Income Inequality and All-Cause Mortality in the USA: A GIS Exploratory Study

Research Description: An inverse relationship exists between socioeconomic status and mortality. Gini coefficients are a commonly utilized measure of income inequality and have been linked with health outcomes, though these relationships are rarely examined geographically and spatially using spatial analysis software such as ArcGIS.

Study Design/Methods: We used all-cause mortality data at the county level (1999-2005) from the Centers for Disease Control and Prevention (CDC). Income inequality was measured using Gini coefficients, based on 2001 census tract data. Using ArcGIS, we joined U.S county boundaries with age-adjusted all-cause mortality and Gini coefficients for U.S. counties (1999-2005) by FIPS code. Both Gini and mortality data was then smoothed using weighted head-banging. Further analysis was performed using Local Moran's I statistic and Z-scores. STATA 10 was used to obtain Spearman's and Pearson's correlation coefficients.

Results: Based on our analysis, there was a significant cluster of elevated age-adjusted all-cause mortality in the Southern states in addition to elevated Gini coefficients in Southern California, the South and the Southwestern United States. We found a medium-strength positive correlation (r=0.35, p<0.001), between age-adjusted mortality rates (1999-2005) and Gini coefficient for all U.S. counties.

Conclusions/Implications: Income inequality, as measured by Gini coefficients, is significantly associated with age-adjusted all-cause mortality at the county level. There exists a statistically-significant relationship between the variation in income inequality at the county level and the variation in age-adjusted all-cause mortality rates. Policy interventions aimed at reducing the gap between rich and poor may also have beneficial public health benefits.