About this item:

678 Views | 276 Downloads

Author Notes:

Correspondence: mkram02@sph.emory.edu

MRK conceived of the study, performed all GIS calculations, conducted statistical analyses, and lead the writing of the manuscript.

HLC, CDD-B, CRH, and LAW each provided substantial input in guiding the analysis, interpretation of results, and each contributed to the preparation of the manuscript.

All authors have read and approved the final manuscript.

We are grateful to the Office of Health Indicators for Planning, Department of Community Health, Georgia Division of Public Health for providing geocoded birth records for the Atlanta MSA.

The authors declare that they have no competing interests.

Subjects:

Research Funding:

This work was supported in part by a Health Resource and Service Administration Maternal and Child Health training grant [T03MC07651 to M.R.K.] and a National Institute of Health Reproductive, Perinatal, and Pediatric Health Training grant [T32 HD052460 to M.R.K.].

Do measures matter? Comparing surface-density-derived and census-tract-derived measures of racial residential segregation

Tools:

Journal Title:

International Journal of Health Geographics

Volume:

Volume 9

Publisher:

, Pages 29-29

Type of Work:

Article | Final Publisher PDF

Abstract:

Background Racial residential segregation is hypothesized to affect population health by systematically patterning health-relevant exposures and opportunities according to individuals' race or income. Growing interest into the association between residential segregation and health disparities demands more rigorous appraisal of commonly used measures of segregation. Most current studies rely on census tracts as approximations of the local residential environment when calculating segregation indices of either neighborhoods or metropolitan areas. Because census tracts are arbitrary in size and shape, reliance on this geographic scale limits understanding of place-health associations. More flexible, explicitly spatial derivations of traditional segregation indices have been proposed but have not been compared with tract-derived measures in the context of health disparities studies common to social epidemiology, health demography, or medical geography. We compared segregation measured with tract-derived as well as GIS surface-density-derived indices. Measures were compared by region and population size, and segregation measures were linked to birth record to estimate the difference in association between segregation and very preterm birth. Separate analyses focus on metropolitan segregation and on neighborhood segregation. Results Across 231 metropolitan areas, tract-derived and surface-density-derived segregation measures are highly correlated. However overall correlation obscures important differences by region and metropolitan size. In general the discrepancy between measure types is greatest for small metropolitan areas, declining with increasing population size. Discrepancies in measures are greatest in the South, and smallest in Western metropolitan areas. Choice of segregation index changed the magnitude of the measured association between segregation and very preterm birth. For example among black women, the risk ratio for very preterm birth in metropolitan areas changed from 2.12 to 1.68 for the effect of high versus low segregation when using surface-density-derived versus tract-derived segregation indices. Variation in effect size was smaller but still present in analyses of neighborhood racial composition and very preterm birth in Atlanta neighborhoods. Conclusion Census tract-derived measures of segregation are highly correlated with recently introduced spatial segregation measures, but the residual differences among measures are not uniform for all areas. Use of surface-density-derived measures provides researchers with tools to further explore the spatial relationships between segregation and health disparities.

Copyright information:

©2010 Kramer et al; licensee BioMed Central Ltd.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 2.0 Generic License (http://creativecommons.org/licenses/by/2.0/).

Creative Commons License

Export to EndNote