Estimates of life expectancy and the slope index of inequality measure by NS-SEC.

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

This table shows three variants of healthy life expectancy: -Life expectancy in perceived good health. -Life expectancy without reported physical limitations. -Life expectancy without reported chronic diseases. -Life expectancy in good mental health In addition, data on mortality probabilities and total life expectancy are presented. Total life expectancy indicates the number of years that a person of a given age is expected to live. In the table, the data on (healthy) life expectancy can be broken down into the following characteristics: -Gender -Age -Income The standardized disposable household income allocated to individuals is used as an indicator of socio-economic status. The figures in the publication relate to the average over the years 2004 up to and including 2007, the average over the years 2007 up to and including 2010, the average over the years 2011 up to and including 2014 and the average over the years 2014 up to and including 2017. Data available from 2004/2007 up to and including 2017. Status of the figures: The figures in this table are final Changes as of 21 December 2022: None, this table has been discontinued. When will new numbers come out? Not applicable anymore. This table is followed by the Healthy life expectancy table; income and wealth. See section 3.

Life expectancy at birth - number of years newborn female children would live if subject to the mortality risks prevailing for the cross section of population at the time of their birth (estimated)

Summary of responses from the questionnaire section “The potential for more personalised information on life expectancy” (Questions 12–14). Frequencies (N, out of 85 except where indicated) and percentages (%) are presented.

Summary of responses to Question 11 (“How do you use, or how have you used in the past, any information which you have learned about your life expectancy, either from your doctor/CF care team or from other sources?”) in section “Whether and how you current find information about life expectancy”. Frequencies (N, out of 85) and percentages (%) are presented and the rows are ordered by the percentage who selected each option. Respondents could select more than one response.

Potential determinants of efficiency, 2015–2019 cross-section.

Summary of multiple choice questionnaire responses from the questionnaire section “Whether and how you currently find information about life expectancy” (Questions 8–10). Frequencies (N, out of 85 except where indicated) and percentages (%) are presented. The shaded areas indicate the sub-question was not applicable.

his paper consists of five related notes on Japanese health care.\ud \ud Section 1 of the paper proposes a simple model of health care needs in a stationary population where all the sickness is concentrated in the period leading up to death. The main variables determining the burden of health care, such as life expectancy, duration of chronic illness prior to death, etc., are identified. While we are not able to comment (at this time), on trends in the prevalence of chronic conditions in old age, extrapolation of trends in life expectancy presented in Section 2 of the paper suggest that there will be continuing increase in the number of Japanese surviving to extremely old ages. This aging of the population will assuredly put upward pressure on health spending, but this pressure must be put in the context of other factors. Section 3 decomposes increase in Japanese health care spending into portions attributable to overall demographic increase, change in population age structure, and change in a residual "underlying factors" term subsuming changes in technology, health system coverage, etc. The residual dominates total increase in health care spending. In fact, based on historical data and projected demographic trends, the strongest upward pressure from population aging occurred in the period 1980-95, when aging accounted for 1.4 percentage points of 5.6% per annum total health expenditure growth. Health care spending growth attributed to ageing is estimated to be 1.13% per annum in 1995- 2020 and only 0.34% per annum in 2020-2050.\ud \ud Section 4 focuses on home care of the elderly and suggests that there is a substantial ongoing decline in the supply of potential in-family caregivers. Lower fertility is an important determinant of this trend. Section 5 describes the overall profile of the Japanese health care system, noting that it receives relatively high marks in international comparisons but tends to lump together acute care and chronically ill patients. As recognized by the "Gold Plan" policy currently being implemented, there is a severe shortage of nursing home facilities beds as well as services to make home care a more practical option for families. A simple ratio analysis suggests that the number of bedridden chronically ill persons (i.e., the population that would ideally be cared for in a nursing home setting) will reach 1,800,000 by 2020 as opposed to 600,000 today.

Utilising a regression analysis we created a correlation matrix utilising a number of demographic indicators from the Local Insight platform. This application is showing the distribution of the datasets that were found to have the strongest relationships, with the base comparison dataset of Indices of Deprivation 2019 income deprivation affecting older people. This app contains the following datasets: proportion of single pension credit claimants, proportion of retirement age people receiving pension credit guarantee element, proportion of benefit claimants aged 50 to 64, proportion of people with numeracy skills at entry level 1 or below, Indices of Deprivation 2015 housing affordability indicator, proportion of people in the Social Grade (N-SEC) 8 never worked and long-term unemployed, female healthy life expectancy at birth, proportion of people part of Sport England Market Segmentation Pub League Team Mates, Indices of Deprviation 2010 income domain score and proportion of people over the age of 65 with 'bad' or 'very bad' health.

(by Joseph Kerski)This map is for use in the "What is the spatial pattern of demographic variables around the world?" activity in Section 1 of the Going Places with Spatial Analysiscourse. The map contains population characteristics by country for 2013.These data come from the Population Reference Bureau's 2014 World Population Data Sheet.The Population Reference Bureau (PRB) informs people around the world about population, health, and the environment, empowering them to use that information to advance the well-being of current and future generations.PRB analyzes complex demographic data and research to provide the most objective, accurate, and up-to-date population information in a format that is easily understood by advocates, journalists, and decision makers alike.The 2014 year's data sheet has detailed information on 16 population, health, and environment indicators for more than 200 countries. For infant mortality, total fertility rate, and life expectancy, we have included data from 1970 and 2013 to show change over time. This year's special data column is on carbon emissions.For more information about how PRB compiles its data, see: https://www.prb.org/

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)

• construct waves of accessible and well-documented panel data;
• provide these data in a convenient and timely fashion to the scientific and policy research community;
• describe health trajectories, disability and healthy life expectancy in a representative sample of the English population aged 50 and over;
• examine the relationship between economic position and health;
• investigate the determinants of economic position in older age;
• describe the timing of retirement and post-retirement labour market activity; and
• understand the relationships between social support, household structure and the transfer of assets.

• Primary Data from Wave 8 onwards (SN 8444) includes all the variables in the SL primary dataset (SN 8346) as well as day of birth, combined SIC 2003 code (5 digit), combined SOC 2000 code (4 digit), NS-SEC long version including and excluding unclassifiable and non-workers.
• Pension Age Data from Wave 8 onwards (SN 8445) includes all the variables in the SL pension age data (SN 8375) as well as year reached pension age variable.
• Detailed geographical identifier files for each wave, grouped by identifier held under SN 8423 (Index of Multiple Deprivation Score), SN 8424 (Local Authority District Pre-2009 Boundaries), SN 8438 (Local Authority District Post-2009 Boundaries), SN 8425 (Census 2001 Lower Layer Super Output Areas), SN 8434 (Census 2011 Lower Layer Super Output Areas), SN 8426(Census 2001 Middle Layer Super Output Areas), SN 8435 (Census 2011 Middle Layer Super Output Areas), SN 8427 (Population Density for Postcode Sectors), SN 8428 (Census 2001 Rural-Urban Indicators), SN 8436 (Census 2011 Rural-Urban Indicators).

• either SN 8424 (Local Authority District Pre-2009 Boundaries) or SN 8438 (Local Authority District Post-2009 Boundaries)
• either SN 8425 (Census 2001 Lower Layer Super Output Areas) or SN 8434 (Census 2011 Lower Layer Super Output Areas)
• either SN 8426 (Census 2001 Middle Layer Super Output Areas) or SN 8435 (Census 2011 Middle Layer Super Output Areas)
• either SN 8428 (Census 2001 Rural-Urban Indicators) or SN 8436 (Census 2011 Rural-Urban Indicators)