1 / 24

Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status

Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status. Barry Bosworth, Gary Burtless and Kan Zhang The Brookings Institution 16th Annual Joint Conference of the Retirement Research Consortium August 7-8, 2014. Mortality differentials by social and economic status.

ivie
Download Presentation

Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status Barry Bosworth, Gary Burtless and Kan Zhang The Brookings Institution 16th Annual Joint Conference of the Retirement Research Consortium August 7-8, 2014

  2. Mortality differentials by social and economic status • At a given age, death rates are higher for folks with low SES • SES measured by income, earnings, or education • Mounting evidence mortality gap is growing • Goal of study: Use HRS data to find reason • Evidence in HRS of growing SES differential? • Causes of death that explain growing gap? • Can growing differences in health-related behavior (smoking, exercise) account for the gap?

  3. Expected age of death among white men and women attaining age 45 in the National Longitudinal Mortality Study, 1979-1985 Women Men Source: Rogot, Sorlie, and Johnson (1992).

  4. Health & Retirement Study • Cohorts spanning birth years 1890-1965 • We examine deaths occurring in 1992-2010 • Sample includes almost 32,000 aged & near-aged Americans • Of whom more than 11,500 died between 1992-2010 • HRS data file contains range of info on SES • Educational attainment & current income • For almost 2/3 of sample, Social Security earnings record • Also: Health status, health-related behaviors, parents’ life spans

  5. Our measures of SES • Educational attainment • Less than high school diploma • College degree or more • Actual average nonzero earnings (ages 41-50) • Based on reported earnings in Social Security record • Combined husband-wife earnings adj. for family size • Predictedaverage nonzero earnings (ages 41-50) • Regression predictions explained by education, race/ethnicity, disability, marital status • In current paper, we use earnings estimate to predict R’s position in income distribution: Top or bottom half.

  6. Age-specific mortality rates observed in RHS sample: 1992-2010 Born in 1925 Born in 1935 Born in 1945 Source: Tabulations of HRS mortality data.

  7. Age-specific mortality rates observed in RHS sample compared with SSA estimates (2005) Born in 1925 SSA estimates Born in 1935 Born in 1945 Source: Tabulations of HRS and SSA (2005).

  8. Age-specific mortality rates observed in RHS sample: 1992-2010 Born in 1925 Born in 1935 Born in 1945 Source: Tabulations of HRS mortality data.

  9. Age-specific mortality rates observed in RHS sample compared with SSA estimates (2005) SSA estimates Born in 1925 Born in 1935 Born in 1945 Source: Tabulations of HRS and SSA (2005).

  10. Does socioeconomic status affect mortality in the HRS? • To find out we use discrete-time logistic model to estimate the influence of risk factors linked to mortality: • Age • Race / ethnicity • Marital status • Alternative measures of SES • All measures of SES are linked in expected way with age-specific mortality rates • Low SES boosts mortality; High SES reduces it.

  11. Estimated mortality rates of low and high predicted earners, by age Predicted low earner Predicted high earner Source: Tabulations of HRS mortality data.

  12. Estimated mortality rates of low and high predicted earners, by age Predicted low earner Predicted high earner High earner born in 1935 Source: Tabulations of HRS mortality data.

  13. Does the impact of socioeconomic status on mortality grow in successive birth cohorts? • Using a simple discrete-time logistic model to estimate the influence of SES in “Early” and “Later” birth cohorts: • “Early cohorts” = Born between 1915-1930 • “Later cohorts” = Born between 1931-1942 • Restrict sample to respondents born in 1915-1942 • Restrict sample to observations for these respondents when they were between ages 68-79 • What is impact of predicted income in top half of income distribution in “Early” vs. “Later” cohorts?

  14. Mortality rates of low and high predicted earners, by age in “Early” cohort Predicted low earner born before 1931 Predicted high earner born before 1931 Source: Tabulations of HRS mortality data.

  15. Mortality rates of low and high predicted earners, by age in “Early” and “Later” cohorts Predicted low earner born before 1931 Predicted high earner born before 1931 Predicted high earner born after 1930 Source: Tabulations of HRS mortality data.

  16. Mortality rate differential of low and high predicted earners, by age in “Early” cohorts Difference Ratio Mortality rate ratio for those born before 1931 Mortality rate difference for those born before 1931 (%) Source: Tabulations of HRS mortality data.

  17. Mortality rate differential of low and high predicted earners, by age in “Early” & “Later” cohorts Difference Ratio Mortality rate ratio for those born before 1931 Mortality rate difference for those born after 1930 (%) Mortality rate difference for those born before 1931 (%) Source: Tabulations of HRS mortality data.

  18. Mortality rate differential of low and high predicted earners, by age in “Early” cohorts Difference Ratio Mortality rate ratio for those born before 1931 Mortality rate difference for those born before 1931 (%) Source: Tabulations of HRS mortality data.

  19. Mortality rate differential of low and high predicted earners, by age in “Early” & “Later” cohorts Difference Ratio Mortality rate ratio for those born before 1931 Mortality rate difference for those born after 1930 (%) Mortality rate difference for those born before 1931 (%) Source: Tabulations of HRS mortality data.

  20. Does the impact of socioeconomic status on mortality grow in successive birth cohorts? • When we use our full sample we find meaningfully large and statistically significant increases in the SES mortality differential across successive birth cohorts • Using both actual and predicted Social-Security-earnings • Using indicators of low and high educational attainment • In specifications where we control for race/ethnicity, disability, and marital status: • We find worsening of mortality in low SES groups • Less than high school / Bottom half of actual or predicted income • Versus generally declining mortality in high SES groups

  21. Changing impact of socioeconomic status on mortality by specific cause of death • We use discrete time logistic models to examine evolution mortality by 8 causes of death • Significant drop in age-specific mortality due to heart disease & cancer for top half of predicted income; • No significant decline in deaths due to these causes for people in bottom half of predicted income. • Similar pattern findings for male deaths due to • “Allergies, hay fever, sinusitis and tonsillitis” & “miscellaneous” • Both among low-predicted-income men & women we find significant increases in mortality due to • “Digestive system issues”

  22. Can behavioral differences account for widening mortality differences by SES group? • To test, we added self-reported behaviors to specification: • Alcohol consumption (level) • Smoking (Sometime in past? and Currently?) • Vigorous physical activity at least once a week • We also examined impact of parental longevity • Finally, we tested the explanatory power and impact of self-reported health in the first HRS interview • Basic idea: If the inclusion of the behavioral variables reduces the measured impact of SES on changes in mortality differentials, then changes in behavior by SES group may account for the growing difference in mortality by SES

  23. Can behavioral differences account for widening mortality differences by SES group? • Self-reported, health-related behaviors have expected and highly significant impacts on risk of mortality -- • Alcohol consumption and Smoking boost age-specific mortality • Vigorous physical activity reduces mortality • Inclusion of behavior variables in specification increases estimated mortality gradient • We find little effect of parental longevity on mortality • Inclusion of initial health status has little impact on the estimated size of change in mortality gradient

  24. Conclusions • All our measures of SES show sizeable mortality differentials by SES group • All measures also show significant increases in magnitude of differentials in later cohorts compared with earlier ones • We find some causes of death--heart disease, cancer, and (among men) “Allergies, hay fever, sinusitis and tonsillitis”—have declined among those with high predicted income but not among those with low SES • Mortality due to “Digestive system issues” has risen among low SES but not high SES groups • Inclusion of health-related behavioral variables does not reduce noticeably the estimated increase in mortality differentials by SES

More Related