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Low income,community poverty and risk of end stage renal disease
Authors:Email author" target="_blank">Deidra?C?CrewsEmail author  Orlando?M?Gutiérrez  Stacey?A?Fedewa  Jean-Christophe?Luthi  David?Shoham  Suzanne?E?Judd  Neil?R?Powe  William?M?McClellan
Institution:1.Division of Nephrology, Department of Medicine,Johns Hopkins Medical Institutions,Baltimore,USA;2.Welch Center for Prevention, Epidemiology and Clinical Research,Johns Hopkins Medical Institutions,Baltimore,USA;3.Division of Nephrology, Department of Medicine,University of Alabama Birmingham,Birmingham,USA;4.Department of Epidemiology,University of Alabama Birmingham,Birmingham,USA;5.Department of Epidemiology,Emory University,Atlanta,USA;6.Institute of Social and Preventive Medicine,Centre Hospitalier Universitaire Vaudois and University of Lausanne,Lausanne,Switzerland;7.Department of Public Health Sciences,Loyola University Chicago,Maywood,USA;8.Department of Biostatistics,University of Alabama Birmingham,Birmingham,USA;9.Department of Medicine,San Francisco General Hospital,San Francisco,USA;10.Department of Medicine,University of California at San Francisco,San Francisco,USA;11.Department of Medicine,Emory University,Atlanta,USA
Abstract:

Background

The risk of end stage renal disease (ESRD) is increased among individuals with low income and in low income communities. However, few studies have examined the relation of both individual and community socioeconomic status (SES) with incident ESRD.

Methods

Among 23,314 U.S. adults in the population-based Reasons for Geographic and Racial Differences in Stroke study, we assessed participant differences across geospatially-linked categories of county poverty outlier poverty, extremely high poverty, very high poverty, high poverty, neither (reference), high affluence and outlier affluence]. Multivariable Cox proportional hazards models were used to examine associations of annual household income and geospatially-linked county poverty measures with incident ESRD, while accounting for death as a competing event using the Fine and Gray method.

Results

There were 158 ESRD cases during follow-up. Incident ESRD rates were 178.8 per 100,000 person-years (105 py) in high poverty outlier counties and were 76.3 /105 py in affluent outlier counties, p trend?=?0.06. In unadjusted competing risk models, persons residing in high poverty outlier counties had higher incidence of ESRD (which was not statistically significant) when compared to those persons residing in counties with neither high poverty nor affluence hazard ratio (HR) 1.54, 95% Confidence Interval (CI) 0.75-3.20]. This association was markedly attenuated following adjustment for socio-demographic factors (age, sex, race, education, and income); HR 0.96, 95% CI 0.46-2.00. However, in the same adjusted model, income was independently associated with risk of ESRD HR 3.75, 95% CI 1.62-8.64, comparing the?<?$20,000 income group to the?>?$75,000 group]. There were no statistically significant associations of county measures of poverty with incident ESRD, and no evidence of effect modification.

Conclusions

In contrast to annual family income, geospatially-linked measures of county poverty have little relation with risk of ESRD. Efforts to mitigate socioeconomic disparities in kidney disease may be best appropriated at the individual level.
Keywords:
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