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Special Communication |

The Association Between Income and Life Expectancy in the United States, 2001-2014

Raj Chetty, PhD1; Michael Stepner, BA2; Sarah Abraham, BA2; Shelby Lin, MPhil3; Benjamin Scuderi, BA4; Nicholas Turner, PhD5; Augustin Bergeron, MA4; David Cutler, PhD4
[+] Author Affiliations
1Department of Economics, Stanford University, Stanford, California
2Department of Economics, Massachusetts Institute of Technology, Cambridge, Massachusetts
3McKinsey and Company, New York, New York
4Department of Economics, Harvard University, Cambridge, Massachusetts
5Office of Tax Analysis, US Treasury, Washington, DC
JAMA. 2016;315(16):1750-1766. doi:10.1001/jama.2016.4226.
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Published online

Importance  The relationship between income and life expectancy is well established but remains poorly understood.

Objectives  To measure the level, time trend, and geographic variability in the association between income and life expectancy and to identify factors related to small area variation.

Design and Setting  Income data for the US population were obtained from 1.4 billion deidentified tax records between 1999 and 2014. Mortality data were obtained from Social Security Administration death records. These data were used to estimate race- and ethnicity-adjusted life expectancy at 40 years of age by household income percentile, sex, and geographic area, and to evaluate factors associated with differences in life expectancy.

Exposure  Pretax household earnings as a measure of income.

Main Outcomes and Measures  Relationship between income and life expectancy; trends in life expectancy by income group; geographic variation in life expectancy levels and trends by income group; and factors associated with differences in life expectancy across areas.

Results  The sample consisted of 1 408 287 218 person-year observations for individuals aged 40 to 76 years (mean age, 53.0 years; median household earnings among working individuals, $61 175 per year). There were 4 114 380 deaths among men (mortality rate, 596.3 per 100 000) and 2 694 808 deaths among women (mortality rate, 375.1 per 100 000). The analysis yielded 4 results. First, higher income was associated with greater longevity throughout the income distribution. The gap in life expectancy between the richest 1% and poorest 1% of individuals was 14.6 years (95% CI, 14.4 to 14.8 years) for men and 10.1 years (95% CI, 9.9 to 10.3 years) for women. Second, inequality in life expectancy increased over time. Between 2001 and 2014, life expectancy increased by 2.34 years for men and 2.91 years for women in the top 5% of the income distribution, but by only 0.32 years for men and 0.04 years for women in the bottom 5% (P < .001 for the differences for both sexes). Third, life expectancy for low-income individuals varied substantially across local areas. In the bottom income quartile, life expectancy differed by approximately 4.5 years between areas with the highest and lowest longevity. Changes in life expectancy between 2001 and 2014 ranged from gains of more than 4 years to losses of more than 2 years across areas. Fourth, geographic differences in life expectancy for individuals in the lowest income quartile were significantly correlated with health behaviors such as smoking (r = −0.69, P < .001), but were not significantly correlated with access to medical care, physical environmental factors, income inequality, or labor market conditions. Life expectancy for low-income individuals was positively correlated with the local area fraction of immigrants (r = 0.72, P < .001), fraction of college graduates (r = 0.42, P < .001), and government expenditures (r = 0.57, P < .001).

Conclusions and Relevance  In the United States between 2001 and 2014, higher income was associated with greater longevity, and differences in life expectancy across income groups increased over time. However, the association between life expectancy and income varied substantially across areas; differences in longevity across income groups decreased in some areas and increased in others. The differences in life expectancy were correlated with health behaviors and local area characteristics.

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Figure 1.
Gompertz Approximations and Empirical Survival Curves for Men in the 5th and 95th Income Percentiles, 2001-2014

For panels A and B, the data for the scatter points were derived from cross-sectional mortality rates by age using income 2 years prior for men aged 40 to 62 years and cohort mortality rates by year using income observed at age 61 years for men aged 63 to 76 years. Empirical mortality rates were observed until the age of 76 years; therefore, empirical survival rates are observed until the age of 77 years. Solid lines show Gompertz extrapolations through the age of 90 years. In panel B, uniform mortality rates from the National Center for Health Statistics (NCHS) and the Social Security Administration (SSA) were used beyond the age of 90 years. Analogous results for women appear in eFigure 4 in the Supplement.

aThe mortality rates were constant across income groups.

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Figure 2.
Race- and Ethnicity-Adjusted Life Expectancy for 40-Year-Olds by Household Income Percentile, 2001-2014

Life expectancies were calculated using survival curves analogous to those in Figure 1. The vertical height of each bar depicts the 95% confidence interval. The difference between expected age at death in the top and bottom income percentiles is 10.1 years (95% CI, 9.9-10.3 years) for women and 14.6 years (95% CI, 14.4-14.8 years) for men. To control for differences in life expectancies across racial and ethnic groups, race and ethnicity adjustments were calculated using data from the National Longitudinal Mortality Survey and estimates were reweighted so that each income percentile bin has the same fraction of black, Hispanic, and Asian adults.

aAveraged across years and ages. The data are in thousands unless otherwise indicated.

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Figure 3.
Changes in Race- and Ethnicity-Adjusted Life Expectancy by Income Group, 2001-2014

Scatter points in the A panels show the race- and ethnicity-adjusted life expectancy estimates by year and household income quartile. Solid lines represent best fit lines estimated using ordinary least-squares regression. The B panels plot the slopes from analogous regressions estimated separately by income ventile (5 percentile point bins). Dashed lines show 95% confidence intervals.

aAveraged across years and ages.

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Figure 4.
Race- and Ethnicity-Adjusted Life Expectancy by Income Ventile in Selected Commuting Zones, 2001-2014

Estimates of race- and ethnicity-adjusted expected age at death for 40-year-olds computed by income ventile (5 percentile point bins).

aAveraged across years and ages.

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Figure 5.
Race- and Ethnicity-Adjusted Life Expectancy by Commuting Zone and Income Quartile, 2001-2014

Estimates of race- and ethnicity-adjusted expected age at death for 40-year-olds computed by commuting zone. The 595 commuting zones with populations above 25 000 are grouped into deciles and colored from dark to light as expected age at death increases. The second and third quartiles appear in eFigure 10 in the Supplement.

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Figure 6.
Mean Annual Change in Life Expectancy by State for Bottom Income Quartile, 2001-2014

Annual changes estimated using ordinary least-squares regression of race- and ethnicity-adjusted expected age at death for 40-year-olds on calendar year by state. States are grouped into deciles and colored from red to turquoise as annual change in expected age at death increases.

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Figure 7.
Expected Life Expectancy for Individuals in the Bottom Income Quartile Living in Selected Commuting Zones, 2001-2014

Solid lines indicate best linear fit, estimated using ordinary least-squares regression.

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Figure 8.
Correlations Between Life Expectancy in the Bottom Income Quartile and Local Area Characteristics, 2001-2014

Population-weighted univariate Pearson correlations estimated between local area characteristics and race- and ethnicity-adjusted expected age at death for 40-year-olds in the bottom income quartile. These correlations were computed at the commuting zone level after averaging life expectancy across sexes. The error bars indicate 95% confidence intervals with errors clustered by state. Definitions and sources of all variables appear in eTable 3 in the Supplement.

aAmong individuals in the bottom income quartile.

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Figure 9.
Correlations Between Life Expectancy in the Top Income Quartile and Local Area Characteristics, 2001-2014

Population-weighted univariate Pearson correlations estimated between local area characteristics and race- and ethnicity-adjusted expected age at death for 40-year-olds in the top income quartile. These correlations were computed at the commuting zone level after averaging life expectancy across sexes. The error bars indicate 95% confidence intervals with errors clustered by state. Definitions and sources of all variables appear in eTable 3 in the Supplement.

aAmong individuals in the top income quartile.

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Tables

References

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House Value and Health
Posted on May 9, 2016
Isabel Azevedo
Department of Biochemistry, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
Conflict of Interest: None Declared
House value and health

Life expectancy relates to income through many factors and their interactions, in a way difficult to disentangle. In the present study, the strong correlation between life expectancy for the bottom income quartile of the observed population and median home values is an impressive finding, deserving further consideration.


The importance of house value for health had already been suggested by its inverse association with obesity rates,1,2 Healthy Eating Index scores,3 and morbidity and mortality risk in older people.4 However, as in the present study, house value has mainly been looked at as an indicator of socioeconomic status2,3 or of cumulative wealth,4 in spite of observations in these same works that the house value continued to be strongly associated with both morbidity and mortality after adjustment for educational attainment and social class,4 that residential property values were not completely concordant with income categories or with educational attainment,3 that, for women, the inverse association between the prevalence of obesity and the value of their home was strong and independent of education and incomes,2 and that in a model adjusting for covariates and spatial dependence, property values were the strongest predictor of the area-based smoothed obesity prevalence.1

The house is often described, in literature, as intimately connected to the life and history of people and families.5 In an interesting essay on The Architecture of Happiness,6 a philosopher refers to the house not only as a physical and psychical refuge, but also as a guardian of identity.

Low socioeconomic status associates with chronic stress, obesity, the metabolic syndrome and cardiovascular diseases, as previously discussed.7 Chronic stress in low social status situations predominantly involves a particular type of stress reaction, a defeat reaction, encompassing higher cortisol levels, decrease of immunity, and a perception of lack of control.8 Marmot9 had already called the attention to the fact that in the relationship between social status and health “the question is not simply one of income or lifestyle. It is the psychological experience of inequality - how much control you have over your life and the opportunities you have for full social participation - that has a profound effect on your health.”

As well understood by architects,10 the house may strongly contribute to the identity and self-esteem of its inhabitants. Its durability adds strength to that quality. It would be interesting to investigate the relative importance of the house characteristics that determine its value – localization, size, beauty, quality, or the participation of the owner, together with assistance from qualified architects, in its design, in the health positive effects it seems to have. After all, and despite its apparently expensive cost, a higher attention to housing would be a relatively simple task to improve public health policies.

References
1. Drewnowski A, Rehm CD, Solet D. Disparities in obesity rates: analysis by ZIP code area. Soc Sci Med. 2007;65(12):2458-2463.
2. Rehm CD, Moudon AV, Hurvitz PM, Drewnowski A. Residential property values are associated with obesity among women in King County, WA, USA. Soc Sci Med. 2012;75(3):491-495. doi: 10.1016/j.socscimed.2012.03.041.
3. Drewnowski A, Aggarwal A, Cook A, Stewart O, Moudon AV. Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS). Prev Med. 2016 Feb;83:46-55. doi: 10.1016/j.ypmed.2015.11.021.
4. Connolly S, O’Reilly D, Rosato M: House value as an indicator of cumulative wealth is strongly related to morbidity and mortality risk in older people: a census-based cross-sectional and longitudinal study. Int. J. Epidemiol. 2010;39 (2): 383-391. doi: 10.1093/ije/dyp356
5. Proust M. Sur la lecture. Arles, France: Actes Sud; 1988.
6. De Botton A. The architecture of happiness. London: Penguin Books; 2007.
7. Azevedo A, Santos AC, Ribeiro L, Azevedo I. The metabolic syndrome. In: Soares R, Costa C, eds. Oxidative stress, inflammation and angiogenesis in the metabolic syndrome. Springer; 2009: 1-19.
8. Folkow B. Evolutionary aspects of stress. In: Arnetz BB, Ekman R, eds. Stress in health and disease. Weinheim: Wiley-VCH; 2006: 20-45.
9. Marmot M. The Status Syndrome: How Social Standing Affects Our Health and Longevity. New York, NY: Owl Books; 2004.
10. Fathy H. Architecture for the poor. Chicago, Ill: The University of Chicago Press; 1973.


Isabel Azevedo, MD, PhD
Department of Biochemistry, Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
e-mail: isabelazeve@gmail.com
phone: 351227622969
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