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LETTER |
Correspondence: Requests for reprints should be sent to Andreas Muller, PhD, Department of Health Policy and Management, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 W Markham, #820, Little Rock, AR 72205 (e-mail: amuller{at}uams.edu).
Lynch et al.s recent analysis of the last 6 US census periods suggests that the relationship between income inequality and mortality is less enduring than was previously thought.1 Statistically significant correlations between state household Gini coefficients and age-adjusted death rates were reported only for the years 1979, 1989, and 1999.
The graphs presented by Lynch et al. omit the population at risk. The authors treat the state as the unit of analysis, which ignores the substantial variation in state populations. From the perspective of population exposure, state-based correlations will favor less populous states. Weighting the correlations by state population will correct this bias and provide an alternate perspective. A population-weighted analysis is also reasonable because income inequality is considered to be a risk factor for individual health.2,3
In Figure 1
, state-based correlations between state household Gini coefficients and age-adjusted death rates are compared with population-weighted correlations; the year 2000 is used as the standard.4,5 Each state is represented by a circle, and the size of the circle indicates the size of the states population. The filled circles represent the 10 most populous states (in descending order, California, Texas, New York, Florida, Illinois, Pennsylvania, Ohio, Michigan, New Jersey, and Georgia).
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As observed by Lynch et al., the population-based correlations indicate that only the year 1989 was exceptional, showing a strong correlation between income inequality and mortality in US states. The lack of correlation for the year 1999 is certainly unexpected, as US income inequality did not decline in the 1990s.8
The inconsistent results found in the population-based analysis suggest that the association between income inequality and mortality is less pervasive than reported by Lynch et al. The temporal volatility of the population-based correlations probably reflects the work of known confounders such as educational attainment, racial composition, population residence, and household income.9,10 Statistically controlling for such factors might clarify the association between income inequality and mortality in the United States.
References
1. Lynch J, Harper S, Kaplan GA, Davey Smith G. Association between income inequality and mortality among US states: the importance of time period and source of income data. Am J Public Health. 2005;95: 14241430.
2. Wilkinson RG. Income distribution and life expectancy. BMJ. 1992;304:165168.
3. Pickett KE, Kelly S, Brunner E, Lobstein T, Wilkinson RG. Wider income gaps, wider waistbands? An ecological study of obesity and income inequality. J Epidemiol Community Health. 2005;59:670674.
4. US Census Bureau. Income. Table S4. Gini ratios by state: 1969, 1979, 1989, 1999. Available at: http://www.census.gov/hhes/www/income/histinc/state/state4.html. Accessed January 12, 2006.
5. CDC Wonder. Compressed mortality file. Available at: http://wonder.cdc.gov/mortSQL.html. Accessed September 23, 2005.
6. Deaton A. Health, inequality, and economic development. J Econ Lit. 2003;41:113158.[CrossRef][Web of Science]
7. Jones AF, Weinberg DH. The Changing Shape of the Nations Income Distribution, 19471998. Washington, DC: US Census Bureau; 2000. Current Population Reports, P60204. Also available at: http://www.census.gov/prod/2000pubs/p60-204.pdf. Accessed January 12, 2006.
8. Rodgers A, Lazere E. Income inequality in the District of Columbia is wider than in any major US. city. Available at: http://www.dcfpi.org/7-22-04pov.pdf. Accessed January 12, 2006.
9. Deaton A, Lubotsky D. Mortality, inequality and race in American cities and states. Soc Sci Med. 2003; 56:11391153.[CrossRef][Web of Science][Medline]
10. Muller A. Education, income inequality, and mortality: a multiple regression analysis. BMJ. 2002;324: 2325.
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