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Death by Segregation: Does the Dimension of Racial Segregation Matter?

Yang TC, Matthews SA - PLoS ONE (2015)

Bottom Line: Race/ethnic segregation was found to contribute to the geographic mortality disparities.Specifically, white/black segregation is positively related to mortality, whereas the segregation between whites and non-black minorities is negatively associated with mortality.Spatial filtering approaches also identified six unique spatial patterns that significantly affect the spatial distribution of mortality.

View Article: PubMed Central - PubMed

Affiliation: Department of Sociology, Center for Social and Demographic Analysis, University at Albany, State University of New York, Albany, NY, United States of America.

ABSTRACT
The county-level geographic mortality differentials have persisted in the past four decades in the United States (US). Though several socioeconomic factors (e.g., inequality) partially explain this phenomenon, the role of race/ethnic segregation, in general, and the different dimensions of segregation, more specifically, has been underexplored. Focusing on all-cause age-sex standardized US county-level mortality (2004-2008), this study has two substantive goals: (1) to understand whether segregation is a determinant of mortality and if yes, how the relationship between segregation and mortality varies by racial/ethnic dyads (e.g., white/black), and (2) to explore whether different dimensions of segregation (i.e., evenness, exposure, concentration, centralization, and clustering) are associated with mortality. A third goal is methodological: to assess whether spatial autocorrelation influences our understanding of the associations between the dimensions of segregation and mortality. Race/ethnic segregation was found to contribute to the geographic mortality disparities. Moreover, the relationship with mortality differed by both race/ethnic group and the dimension of segregation. Specifically, white/black segregation is positively related to mortality, whereas the segregation between whites and non-black minorities is negatively associated with mortality. Among the five dimensions of segregation, evenness and exposure are more strongly related to mortality than other dimensions. Spatial filtering approaches also identified six unique spatial patterns that significantly affect the spatial distribution of mortality. These patterns offer possible insights that help identify omitted variables related to the persistent patterning of mortality in the US.

No MeSH data available.


Related in: MedlinePlus

Spatial Patterns of the Two Additional Eigenvectors and Moran’s I Values (maps are created by the authors and the shapefiles are publicly available online).
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pone.0138489.g003: Spatial Patterns of the Two Additional Eigenvectors and Moran’s I Values (maps are created by the authors and the shapefiles are publicly available online).

Mentions: Third, the total number of eigenvectors found in each model is comparable across segregation dimensions and, among them, six were commonly shared by the five segregation dimensions, i.e., eigenvectors 15, 19, 1, 6, 21, and 17. Comparing with the findings in Table 2, two additional eigenvectors, 21 and 17, were identified and they were shown in Fig 3. Again, both eigenvectors have spatial patterns that were different from those in Fig 2 and their associations with mortality were positive (see Tables 3 and 4). As seen in Figs 2 and 3, the Moran’s I values of the six most important eigenvectors were all positive, indicating that the high (low) component values of these eigenvectors are geographically close to one another. We would like to reiterate that the six eigenvectors capture the spatial processes that are not associated with the independent variables in the models but they contribute to the observed spatial pattern of mortality in the US.


Death by Segregation: Does the Dimension of Racial Segregation Matter?

Yang TC, Matthews SA - PLoS ONE (2015)

Spatial Patterns of the Two Additional Eigenvectors and Moran’s I Values (maps are created by the authors and the shapefiles are publicly available online).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4580431&req=5

pone.0138489.g003: Spatial Patterns of the Two Additional Eigenvectors and Moran’s I Values (maps are created by the authors and the shapefiles are publicly available online).
Mentions: Third, the total number of eigenvectors found in each model is comparable across segregation dimensions and, among them, six were commonly shared by the five segregation dimensions, i.e., eigenvectors 15, 19, 1, 6, 21, and 17. Comparing with the findings in Table 2, two additional eigenvectors, 21 and 17, were identified and they were shown in Fig 3. Again, both eigenvectors have spatial patterns that were different from those in Fig 2 and their associations with mortality were positive (see Tables 3 and 4). As seen in Figs 2 and 3, the Moran’s I values of the six most important eigenvectors were all positive, indicating that the high (low) component values of these eigenvectors are geographically close to one another. We would like to reiterate that the six eigenvectors capture the spatial processes that are not associated with the independent variables in the models but they contribute to the observed spatial pattern of mortality in the US.

Bottom Line: Race/ethnic segregation was found to contribute to the geographic mortality disparities.Specifically, white/black segregation is positively related to mortality, whereas the segregation between whites and non-black minorities is negatively associated with mortality.Spatial filtering approaches also identified six unique spatial patterns that significantly affect the spatial distribution of mortality.

View Article: PubMed Central - PubMed

Affiliation: Department of Sociology, Center for Social and Demographic Analysis, University at Albany, State University of New York, Albany, NY, United States of America.

ABSTRACT
The county-level geographic mortality differentials have persisted in the past four decades in the United States (US). Though several socioeconomic factors (e.g., inequality) partially explain this phenomenon, the role of race/ethnic segregation, in general, and the different dimensions of segregation, more specifically, has been underexplored. Focusing on all-cause age-sex standardized US county-level mortality (2004-2008), this study has two substantive goals: (1) to understand whether segregation is a determinant of mortality and if yes, how the relationship between segregation and mortality varies by racial/ethnic dyads (e.g., white/black), and (2) to explore whether different dimensions of segregation (i.e., evenness, exposure, concentration, centralization, and clustering) are associated with mortality. A third goal is methodological: to assess whether spatial autocorrelation influences our understanding of the associations between the dimensions of segregation and mortality. Race/ethnic segregation was found to contribute to the geographic mortality disparities. Moreover, the relationship with mortality differed by both race/ethnic group and the dimension of segregation. Specifically, white/black segregation is positively related to mortality, whereas the segregation between whites and non-black minorities is negatively associated with mortality. Among the five dimensions of segregation, evenness and exposure are more strongly related to mortality than other dimensions. Spatial filtering approaches also identified six unique spatial patterns that significantly affect the spatial distribution of mortality. These patterns offer possible insights that help identify omitted variables related to the persistent patterning of mortality in the US.

No MeSH data available.


Related in: MedlinePlus