Threshold Index (LFS) corresponds to the proportion of families with children whose income is close to or below the low income threshold. The low-income cut-off is defined as the income level at which families are estimated to spend 20% more than the overall average on food, housing, and clothing. It provides information that is used to estimate the proportion of families whose incomes can be considered low, taking into account the size of the family and the environment of residence (rural region, small urban area, large agglomeration, etc.). For the 2023-2024 school year, the socio-economic data used are extracted from the 2016 Canadian census and relate to the situation of Quebec families with at least one child aged 0 to 18. Depending on their geographical position, these families are grouped together in one of the 3,680 settlement units established by the Ministry. The annual school indices are grouped in decimal rank in order to locate the relative position of the school among all public schools, for primary and secondary education. Note that schools may include more than one school building, that no index is calculated for school boards with special status (Cree, Kativik Ilisarniliriniq and Littoral) and that only schools with 30 students or more are selected (without an MEQ-MSSS agreement). For the school year 2023-2024, 695 primary schools and 197 secondary schools are considered disadvantaged (decile ranks 8, 9 or 10) according to the IMSE index. These schools have 15,7109 and 113,781 students respectively, representing 30% of the public network for each of these two levels of education.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset contains the main ranking for the Index of Multiple Deprivation, 2010. This dataset puts the 32,482 LSOAs into a rank order based on their 2010 IMD score. A rank of 1 is the most deprived.

The English Indices of Deprivation provide a relative measure of deprivation at small area level across England. Areas are ranked from least deprived to most deprived on seven different dimensions of deprivation and an overall composite measure of multiple deprivation. Most of the data underlying the 2010 indices are for the year 2008.

The indices have been constructed by the Social Disadvantage Research Centre at the University of Oxford for the Department for Communities and Local Government. All figures can only be reproduced if the source (Department for Communities and Local Government, Indices of Deprivation 2010) is fully acknowledged.

The domains used in the Indices of Deprivation 2010 are: income deprivation; employment deprivation; health deprivation and disability; education deprivation; crime deprivation; barriers to housing and services deprivation; and living environment deprivation. Each of these domains has its own scores and ranks, allowing users to focus on specific aspects of deprivation.

Because the indices give a relative measure, they can tell you if one area is more deprived than another but not by how much. For example, if an area has a rank of 40 it is not half as deprived as a place with a rank of 20.

The Index of Multiple Deprivation was constructed by combining scores from the seven domains. When comparing areas, a higher deprivation score indicates a higher proportion of people living there who are classed as deprived. But as for ranks, deprivation scores can only tell you if one area is more deprived than another, but not by how much.

This dataset was created from a spreadsheet provided by the Department of Communities and Local Government, which can be downloaded here.

The method for calculating the IMD score and underlying indicators is detailed in the report 'The English Indices of Deprivation 2010: Technical Report'.

The data is represented here as Linked Data, using the Data Cube ontology.

Created with a 500 meter side hexagon grid, we undertook a regression analysis creating a correlation matrix utilising a number of demographic indicators from the Local Insight OCSI platform. This dataset is showing the distribution of the metrics that were found to have the strongest relationships, with the base comparison metric of Indices of Deprivation 2019 income deprivation affecting older people. This dataset contains the following metrics: IoD 2019 Income Deprivation Affecting Older People (IDAOPI) Score (rate) - The Indices of Deprivation (IoD) 2019 Income Deprivation Affecting Older People Index captures deprivation affecting older people defined as those adults aged 60 or over receiving Income Support or income-based Jobseekers Allowance or income-based Employment and Support Allowance or Pension Credit (Guarantee) or Universal Credit (in the 'Searching for work', 'No work requirements', 'Planning for work', 'Working with requirements' and 'Preparing for work' conditionality groups) or families not in receipt of these benefits but in receipt of Working Tax Credit or Child Tax Credit with an equivalised income (excluding housing benefit) below 60 per cent of the national median before housing costs. Asylum seekers aged 60 and over are not included in the Income Deprivation Affecting Older People Index. Rate calculated as = (ID 2019 Income Deprivation Affecting Older People Index (IDAOPI) numerator)/(ID 2019 Older population aged 60 and over: mid 2015 (excluding prisoners))*100.Pension Credit claimants who are single - Shows the proportion of people receiving Pension Credit who are single (as a % of all of pensionable age). Pension Credit provides financial help for people aged 60 or over whose income is below a certain level set by the law. Rate calculated as = (Pension Credit claimants, single)/(Population aged 65+)*100.Pension Credit claimants, Guarantee Element - Shows the proportion of people of retirement age receiving Pension Credit Guarantee Element. Pension Credit provides financial help for people aged 60 or over whose income is below a certain level set by the law. The Guarantee Element is payable to tops up incomes that are below a minimum threshold. Rate calculated as = (Pension Credit claimants, Guarantee Element)/(Population aged 65+)*100.Working-age DWP benefit claimants aged 50 and over - Shows the proportion of people aged 50-64 receiving DWP benefits. DWP Benefits are benefits payable to all people who need additional financial support due to low income, worklessness, poor health, caring responsibilities, bereavement or disability. The following benefits are included: Bereavement Benefit, Carers Allowance, Disability Living Allowance, Incapacity Benefit/Severe Disablement Allowance, Income Support, Jobseekers Allowance, Pension Credit and Widows Benefit. Figure are derived from 100% sample of administrative records from the Work and Pensions Longitudinal Study (WPLS), with all clients receiving more than one benefit counted only by their primary reason for interacting with the benefits system (to avoid double counting). Universal Credit (UC) and Personal Independence Payment (PIP) started to replace the benefits included in this measure from April 2013 when new Jobseeker's Allowance and Disability Living Allowance claimants started to move onto the new benefits in selected geographical areas. This rollout intensified from March 2016 onwards to capture all of the other Working age DWP Benefits. As UC and PIP are not included in this measure it no longer represent a complete count of working age people receiving DWP Benefits. As a result the measure was discontinued in November 2016. Rate calculated as = (Working-age DWP benefit claimants aged 50 and over) /(Population aged 50+)*100.People with numeracy skills at entry level 1 or below (2011) (%) - Shows the proportion of people with numeracy skills at entry level 1 or below. The Skills for Life Survey 2011 was commissioned by the Department for Business Innovation and Skills. The survey aimed to produce a national profile of adult literacy, numeracy and Information and Communication Technology (ICT) skills, and to assess the impact different skills had on people's lives. Each figure is a mean estimate of the number of adults with each skill level (or who do / do not speak English as a first language). The survey was conducted at regional level as a part interview part questionnaire. The interview comprised a background questionnaire followed by a pre-assigned random combination of two of the three skills assessments: literacy, numeracy and ICT. The background questionnaire was designed to collect a broad set of relevant demographic and behavioural data. This demographic data was used to model the information down to neighbourhood level using the neighbourhood characteristics of each MSOA to create a likely average skill level of the population within each MSOA. survey. Respondents who completed the questions allocated to the literacy and numeracy assessments were assigned to one of the five lowest levels of the National Qualifications Framework: Entry Level 1 or below; Entry Level 2; Entry Level 3; Level 1; or Level 2 or above. Each figure is a mean estimate of the number of adults with each skill level (or who do / do not speak English as a first language).IoD 2015 Housing affordability indicator -Social Grade (N-SEC): 8. Never worked and long-term unemployed - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 8. Never worked and long-term unemployed. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Never worked and long-term unemployed (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Female healthy life expectancy at birth - Female healthy life expectancy at birth. Healthy life expectancy (HLE) is the average number of years that an individual might expect to live in "good" health in their lifetime. The 'good' health state used for estimation of HLE was based on self-reports of general health at the 2011 Census; specifically those reporting their general health as 'very good' or 'good' were defined as in 'Good' health in this context. The HLE estimates are a snapshot of the health status of the population, based on self-reported health status and mortality rates for each area in that period. They are not a guide to how long someone will actually expect to live in "good" health, both because mortality rates and levels of health status are likely to change in the future, and because many of those born in an area will live elsewhere for at least part of their lives.Sport England Market Segmentation: Pub League Team Mates - Shows the proportion of people living in the area that are classified as Pub League Team Mates in the Sports Market Segmentation tool developed by Sport England. The Pub League Team Mates classification group are predominantly aged 36-45 are a mix of married/single child and childless and likely to be engaged in a vocational job. For more details about the characteristics of this group see http://segments.sportengland.org/pdf/penPortrait-9.pdf. Sports Market Segmentation is a web-based tool developed by Sport England to help all those delivering sport to better understand their local markets and target them more effectively.IoD 2010 Income Domain, score - The Indices of Deprivation (IoD) 2010 Income Deprivation Domain measures the proportion of the population in an area experiencing deprivation relating to low income. The definition of low income used includes both those people that are out-of-work, and those that are in work but who have low earnings (and who satisfy the respective means tests). The domain forms part of the overall Index of Multiple Deprivation (IMD) 2010. The IMD 2010 is the most comprehensive measure of multiple deprivation available. Drawn primarily from 2008 data and presented at small area level, the IMD 2010 is a unique and invaluable tool for measuring deprivation nationally and across local areas. The concept of multiple deprivation upon which the IMD 2010 is based is that separate types of deprivation exist, which are separately recognised and measurable.People over the age of 65 with bad or very bad health - Shows the proportion of people over the age of 65 that reported to have bad or very bad health. Figures are self-reported and taken from the 2011 Census. Rate calculated as = (Bad or very bad health (census LC3206)/(Population aged 65+)*100