Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status - PowerPoint Presentation

Slide1

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

Slide2

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?

Slide3

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).

Slide4

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

Slide5

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

Predicted

average 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.

Slide6

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.

Slide7

Age-specific mortality rates observed in RHS sample compared with SSA estimates (2005)

Born in 1925

Born in 1935

Born in 1945

SSA estimates

Source:

Tabulations of HRS and SSA (2005).

Slide8

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.

Slide9

Age-specific mortality rates observed in RHS sample compared with SSA estimates (2005)

Born in 1925

Born in 1935

Born in 1945

SSA estimates

Source:

Tabulations of HRS and SSA (2005).

Slide10

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

.

Slide11

Estimated mortality rates of low and high predicted earners, by age

Predicted low earner

Predicted high earner

Source:

Tabulations of HRS mortality data.

Slide12

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.

Slide13

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?

Slide14

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.

Slide15

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.

Slide16

Mortality rate differential of low and high predicted earners, by age in “Early” cohorts

Mortality rate ratio for those born before 1931

Mortality rate difference for those born before 1931 (%)

Source:

Tabulations of HRS mortality data.

Difference

Ratio

Slide17

Mortality rate differential of low and high predicted earners, by age

in “Early” & “Later” cohorts

Mortality rate ratio for those born before 1931

Mortality rate difference for those born before 1931 (%)

Mortality rate difference for those born after 1930 (%)

Source:

Tabulations of HRS mortality data.

Difference

Ratio

Slide18

Mortality rate differential of low and high predicted earners, by age

in “Early” cohorts

Mortality rate ratio for those born before 1931

Mortality rate difference for those born before 1931 (%)

Source: Tabulations of HRS mortality data.

Difference

Ratio

Slide19

Mortality rate differential of low and high predicted earners, by age

in “Early” & “Later” cohorts

Mortality rate ratio for those born before 1931

Mortality rate difference for those born before 1931 (%)

Mortality rate difference for those born after 1930 (%)

Source:

Tabulations of HRS mortality data.

Difference

Ratio

Slide20

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

Slide21

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

”

Slide22

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

Slide23

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

Slide24

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