A Multilevel Analysis of the Relationship Between Institutional and Individual Racial Discrimination and Health Status

Training Program in Identity, Self, Role, and Mental Health, Department of Sociology, Indiana University, Ballantine Hall 744, 1020 E Kirkwood Avenue, Bloomington, IN 47405-7103, USA.
American Journal of Public Health (Impact Factor: 4.55). 05/2002; 92(4):615-23. DOI: 10.2105/AJPH.92.4.615
Source: PubMed


This study examined whether individual (self-perceived) and institutional (segregation and redlining) racial discrimination was associated with poor health status among members of an ethnic group.
Adult respondents (n = 1503) in the cross-sectional Chinese American Psychiatric Epidemiologic Study were geocoded to the 1990 census and the 1995 Home Mortgage Disclosure Act database. Hierarchical linear modeling assessed the relationship between discrimination and scores on the Medical Outcomes Study Short-Form 36 and revised Symptom Checklist 90 health status measures.
Individual and institutional measures of racial discrimination were associated with health status after control for acculturation, sex, age, social support, income, health insurance, employment status, education, neighborhood poverty, and housing value.
The data support the hypothesis that discrimination at multiple levels influences the health of minority group members.

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    • "However, the ORE is also well established to occur where strong socio-economic differences do not exist. Asians, in the USA and Australia, do not differ noticeably in socio-economic status from Caucasians (Gee, 2002; Ip, 2001; Ip, Wu, & Inglis, 1998; LaVeist, 2005) and are commonly found in high status professions (e.g., doctors, Australian Bureau of Statistics, 2008). In the only previous test of motivation instructions for Caucasian and Asian faces (and testing only US Caucasians as participants ), Tullis et al. (2014) found that Hugenberg's motivation instructions had no effect at all on the ORE, even in participants with higher levels of experience with Asians. "
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    ABSTRACT: Competing approaches to the other-race effect (ORE) see its primary cause as either a lack of motivation to individuate social outgroup members, or a lack of perceptual experience with other-race faces. Here, we argue that the evidence supporting the social-motivational approach derives from a particular cultural setting: a high socio-economic status group (typically US Whites) looking at the faces of a lower status group (US Blacks) with whom observers typically have at least moderate perceptual experience. In contrast, we test motivation-to-individuate instructions across five studies covering an extremely wide range of perceptual experience, in a cultural setting of more equal socio-economic status, namely Asian and Caucasian participants (N=480) tested on Asian and Caucasian faces. We find no social-motivational component at all to the ORE, specifically: no reduction in the ORE with motivation instructions, including for novel images of the faces, and at all experience levels; no increase in correlation between own- and other-race face recognition, implying no increase in shared processes; and greater (not the predicted less) effort applied to distinguishing other-race faces than own-race faces under normal ("no instructions") conditions. Instead, the ORE was predicted by level of contact with the other-race. Our results reject both pure social-motivational theories and also the recent Categorization-Individuation model of Hugenberg, Young, Bernstein, and Sacco (2010). We propose a new dual-route approach to the ORE, in which there are two causes of the ORE-lack of motivation, and lack of experience-that contribute differently across varying world locations and cultural settings. Copyright © 2015 Elsevier B.V. All rights reserved.
    Cognition 11/2015; 144:91-115. DOI:10.1016/j.cognition.2015.07.011 · 3.63 Impact Factor
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    • "Extant research suggests that institutionalized forms of racial bias are implicated in the creation of harmful ecological environments (Gee 2002). Mendez and colleagues (2012), for instance, finds that economically disadvantaged pregnant women living in Philadelphia neighborhoods subjected to racialized loan denial penalties are more likely to rate the quality of their neighborhood "
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    ABSTRACT: Systems of race, racism, and racial inequality are of consequence to the prevalence of illness and to racial disparities in illness prevalence. Yet a histo-ricized understanding of how racially segregated neighborhoods are created from these systems of racial oppression is absent in neighborhood effect studies documenting the harmful effects of living in racial ghettos. Drawing on insights from the neighborhood change, place stratification, and housing discrimination literatures, this essay illustrates the ways that structural manifestations of racial oppression are implicated in the illnesses experienced via the neighborhood arrangements that arise out of institutionalized forms of racism. These illustrations are demonstrated by focusing on a system of unequal relationships created by racial oppression on the part of the mortgage lending industry. This essay explicates how the unequal distribution of resources by gatekeepers of the mortgage lending industry is implicated in the poor health outcomes of racial minorities, the deteriorated conditions of the environments in which racial minorities spend their lives, and the segregation of ethnoracially marginalized people into socially, economically, and materially deprived communities.
    Race and Real Estate, Edited by Adrienne Brown; Valerie Smith, 05/2015: chapter 6: pages 87-105; Oxford University Press., ISBN: 9780199977277
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    • "There is only limited work employing MLM in house prices research, while most applications are in education (Harris, Johnston, & Burgess, 2007; Goldstein, 1999; Haurin & Brasington, 1996; Aitkin & Longford, 1986); public health (Gee 2008; Diez-roux 2000) and epidemiology (Merlo et al., 2006; Congdon, 2003; Bryk & Raudenbush, 1992). Jones (1991) is the first paper to employ a multilevel approach to house price research. "
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    DESCRIPTION: Two advanced modelling approaches, Multi-Level Models and Artificial Neural Networks are employed to model house prices. These approaches and the standard Hedonic Price Model are compared in terms of predictive accuracy, capability to capture location information, and their explanatory power. These models are applied to 2001-2013 house prices in the Greater Bristol area, using secondary data from the Land Registry, the Population Census and Neighbourhood Statistics so that these models could be applied nationally. The results indicate that MLM offers good predictive accuracy with high explanatory power, especially if neighbourhood effects are explored at multiple spatial scales.
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