Measuring intersectional segregation with race and age in the context of health

Keywords: County health rankings, race-ethnicity, age, intersectional segregation, dissimilarity indices
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 11:20 AM (Eastern Time (US & Canada)) - 2/25/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 56

Abstract

Connections between segregation and health outcomes are well conceptualized and investigated in the literature. Health policy formulations also incorporate segregation measures to assess the health status of places. An example is the County Health Rankings & Roadmaps program (www.countyhealthrankings.org), which include racial segregation measures to produces county health rankings. However, in measuring segregation, most of these programs and existing studies consider a single population attribute, which most commonly has been either race-ethnicity, gender, or economic status. If multiple attributes need to be considered, how can existing segregation measures be adapted to accommodate such a scenario and how should corresponding results be interpreted? The literature is relatively sparse on the topic of multi-attribute segregation. Given the growing non-white and the graying trend of the US population, measuring segregation at the intersection of race and age is appropriate but may be challenging. In this presentation, we report key results from our exploratory study considering both race and age in the segregation indices. Data used are the 2010 decennial census data at the census tract level. We evaluate the performance and effectiveness of the traditional two-group, multi-group dissimilarity indices and their spatial versions. In connection, we highlight the inherent challenges when interpreting segregation results derived using multiple population attributes. We also explore the utility of methods to analyze contingency tables in interpreting multi-attribute segregation results. Our results offer implications of extending the segregation measurement exercise to more than two population attributes or dimensions.