We recently used ecologic variables to assess relationships

We recently used ecologic variables to assess relationships among health outcomes and income, income inequality, and residential segregation in Black and White end-stage renal disease (ESRD) patients. Black patients who lived in areas characterized by segregation and lower income had higher mortality (Kimmel, Fwu, & Eggers, 2013). The mortality disadvantage for Blacks in the US elderly population is substantial. The disparity varies with age, decreasing from a mortality disadvantage of 49% in those 65–69, to 12% in those 80–84. The racial mortality disadvantage reverses after age 85. The reason for crossover at 85 is unknown, but the finding is well-documented (Liu & Witten, 1995; Sautter et al., 2012). Therefore, the etiology of Black disadvantage is undoubtedly complex, reflecting many confounding factors. Poverty is an important factor underlying US racial mortality differences, given the strong link between higher mortality and adverse economic conditions (Isaacs & Schroeder, 2004) and the well-documented differences in poverty rates across racial groups (DeNavas-Walt, Proctor, & Smith, 2013). Poverty and poor health can reinforce each other, a notion increasingly recognized as an impediment to economic advances in both developed and low-income nations (Mirvis, Chang, & Cosby, 2008). Residential or ecologic characteristics such as neighborhood median household income and racial segregation may also contribute to racial mortality disparities (Kimmel et al., 2013; Nuru-Jeter & LaVeist, 2011). Residence in a socioeconomically disadvantaged ion channels is associated with poorer health and higher mortality (Ludwig et al., 2011; Nuru-Jeter & LaVeist, 2011). Poor neighborhoods can provide unhealthy environments and offer residents little chance to engage in healthy behaviors (Nuru-Jeter & LaVeist, 2011). Residential segregation perpetuates poor housing, unhealthy neighborhood environments (Kramer & Hogue, 2009; Nuru-Jeter & LaVeist, 2011) and limited health care access (Rodriguez et al., 2007). Typical analytic approaches evaluating SES factors in US studies include linking large databases, such as Medicare enrollment files, with Census level SES measures. Analyses using area level approaches usually show modest associations of SES and outcomes, but are subject to ecological biases (Kimmel et al., 2013; Rodriguez et al., 2007). Relying solely on area-wide poverty or income measures to account for individual variation in health outcomes may result in misleading or inadequate assessment of income effects on health (Hanley & Morgan, 2008). Individual level information regarding income and wealth as socioeconomic indicators is largely missing from US administrative health registries (Isaacs & Schroeder, 2004). Direct individual level income or poverty measures are much preferred for such analyses since even race-specific ecologic analyses may subject the evaluation of certain characteristics, such as income, to misclassification (Hanley & Morgan, 2008). Medicare data, however, include both individual level disability and poverty measures, not widely used in outcome analyses (Lovald et al., 2013). First, Social Security offers Medicare coverage to those unable to work because of medically determined physical or mental impairment before age 65. This lack of participation in the legal workforce, acknowledged by disability status, in combination with relatively low levels of monetary reimbursement, puts even recipients of disability benefits at economic disadvantage. Compared to elderly beneficiaries, disabled Medicare beneficiaries are much more likely to be of a minority group (Iezzoni, 2006). Disability also is associated with increased mortality (Lubitz & Pine, 1986). Therefore, Medicare disability eligibility is a marker of economic and health disadvantage during beneficiaries’ early lives arteries may have enduring effects, which could contribute to racial mortality disparities.