TY - JOUR
T1 - Using quantile regression to examine the effects of inequality across the mortality distribution in the U.S. counties
AU - Yang, Tse Chuan
AU - Chen, Vivian Yi Ju
AU - Shoff, Carla
AU - Matthews, Stephen A.
N1 - Funding Information:
This work was partially supported by internal funds from the Social Science Research Institute at Penn State. Additional support has been provided by the Geographic Information Analysis Core at Penn State's Population Research Institute, which receives core funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development ( R24-HD41025 ).
PY - 2012/6
Y1 - 2012/6
N2 - The U.S. has experienced a resurgence of income inequality in the past decades. The evidence regarding the mortality implications of this phenomenon has been mixed. This study employs a rarely used method in mortality research, quantile regression (QR), to provide insight into the ongoing debate of whether income inequality is a determinant of mortality and to investigate the varying relationship between inequality and mortality throughout the mortality distribution. Analyzing a U.S. dataset where the five-year (1998-2002) average mortality rates were combined with other county-level covariates, we found that the association between inequality and mortality was not constant throughout the mortality distribution and the impact of inequality on mortality steadily increased until the 80th percentile. When accounting for all potential confounders, inequality was significantly and positively related to mortality; however, this inequality-mortality relationship did not hold across the mortality distribution. A series of Wald tests confirmed this varying inequality-mortality relationship, especially between the lower and upper tails. The large variation in the estimated coefficients of the Gini index suggested that inequality had the greatest influence on those counties with a mortality rate of roughly 9.95 deaths per 1000 population (80th percentile) compared to any other counties. Furthermore, our results suggest that the traditional analytic methods that focus on mean or median value of the dependent variable can be, at most, applied to a narrow 20 percent of observations. This study demonstrates the value of QR. Our findings provide some insight as to why the existing evidence for the inequality-mortality relationship is mixed and suggest that analytical issues may play a role in clarifying whether inequality is a robust determinant of population health.
AB - The U.S. has experienced a resurgence of income inequality in the past decades. The evidence regarding the mortality implications of this phenomenon has been mixed. This study employs a rarely used method in mortality research, quantile regression (QR), to provide insight into the ongoing debate of whether income inequality is a determinant of mortality and to investigate the varying relationship between inequality and mortality throughout the mortality distribution. Analyzing a U.S. dataset where the five-year (1998-2002) average mortality rates were combined with other county-level covariates, we found that the association between inequality and mortality was not constant throughout the mortality distribution and the impact of inequality on mortality steadily increased until the 80th percentile. When accounting for all potential confounders, inequality was significantly and positively related to mortality; however, this inequality-mortality relationship did not hold across the mortality distribution. A series of Wald tests confirmed this varying inequality-mortality relationship, especially between the lower and upper tails. The large variation in the estimated coefficients of the Gini index suggested that inequality had the greatest influence on those counties with a mortality rate of roughly 9.95 deaths per 1000 population (80th percentile) compared to any other counties. Furthermore, our results suggest that the traditional analytic methods that focus on mean or median value of the dependent variable can be, at most, applied to a narrow 20 percent of observations. This study demonstrates the value of QR. Our findings provide some insight as to why the existing evidence for the inequality-mortality relationship is mixed and suggest that analytical issues may play a role in clarifying whether inequality is a robust determinant of population health.
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U2 - 10.1016/j.socscimed.2012.02.029
DO - 10.1016/j.socscimed.2012.02.029
M3 - Article
C2 - 22497847
AN - SCOPUS:84860485571
SN - 0277-9536
VL - 74
SP - 1900
EP - 1910
JO - Social Science and Medicine
JF - Social Science and Medicine
IS - 12
ER -