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Assessing community-level exposure to social vulnerability and isolation: spatial patterning and urban-rural differences

Journal of Exposure Science & Environmental Epidemiology volume 33, pages 198–206 (2023)Cite this article

Abstract

Background

Environmental health disparity research involves the use of metrics to assess exposure to community-level vulnerabilities or inequities. While numerous vulnerability indices have been developed, there is no agreement on standardization or appropriate use, they have largely been applied in urban areas, and their interpretation and utility likely vary across different geographies.

Objective

We evaluated the spatial distribution, variability, and relationships among different metrics of social vulnerability and isolation across urban and rural settings to inform interpretation and selection of metrics for environmental disparity research.

Methods

For all census tracts in North Carolina, we conducted a principal components analysis using 23 socioeconomic/demographic variables from the 2010 United States Census and American Community Survey. We calculated or obtained the neighborhood deprivation index (NDI), residential racial isolation index (RI), educational isolation index (EI), Gini coefficient, and social vulnerability index (SVI). Statistical analyses included Moran’s I for spatial clustering, t-tests for urban-rural differences, Pearson correlation coefficients, and changes in ranking of tracts across metrics.

Results

Social vulnerability metrics exhibited clear spatial patterning (Moran’s I ≥ 0.30, p < 0.01). Greater educational isolation and more intense neighborhood deprivation was observed in rural areas and greater racial isolation in urban areas. Single-domain metrics were not highly correlated with each other (rho ≤ 0.36), while composite metrics (i.e., NDI, SVI, principal components analysis) were highly correlated (rho > 0.80). Composite metrics were more highly correlated with the racial isolation metric in urban (rho: 0.54–0.64) versus rural tracts (rho: 0.36–0.48). Census tract rankings changed considerably based on which metric was being applied.

Significance

High correlations between composite metrics within urban and rural tracts suggests they could be used interchangeably; single domain metrics cannot. Composite metrics capture different facets of vulnerabilities in urban and rural settings, and these complexities should be examined by researchers applying metrics to areas of diverse urban and rural forms.

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Fig. 1: Urban and rural areas and geographic regions in North Carolina.
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Data availability

All data analyzed in this paper are publicly available with data sources provided in the reference list.

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Acknowledgements

The authors thank Claire Osgood for data management expertise.

Funding

Research reported in this publication was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD012769. F. Macalintal contributed to this work as part of a summer internship supported by NIEHS training grant R25ES029052. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. In addition, this publication was developed under Assistance Agreement No. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. This work was supported in part by a Pilot Grant from Yale Cancer Center.

Author information

Authors and Affiliations

  1. Yale School of Public Health, Department of Environmental Health Sciences, New Haven, CT, USA

    Nicole C. Deziel & Franchesca Macalintal

  2. Yale School of Public Health, Department of Biostatistics, New Haven, CT, USA

    Joshua L. Warren

  3. Duke University, Global Health Institute, School of Medicine, Durham, NC, USA

    Mercedes A. Bravo

  4. Fordham University, Fordham College at Lincoln Center, New York, NY, USA

    Franchesca Macalintal

  5. Rice University, Department of Sociology, Houston, TX, USA

    Rachel T. Kimbro

  6. Yale School of the Environment, New Haven, CT, USA

    Michelle L. Bell

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  1. Nicole C. Deziel
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Contributions

NCD: Conceptualization, methodology, acquired data, led writing of original draft and revisions. JW: Methodology, Formal analysis, Visualization, review & editing. MAB: Methodology, data interpretation, visualization, review & editing. FM: Contributed to original draft, review of final version. RTK: Conceptualization, writing original draft, review & editing. MB: Project conception, data interpretation, review & editing, funding acquisition.

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Correspondence to Nicole C. Deziel.

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Deziel, N.C., Warren, J.L., Bravo, M.A. et al. Assessing community-level exposure to social vulnerability and isolation: spatial patterning and urban-rural differences. J Expo Sci Environ Epidemiol 33, 198–206 (2023). https://doi.org/10.1038/s41370-022-00435-8

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