According to a survey conducted in the United Kingdom (UK) in 2021, ** percent of people thought it is important that the government addresses health differences due to income, while a further ** percent thought it is important to address health differences due to geographical areas.

The COVID-19 Health Inequalities Monitoring in England (CHIME) tool brings together data relating to the direct impacts of coronavirus (COVID-19) on factors such as mortality rates, hospital admissions, confirmed cases and vaccinations.

By presenting inequality breakdowns - including by age, sex, ethnic group, level of deprivation and region - the tool provides a single point of access to:

• show how inequalities have changed during the course of the pandemic and what the current cumulative picture is
• bring together data in one tool to enable users to access and use the intelligence more easily
• provide indicators with a consistent methodology across different data sets to facilitate understanding
• support users to identify and address inequalities within their areas, and identify priority areas for recovery

In the March 2023 update, data has been updated for deaths, hospital admissions and vaccinations. Data on inequalities in vaccination uptake within upper tier local authorities has been added to the tool for the first time. This replaces data for lower tier local authorities, published in December 2022, allowing the reporting of a wider range of inequality breakdowns within these areas.

Please send any questions or comments to PHA-OHID@dhsc.gov.uk

This slide deck includes a short briefing which summarises key findings from the full analysis ‘Health inequality:’ Closing the life expectancy gap over time?’ for both Camden and Islington.

The Mayors Health Inequalities Strategy sets out his plans to tackle unfair differences in health to make London a healthier, fairer city. This dataset reports the 14 headline population health indicators that will be used to monitor London’s progress in reducing health inequalities over the next ten years. The themes of the indicators are listed below. The measures will monitor an identified inequality gap between defined populations. Healthy life expectancy at birth – male Healthy life expectancy at birth – female Children born with low birth weight School readiness among children Excess weight in children at age 10-11 (year 6) Excess mortality in adults with serious mental illness Suicide Mortality caused by Particulate Matter (PM2.5) Employment Feeling of belonging to a community (provisional) HIV late diagnosis People diagnosed with TB Adults walking or cycling for two periods of ten minutes each day Smoking

This archive contains the ESRC funded data collection (UK data) only.

The data collection contains a state file consisting of 902 variables, 677 observations. The codebook available in the data collection provides detailed descriptions of variables and data codes (missing etc). For more information please contact stephani.hatch@kcl.ac.uk

Research from the United Kingdom and the United States shows wide health inequalities by race/ethnicity and socio-economic status. So far we do not clearly understand the roles that discrimination and social context play in creating these inequalities.

Research teams at King's College London (UK) and Columbia University (USA) will carry out studies to investigate:

the roles that the historical social context and policy play in shaping observed patterns of health inequalities;

differences in anticipated and perceived experiences of discrimination;

how discrimination contributes to inequalities in everyday social functioning, mental health, physical health, and use of health services.

Comparisons will be made with 1600 adults from two larger studies, (i) the UK National Institute for Health Research-funded South East London Community Health study at the Biomedical Research Centre for Mental Health, and (ii) the US National Institute of Health-funded Child Health and Development Disparities Study in the East Bay Area of California. UK and US researchers, health practitioners, and community members will be invited to participate in developing the social and historical contextual narratives and in planning the dissemination of our research findings.

Health inequalities are the differences in health and wellbeing, risk or outcomes, between different groups of people. Tackling health inequalities requires knowledge about the factors affecting health. With input from key stakeholders we selected 12 indicators of health and the wider determinants of health which we will monitor over time. These indicators will improve our understanding of health inequalities.

Go to Tackling London’s Health Inequalities for more information on the HIS Health Inequalities Strategy and the Indicators.

Data and Resources

The most recent data for each indicator will be available for download below:

Overall measures of health inequality:

1. Slope Index of Inequality for Life Expectancy
2. Healthy Life Expectancy
3. Self-reported wellbeing:
   1. Happiness
   2. Life Satisfaction
   3. Worthwhileness
   4. Anxiety

More specific measures of health inequality:

1. School Readiness at age 5
2. Educational Achievement - Percentage of pupils achieving 5 or more GCSEs at grades A*-C (including English and Maths)
3. Antenatal late booking - Percentage of pregnant women booking after 12wks +6 days
4. Flu vaccination proportion of those >65 years and of those at risk
5. Vulnerable road user risk of fatality or serious injury
6. Homelessness
   1. Statutory homelessness
   2. Homelessness prevention
   3. Change in number of rough sleepers
7. Proportion of employees receiving lower income than the London Living Wage
8. Unemployment rates – model-based estimates; proportion of resident population
9. Childhood obesity - Proportion of children aged 4-5 classified as overweight or obese.

Description This data companion pack is a resource intended to frame and be read alongside the linked Evidence Review: Housing and Health Inequalities in London (available on the Institute of Health Equity website) . The resource provides intelligence and context on the housing and health inequalities in London only, while the accompanying rapid review of evidence for interventions contains the recommendations for action.​ This pack is intended to provide a high-level overview of the best available data on housing in London and the key housing-related health inequalities issues faced by Londoners, in correlation with the IHE Evidence Review: Housing and Health Inequalities in London.​ This pack identifies how certain groups in the population are at greatest risk of housing-related health inequalities, as well as noting gaps in available data for particularly excluded groups. The pack provides a platform for partnership work on housing-related health inequalities across London, including providing an overview of key issues, and identifying key gaps in intelligence that would help improve understanding of housing-related inequalities across the capital. ​ Audience It will be useful for health leaders, analysts, officers, and policy makers from local and regional government, integrated care systems, and more, to address ​housing-related health inequalities by - Advocating for the need for action to address housing inequalities, given impacts on health and health inequalities ​ Framing the context for the interventions highlighted in the linked rapid review of interventions​ Engaging communities ​ ​Development of this resource The Institute of Health Equity (IHE), Greater London Authority (GLA) Health, GLA City Intelligence Unit, Office for Health Improvement and Disparities London (OHID), Association of Directors of Public Health London (ADPH), and NHSE have collaboratively produced this report, as part of the Building the Evidence (BTE) programme of work The sources of data available and topics included have been identified from existing published data, working in partnership through iterative discussion The resource is provided in PDF and PowerPoint format to support colleagues in their work to There is no current plan for periodic updates of this resource, though this will be discussed on completion of this programme of work

This slide set is an analysis intended to inform Camden and Islington Public Health team and Clinical Commissioning Groups about causes of death contributing to changes in the life expectancy gap over time. Specifically, this analysis shows how the gap in life expectancy between the most and least deprived areas have changed over time in Camden and Islington, and explores what causes of death are contributing to these changes

Healthy life expectancy at birth by groupings of LSOAs into national deciles of area deprivation.

Source agency: Office for National Statistics

Designation: National Statistics

Language: English

Alternative title: Inequality in Healthy Life Expectancy at birth by national deciles of area deprivation: England

Unlocking Data to Inform Public Health Policy and Practice: WP1 Mapping Review Supplementary Excel S1
The data extracted into Excel Tab "S1 Case studies (extracted)" represents information from 31 case studies as part of the "Unlocking Data to Inform Public Health Policy and Practice" project, Workpackage (WP) 1 Mapping Review. Details about the WP1 mapping review can be found in the "Unlocking Data to Inform Public Health Policy and Practice" project report, which can be found via this DOI link: https://doi.org/10.15131/shef.data.21221606

OBJECTIVES: To determine the association between area and individual measures of social disadvantage and infant health in the United Kingdom (UK). DESIGN: Systematic review and meta-analyses. DATA SOURCES: 26 databases and web sites, reference lists, experts in the field and hand-searching. STUDY SELECTION: 36 prospective and retrospective observational studies with socio-economic data and health outcomes for infants in the UK, published from 1994 to May 2011. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers assessed the methodological quality of the studies and abstracted data. Where possible, study outcomes were reported as odds ratios for the highest versus the lowest deprivation quintile. RESULTS: In relation to the highest versus lowest area deprivation quintiles the odds of adverse birth outcomes were 1.81 (1.71 to 1.92) for low birth weight, 1.67 (1.42 to 1.96) for premature birth and 1.54 (1.39 to 1.72) for still birth. For infant mortality rates the odds ratios were 1.72 (1.37 to 2.15) overall, 1.61 (1.08 to 2.39) for neonatal and 2.31 (2.03 to 2.64) for post-neonatal mortality. For lowest versus highest social class, the odds were 1.79 (1.71 to 1.92) for premature birth, 1.52 (1.44 to 1.61) for overall infant mortality, 1.42 (1.33 to1.51) for neonatal and 1.69 (1.53 to 1.87) for post-neonatal mortality. There are similar patterns for other infant health outcomes with the possible exception of failure to thrive, where there is no clear association. CONCLUSIONS: This review quantifies the influence of social disadvantage on infant outcomes in the UK. The magnitude of effect is similar across a range of area and individual deprivation measures and birth and mortality outcomes. Further research should explore the factors that are more proximal to mothers and infants, to help throw light on the most appropriate times to provide support and the form(s) that this support should take.

This indicator measures inequalities in life expectancy at birth within England as a whole, each English region, and each local authority. Life expectancy at birth is calculated for each deprivation decile of lower super output areas within each area and then the slope index of inequality (SII) is calculated based on these figures.

The SII is a measure of the social gradient in life expectancy, i.e., how much life expectancy varies with deprivation. It takes account of health inequalities across the whole range of deprivation within each area and summarises this in a single number. This represents the range in years of life expectancy across the social gradient from most to least deprived, based on a statistical analysis of the relationship between life expectancy and deprivation across all deprivation deciles.

Life expectancy at birth is a measure of the average number of years a person would expect to live based on contemporary mortality rates. For a particular area and time period, it is an estimate of the average number of years a newborn baby would survive if he or she experienced the age-specific mortality rates for that area and time period throughout his or her life.

The SII for England and for regions have been presented alongside the local authority figures in order to improve the display of the indicators on the overview page. However, they should not be considered as comparators for the local authority figures. The SII for England takes account of the full range of deprivation and mortality across the whole country. This does not therefore provide a suitable benchmark with which to compare local authority results, which take into account the range of deprivation and mortality within much smaller geographies.

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

aSII: Slope Index of Inequality; RII(mean): Relative Index of Inequality for the mean; and RII(ratio): Relative Index of Inequality for the ratio.

This publication signposts to a range of information relating to a range of Public Health Statistics on Alcohol, Drug Misuse, Obesity, Physical Activity, Diet and Smoking. The publication is broken down by data source and within that the domains as follows: Part 1: Hospital Admissions - sourced from the Office for Health Improvement and Disparities (OHID). Part 2: Mortality - sourced from the Office for Health Improvement and Disparities (OHID). Part 3: Prescriptions - sourced from the NHS Business Services Authority (NHS BSA). Part 4: Affordability and Expenditure - sourced from the Office for Health Improvement and Disparities (OHID). Part 5: Other Data Sources.

Objective: This study explored the additive value of the multi-item EuroQol 5-Dimension 5-Level (EQ-5D-5L) as an outcome measure in health inequality analyses, relative to the single-item EuroQol visual analog scale (EQ VAS).Methods: A sample comprising the general population from Italy, the Netherlands, and United Kingdom (UK) completed the EQ-5D-5L and the EQ VAS. The level of education was selected as a proxy for socio-economic status (SES). EQ-5D-5L level sum scores (LSS) were compared against EQ VAS scores. Stratified and multivariable analyses were used to study the associations between SES and the LSS/EQ VAS relative to the presence of chronic health conditions.Results: A total of 10,172 people participated in this study. In the UK and Netherlands, the LSS was worst for respondents with a low educational level and better for respondents with middle and high educational levels. For Italy, the LSS was best for respondents with a middle educational level compared to respondents with low and high educational levels. The same patterns were observed for the EQ VAS, but differences were slightly smaller. Multivariable analyses showed generally stronger predictive relations in the UK, and with the LSS. The presence of chronic health conditions and being unable to work were independent strong predictors, canceling out the effects of education.Conclusions: In three different European countries, the EQ-5D measures show the presence of education-dependent health inequalities, which are universally explained in regression analysis by independently the presence of chronic health conditions and the inability to work. In stratified analysis, the EQ-5D-5L LSS discriminates slightly better between participants with different levels of SES compared to the EQ VAS.

Life expectancy (LE), healthy life expectancy (HLE), disability-free life expectancy (DFLE), Slope Index of Inequality (SII) and range by national deprivation deciles using the Index of Multiple Deprivation 2015 for data periods from 2011 to 2013 to 2015 to 2017, and the Index of Multiple Deprivation 2019 for data periods from 2016 to 2018 to 2018 to 2020: England, 2011 to 2013 to 2018 to 2020.