Document Detail


Rising U.S. income inequality, gender and individual self-rated health, 1972-2004.
MedLine Citation:
PMID:  19733951     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The effect of income inequality on health has been a contested topic among social scientists. Most previous research is based on cross-sectional comparisons rather than temporal comparisons. Using data from the General Social Survey and the U.S. Census Bureau, this study examines how rising income inequality affects individual self-rated health in the U.S. from 1972 to 2004. Data are analyzed using hierarchical generalized linear models. The findings suggest a significant association between income inequality and individual self-rated health. The dramatic increase in income inequality from 1972 to 2004 increases the odds of worse self-rated health by 9.4 percent. These findings hold for three measures of income inequality: the Gini coefficient, the Atkinson Index, and the Theil entropy index. Results also suggest that overall income inequality and gender-specific income inequality harm men's, but not women's, self-rated health. These findings also hold for the three measures of income inequality. These findings suggest that inattention to gender composition may explain apparent discrepancies across previous studies.
Authors:
Hui Zheng
Publication Detail:
Type:  Journal Article     Date:  2009-09-06
Journal Detail:
Title:  Social science & medicine (1982)     Volume:  69     ISSN:  1873-5347     ISO Abbreviation:  Soc Sci Med     Publication Date:  2009 Nov 
Date Detail:
Created Date:  2009-10-12     Completed Date:  2009-12-03     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8303205     Medline TA:  Soc Sci Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  1333-42     Citation Subset:  IM    
Affiliation:
Department of Sociology, Duke University, United States. huizheng@soc.duke.edu
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MeSH Terms
Descriptor/Qualifier:
Female
Health Status*
Health Status Disparities
Humans
Income / statistics & numerical data,  trends*
Linear Models
Logistic Models
Male
Sex Factors
Socioeconomic Factors
United States

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