Quantification of segregation in southeastern Wisconsin as a marker for economic disadvantage
Tuesday, November 3, 2015
The degree of racial segregation in a region is an important environmental variable and influences a wide variety of different public health and social justice issues such as housing equity, economic prosperity, access to healthcare, and disease outcomes and disparities. Furthermore, it is now possible to take advantage of high resolution spatial data and advancements in computing power and analytical approaches to examine small area environmental factors and health outcomes. However, many of the existing segregation metrics were designed to summarize segregation patterns over larger regions, such as metropolitan statistical areas; measures derived at this scale are of limited utility for examining local neighborhood health effects. To address these concerns, this project alters previous segregation indices and defines novel ones in order to quantify segregation at the local level, as compared to the greater region of interest. The indices are tested for their ability to serve as predictors of economic disadvantage in each neighborhood for the southeastern Wisconsin area. Both ordinary least squares (R-squared: 0.7363, p < 0.001) and spatial regression (R-squared: 0.7781, p < 0.001) analyses demonstrate that these indices are significantly related to a measure of economic disadvantage. This finding indicates that these measures of local segregation likely have predictive power for public health research questions. Proper local quantification of residential racial segregation in public health research will ultimately enable the targeting of local public health interventions.
Environmental health sciences
Public health or related public policy
Public health or related research
Social and behavioral sciences
Demonstrate how different segregation metrics quantify racial disparities
Identify regions which are economically disadvantaged
Analyze which segregation metrics are good predictors of poor economic status
Keyword(s): Biostatistics, Underserved Populations
Presenting author's disclosure statement:
Qualified on the content I am responsible for because: I am a doctoral student who is currently using segregation metrics for analysis of disparities in cancer incidence and mortality. My undergraduate research background was in quantitative methods, and I was exposed to a variety of statistical and mathematical analytic techniques in that time period.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines,
and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed
in my presentation.