According to a survey conducted in 2022, ** percent of healthcare leaders who were classed as early adopters of digital health technology and predictive analytics reported to have initiatives in place to deal with health inequalities, compared to ** percent of the global average.

The Overview of Health Disparities analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included 611 objectives in HP2020. This file contains summary level information used for the evaluation of changes in disparities during HP2020, including calculations for the disparities measures and the disparities change categories for all objectives and population characteristics in the analysis. See Technical Notes for the Healthy People 2020 Overview of Health Disparities (https://www.cdc.gov/nchs/healthy_people/hp2020/health-disparities-technical-notes.htm) for additional information and criteria for objectives, data years, and population characteristics included in the analysis and statistical formulas and definitions for the disparities measures. Data for additional years during the HP2020 tracking period that are not included in the Overview of Health Disparities are available on the HP2020 website (https://www.healthypeople.gov/2020/). Note that “rate” as used may refer to a statistical rate expressed per unit population or a proportion, depending on how the HP2020 objective was defined.

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

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

As of 2023, across 70 measures assessing health and healthcare in the U.S., the Black, AI/AN, and Hispanic populations fare worse than the White population. The racial/ethnic disparity was largest comparing Black and White populations. The Black population fared worse than the White population across 55 health and healthcare measures, while they only fared better than the White population for 12 of them.

On the other hand, the Asian population did not fare worse than White people across most examined measures. Nonetheless, these measures cover aspects of health coverage, access, and use; health status, outcomes, and behaviors; and social determinants of health, yet more is needed to provide the full scope of healthcare disparities.

Racial/ethnic health disparities are higher rates of serious health conditions or deaths that affect communities of color. These disparities can result in shorter lifespans and lower quality of life, are rooted in inequities in the opportunities and resources needed for good health, such as education, employment, safe and healthy neighborhoods, and access to health care. These inequities are often the result of current and historical institutionalized racism or explicit racial bias.

Abstract The issue of social inequalities is a subject of recurrent studies and remains relevant due to the growing trend of these inequalities over the years. This study proposes the creation of the Health Inequality Index (HII) composed of health indicators – Mean life span and Mean Potential Years of Life Lost (PYLL) – and socioeconomic indicators of income, schooling, and population living in poverty in the city of Natal – the State Capital of Rio Grande do Norte, Brazil. Therefore, a probabilistic linkage was made between mortality and socioeconomic databases in order to capture the census tracts of households with death records from 2007 to 2013. The authors used the Principal Component Factor Analysis to calculate the index. The Health Inequality Index showed areas with worse socioeconomic and health conditions located in the suburban areas of the city, with differences between and within the districts. The difference in the mean life span between the districts of Natal arrives at 25 years, and the worst district has mortality rates comparable to poor African countries. Public policymakers can use the index to prioritize actions aimed at reducing or eliminating health inequalities.

Background In the World Health Report 2000, the World Health Organization made the controversial choice to measure inequality across individuals rather than across groups, the standard in the field. This choice has been widely discussed and criticized. Discussion We look at the three questions: (1) is the World Health Organization's health inequality measure value-free as it claims? (2) if it is not, what is the normative position implied by its approach when measuring health inequality? and (3) is the individual approach a logically consistent methodological choice for that normative position? Summary We argue that the World Health Organization's health inequality measure is not value-free. If it was, the health inequality information that the measurement collected could not reasonably be included in its ranking of how well national health systems performed. The World Health Organization's normative position can be interpreted as a quite expansive view of justice, in which health distributions that have causes amenable to human intervention are considered to be matters of justice. Our conclusion is that if the World Health Organization's health inequality measure is to be interpreted meaningfully in a policy context, its conceptual underpinning must be re-evaluated.

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.

This release presents trends in estimates of mortality rates for males and females of working age in English regions and Wales, from 2001-03 to 2008-10, calculated using population denominators derived from the Labour Force Survey (LFS). The analysis is based on the seven class reduced National Statistics Socio-economic Classification (NS-SEC).

Source agency: Office for National Statistics

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Health Inequalities

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.

Unequal impact of COVID-19: BAME disproportionality This slide pack covers the latest PHE and ONS data, national and local, showing diagnosis and death rates by deprivation, underlying conditions and ethnicity (note: these analyses did not account for the effect of occupation, co-morbidities or obesity).

Abstract

This study explores three urban units; formal, transition and informal of the capital of Cape Verde, in terms of overweight/obesity, cardiometabolic risk, physical activity and other aspects related to the urban environment.

Geographic coverage

Sub-national coverage, only urban areas.

Analysis unit

Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

A random sampling strategy based on geographical coordinates of private households was used to select in each household one adult (greater than or equal to 18 years old), living at least six months in the neighbourhood. To select a random sample a sampling frame was needed, i.e., a complete list of all residents at least 18 years old who lived in each unit for at least 6 months. Given the lack of this type of sampling frame, an alternative sampling frame was developed based on the geographical coordinates of private households in each urban unit, combining GIS and statistical software.

Nonclassical households (hospitals, orphanages, military, etc.) and homeless were not included in this study. The urban planning team identified the geographical coordinates corresponding to households, providing the centroid of the polygons which is supposed to represent a building or a detached house. However, the spatial visualization shows roofs which may represent a household or a set of households, for example, a building with 7 floors with 2 households per floor. In the last case, we repeated the corresponding geographical coordinate 14 times. Field workers were needed to complete this exhaustive field work in order to provide a more realistic list of households in each area. This list was exported to SPSS statistical software and a random sample was generated for each area.

Mode of data collection

Face-to-face [f2f]

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers. More information about the data table and a data dictionary can be found in the About/Attachments section.

The Overview of Health Disparities analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included 611 objectives in HP2020. See Technical Notes for the Healthy People 2020 Overview of Health Disparities (https://www.cdc.gov/nchs/healthy_people/hp2020/health-disparities.htm) for additional information and criteria for objectives, data years, and population characteristics included in the analysis and statistical formulas and definitions for the disparities measures. This file contains estimates and standard errors for the baseline and final years for individual population groups used in the Overview of Health Disparities analysis. The number and definitions of population groups varied across the HP2020 objectives and data sources used. These population groups are shown in the disparities file as originally reported by the data source, rather than the harmonized categories that were used for the HP2020 Progress by Population Group analysis (https://www.cdc.gov/nchs/healthy_people/hp2020/population-groups.htm). Additionally, for any given objective, the baseline and final years used for the disparities analysis do not necessarily correspond to the baseline and final years used to evaluate progress toward target attainment in the HP2020 Final Review Progress Table (https://www.cdc.gov/nchs/healthy_people/hp2020/progress-tables.htm) and Progress by Population Group analysis (https://www.cdc.gov/nchs/healthy_people/hp2020/population-groups.htm). These distinctions should be considered when merging the downloadable Progress Table or Progress by Population Group data files with the Overview of Health Disparities data files, or when integrative analyses that incorporate both disparities and progress data are conducted. Data for additional years during the HP2020 tracking period that are not included in the Overview of Health Disparities are available on the HP2020 website (https://www.healthypeople.gov/2020/).

Do files and other material. Visit https://dataone.org/datasets/sha256%3A133b84ac2f5e6e396eca309003b952473aa2f551e78eb0fc135c444ec49791cc for complete metadata about this dataset.

Unequal impact of COVID-19: BAME disproportionality Camden Demographics of Shielded Population by location age ethnicity deprivation gender GPs and reason for shielding.

This dataset is inspired by the need to address health and socio-economic disparities affecting African American women and children from underserved communities. The variables of concern involve great causes that explain access to prenatal care, income level, and infant mortality rate, which provide valuable insights into public health research. The data will be structured to help analyze the influence of access to healthcare services and socio-economic status on maternal and child health outcomes, with the intention of informing policy changes and health interventions.