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

Overview of attention for article published in JAMA: Journal of the American Medical Association, April 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#2 of 18,552)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
273 news outlets
blogs
41 blogs
twitter
1797 tweeters
facebook
40 Facebook pages
googleplus
24 Google+ users
reddit
5 Redditors

Readers on

mendeley
293 Mendeley
citeulike
1 CiteULike
Title
The Association Between Income and Life Expectancy in the United States, 2001-2014
Published in
JAMA: Journal of the American Medical Association, April 2016
DOI 10.1001/jama.2016.4226
Pubmed ID
Authors

Raj Chetty, Michael Stepner, Sarah Abraham, Shelby Lin, Benjamin Scuderi, Nicholas Turner, Augustin Bergeron, David Cutler, Chetty, Raj, Stepner, Michael, Abraham, Sarah, Lin, Shelby, Scuderi, Benjamin, Turner, Nicholas, Bergeron, Augustin, Cutler, David

Abstract

The relationship between income and life expectancy is well established but remains poorly understood. 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. 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. Pretax household earnings as a measure of income. 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. 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). 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 293 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 15 5%
United Kingdom 4 1%
Spain 2 <1%
Switzerland 2 <1%
Colombia 2 <1%
Chile 1 <1%
Brazil 1 <1%
Denmark 1 <1%
Finland 1 <1%
Other 5 2%
Unknown 259 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 68 23%
Researcher 63 22%
Student > Master 32 11%
Student > Doctoral Student 29 10%
Professor 28 10%
Other 73 25%
Readers by discipline Count As %
Medicine and Dentistry 117 40%
Social Sciences 56 19%
Economics, Econometrics and Finance 41 14%
Psychology 19 6%
Agricultural and Biological Sciences 14 5%
Other 46 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 3834. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 March 2017.
All research outputs
#34
of 7,425,528 outputs
Outputs from JAMA: Journal of the American Medical Association
#2
of 18,552 outputs
Outputs of similar age
#3
of 270,530 outputs
Outputs of similar age from JAMA: Journal of the American Medical Association
#1
of 420 outputs
Altmetric has tracked 7,425,528 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 18,552 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 33.9. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,530 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 420 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.