Authors: Guangran Deng*, University of Florida, Liang Mao, University of Florida
Topics: Medical and Health Geography, Geography and Urban Health, Spatial Analysis & Modeling
Keywords: Spatial age segregation; Health status; Elderly people; Multi-level analysis; GWR; SMART dataset
Session Type: Paper
Start / End Time: 5:20 PM / 7:00 PM
Room: Studio 4, Marriott, 2nd Floor
There have been mixed findings on whether residential (spatial) age segregation offers better or worse health to older adults. These inconsistencies can possibly be attributed to two limitations in previous studies. First, many studies used statistical age composition to indicate the residential age segregation in a community, but this statistic does not consider spatial arrangement of residents. Second, many national scale studies focused on averaged (or global) associations between age segregation and senior health, and assumed these associations represent the situation in every part of a country. Little attention has been paid to local patterns of such association at different places. To address these limitations, we calculated a spatially explicit age segregation index for each US city to replace conventional age composition index. We then examined global and local associations between spatial age segregation and self-rated health of older adults across US cities. Our multi-level global analysis suggested that older adults living in age-segregated metropolitan areas experienced more mentally unhealthy days. On the other hand, the local regression analysis identified local clusters of positive associations between the age segregation and the elderly’s overall health status in western and southern metropolitan areas, but no significant associations in Midwestern and northeastern cities. In short, we advocated the use of spatially explicit approach to deepen understandings on age segregation and senior health. The new age segregation metric and new analytic approach can offer new insights into ongoing debate regarding aging in place.