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Figure.  Relative Geographic Median Household Income Inequality and Racial Inequality
Relative Geographic Median Household Income Inequality and Racial Inequality

Relative geographic inequality was quantified as the ratio of the 99th to the 1st percentile level using US Census data. Line graphs demonstrate (A) median household income inequality between the 99th and 1st percentile level using US Census Data, and (B) racial inequality between the 99th and 1st percentile counties over time, with the 99th percentile having the smallest percentage of minorities and the 1st percentile having the largest.

aMinorities are those coded as black, Hispanic, or Native by the US Census Bureau (these were the same race/ethnicity categories analyzed by Dwyer-Lindgren et al2).

1.
Fogel  RW.  Catching up with the economy.  Am Econ Rev. 1999;89(1):1-21. doi:10.1257/aer.89.1.1Google ScholarCrossref
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Dwyer-Lindgren  L, Bertozzi-Villa  A, Stubbs  RW,  et al.  Inequalities in life expectancy among US counties, 1980 to 2014: temporal trends and key drivers.  JAMA Intern Med. 2017;177(7):1003-1011. doi:10.1001/jamainternmed.2017.0918PubMedGoogle ScholarCrossref
3.
Fitz  N. Economic inequality: it’s far worse than you think. Scientific American. https://www.scientificamerican.com/article/economic-inequality-it-s-far-worse-than-you-think/. Accessed June 3, 2017.
4.
Humphreys  K. Why the wealthy stopped smoking, but the poor didn’t. Washington Post. January 14, 2015. https://www.washingtonpost.com/news/wonk/wp/2015/01/14/why-the-wealthy-stopped-smoking-but-the-poor-didnt/?utm_term=.5a11734146d8. Accessed June 3, 2017.
5.
Williams  DR, Mohammed  SA, Leavell  J, Collins  C.  Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities.  Ann N Y Acad Sci. 2010;1186:69-101. doi:10.1111/j.1749-6632.2009.05339.xPubMedGoogle ScholarCrossref
6.
Fischer  CS, Stockmayer  G, Stiles  J, Hout  M.  Distinguishing the geographic levels and social dimensions of U.S. metropolitan segregation, 1960-2000.  Demography. 2004;41(1):37-59.PubMedGoogle ScholarCrossref
Research Letter
April 2018

Assessment of Linked Associations in Predictors of Life Expectancy Inequality

Author Affiliations
  • 1Department of Emergency Medicine, University of Arizona College of Medicine–Phoenix, Maricopa Integrated Health System, Phoenix
JAMA Intern Med. 2018;178(4):563-564. doi:10.1001/jamainternmed.2017.7893

Human health has improved dramatically over the last couple centuries.1 Yet, disparities remain, and recent work2 suggests that these disparities are widening for life expectancy. These disparities are owing to a combination of socioeconomic and race/ethnicity factors, behavioral and metabolic risk factors, and health care factors. However, multivariable analyses that include all these risk factors suggest that the variations in life expectancy are largely explained by behavioral and metabolic risk factors, with socioeconomic and race/ethnicity factors no longer being statistically significant.2 This is an interesting finding, especially given how much we hear about rising income inequality.3 Indeed, in bivariate analysis, income is the strongest predictor of life expectancy.2 Given these somewhat incongruous results, I sought to compare the rise of income inequality, as well as racial inequality, over the same time frame as the rise in life expectancy inequality.

Methods

Available US Census data was used to chart income inequality by county over time, using the ratio of the 99th to the 1st percentile level similar to the method used by Dwyer-Lindgren et al.2 Similar charting was performed for racial inequality over time by analyzing the ratio of counties with the fewest percentage of minorities (examining black, Native, and Hispanic populations2) to counties with the highest percentage of minorities. All calculations were performed, and charts created, in Microsoft Excel (Microsoft Corp).

Results

Inequality in median household income between the top and bottom percentiles appeared to vary randomly over time (Figure, A). The ratio of the 99th percentile to the 1st percentile ranged from 4.12 at its highest in 1989 to 3.64 in 2010 at its lowest. Racial inequality between counties rose over time (Figure, B). The ratio of the counties with the fewest percentage of minorities and counties with highest percentage of minorities went from 4.38 in 1990 at its lowest to 5.48 in 2010 at its highest.

Discussion

This analysis showed that income inequality did not rise over time but rather varied over the years, suggesting that the rise in life expectancy inequality indeed is not solely related to the (purported) rise in income inequality. However, racial inequality has been rising over time—in fact, at a rate very similar to that of life expectancy inequality (Figure 3B in Dwyer-Lindgren et al2).

This is a visible demonstration that it is not simply behavioral and metabolic risk factors that play a role in longevity. There are likely multiple potential routes (including socioeconomic) to more equitable health outcomes, and many of these factors are integrally connected to each other. For example, those with higher income are much more likely to quit smoking than those with lower income.4 The converse is true as well, inextricably linking socioeconomic and behavioral risk factors.

Furthermore, income equality shows only a slice of the disparities in economic status; the gap in assets and wealth—in particular between white individuals compared with black and Hispanic individuals—is appallingly large.5 Combining this with the increasing residential segregation occurring based on wealth6—leading rich counties to become even richer—shows how difficult it is to unchain all these variables from each other.

The various factors associated with life expectancy inequality, then, are not necessarily confounders, but perhaps rather mediators between class and health. Likely, a multifaceted approach is needed in reducing inequalities in longevity and beyond.

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Article Information

Corresponding Author: Murtaza Akhter, MD, Department of Emergency Medicine, University of Arizona College of Medicine–Phoenix, Maricopa Integrated Health System, 425 N Fifth St, ABC-1 Bldg, Rm 319, Phoenix, AZ 85004-2157 (murtazaakhter@gmail.com).

Accepted for Publication: November 15, 2017.

Published Online: January 8, 2018. doi:10.1001/jamainternmed.2017.7893

Conflict of Interest Disclosures: Dr Akhter is supported by a career development grant from the Emergency Medicine Foundation for his basic science research in traumatic brain injury. No other conflicts are reported.

References
1.
Fogel  RW.  Catching up with the economy.  Am Econ Rev. 1999;89(1):1-21. doi:10.1257/aer.89.1.1Google ScholarCrossref
2.
Dwyer-Lindgren  L, Bertozzi-Villa  A, Stubbs  RW,  et al.  Inequalities in life expectancy among US counties, 1980 to 2014: temporal trends and key drivers.  JAMA Intern Med. 2017;177(7):1003-1011. doi:10.1001/jamainternmed.2017.0918PubMedGoogle ScholarCrossref
3.
Fitz  N. Economic inequality: it’s far worse than you think. Scientific American. https://www.scientificamerican.com/article/economic-inequality-it-s-far-worse-than-you-think/. Accessed June 3, 2017.
4.
Humphreys  K. Why the wealthy stopped smoking, but the poor didn’t. Washington Post. January 14, 2015. https://www.washingtonpost.com/news/wonk/wp/2015/01/14/why-the-wealthy-stopped-smoking-but-the-poor-didnt/?utm_term=.5a11734146d8. Accessed June 3, 2017.
5.
Williams  DR, Mohammed  SA, Leavell  J, Collins  C.  Race, socioeconomic status, and health: complexities, ongoing challenges, and research opportunities.  Ann N Y Acad Sci. 2010;1186:69-101. doi:10.1111/j.1749-6632.2009.05339.xPubMedGoogle ScholarCrossref
6.
Fischer  CS, Stockmayer  G, Stiles  J, Hout  M.  Distinguishing the geographic levels and social dimensions of U.S. metropolitan segregation, 1960-2000.  Demography. 2004;41(1):37-59.PubMedGoogle ScholarCrossref
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