Abstract Disparities in cancer survival continue to persist by race/ethnicity, socioeconomic status (SES) and nativity. Neighborhood factors are critical pathways through which disparities are shaped and perpetuated. However, the place and health literature has shown mixed results and neighborhood-based interventions have had limited success. To advance research on neighborhoods and health, new approaches need to reflect the fact that neighborhood effects on health arise through complex and dynamic processes, underscoring the importance of studying not only the individual effects of neighborhood features on health outcomes, but also their synergistic effects. A promising approach is to encompass multiple dimensions within a single classification system, archetypes, that capture meaningful distinctions across neighborhoods. The archetype approach is an efficient way to integrate multiple neighborhood characteristics and has the potential to identify improved opportunities for intervening and reducing health disparities. Leveraging a comprehensive database of small-area level data on neighborhood social and built environments (including socioeconomic status, racial/ethnic composition, immigration/acculturation factors, urban/rural status, population density, street connectivity, walkable destinations, food environment, recreational opportunities, traffic density, and green space), we applied latent class analysis (LCA) to develop neighborhood archetypes for 2000 and 2010 California block groups and census tracts. We selected the best fitting LCA model based on goodness of fit statistics (e.g., Akaike information criterion, Bayesian information criterion, etc.). We will apply the archetypes to California Cancer Registry data to demonstrate the extent to which cancer mortality varies by place, and whether these associations will vary further by race/ethnicity and nativity. We identified a 6 class archetype that characterizes the census tracts in California. The classes distinguished neighborhoods into archetypes based on the following characteristics: (1) Mid-level SES, Highest percent of African American, Asian American/Native Hawaiian/Pacific Islander, and Fewer businesses; (2) Higher percent of single households, female headed households, More businesses and recreational facilities and Higher traffic density; (3) Low SES, Highest percent of Hispanic and Foreign-born, Higher percent of African Americans, Urban/high population density, and Higher street connectivity; (4) Highest percent of White, Lowest percent of minority, More working at home, Less commuting (length, public transportation) and More rural; (5): Highest SES, Highest percent of middle-age, More owner occupied housing and Lower street connectivity; and (6) Lower SES, Higher percent of Hispanic, Lower percent of foreign-born, Lower traffic density. In California, 11% of 2000 census tracts are class 1, 20% are class 2, 23% class 3, 15% class 4, 18% class 5 and 13% class 6. The archetype approach will yield insights into how neighborhood characteristics work synergistically with each other and interact with individual characteristics to influence cancer mortality. This research has the potential to contribute to a broader, more fundamental understanding of place effects on health, with applications to both population health and health disparities research. Citation Format: Salma Shariff-Marco, Juan Yang, Margaret Weden, Andrew Hertz, David Nelson, Scarlett L. Gomez. Neighborhood archetypes: An innovative tool for understanding how place impacts disparities in cancer mortality. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr A72.
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Dec 30, 2016
S Shekhar + 4
Open Access
