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Racial and ethnic disparities in hospital care resulting from air pollution in excess of federal standards

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  • Hackbarth, Andrew D.
  • Romley, John A.
  • Goldman, Dana P.

Abstract

This study investigates racial and ethnic disparities in hospital admission and emergency room visit rates resulting from exposure to ozone and fine particulate matter levels in excess of federal standards (“excess attributable risk”). We generate zip code-level ambient pollution exposures and hospital event rates using state datasets, and use pollution impact estimates in the epidemiological literature to calculate excess attributable risk for racial/ethnic groups in California over 2005–2007. We find that black residents experienced roughly 2.5 times the excess attributable risk of white residents. Hispanic residents were exposed to the highest levels of pollution, but experienced similar excess attributable risk to whites. Asian/Pacific Islander residents had substantially lower excess attributable risk compared to white. We estimate the distinct contributions of exposure and other factors to these results, and find that factors other than exposure can be critical determinants of pollution-related disparities.

Suggested Citation

  • Hackbarth, Andrew D. & Romley, John A. & Goldman, Dana P., 2011. "Racial and ethnic disparities in hospital care resulting from air pollution in excess of federal standards," Social Science & Medicine, Elsevier, vol. 73(8), pages 1163-1168.
    • Handle: RePEc:eee:socmed:v:73:y:2011:i:8:p:1163-1168
    DOI: 10.1016/j.socscimed.2011.08.008
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    References listed on IDEAS

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    Cited by:

    1. Collins, Timothy W. & Grineski, Sara E. & Morales, Danielle X., 2017. "Environmental injustice and sexual minority health disparities: A national study of inequitable health risks from air pollution among same-sex partners," Social Science & Medicine, Elsevier, vol. 191(C), pages 38-47.
    2. McDonald, Yolanda J. & Grineski, Sara E. & Collins, Timothy W. & Kim, Young-An, 2015. "A scalable climate health justice assessment model," Social Science & Medicine, Elsevier, vol. 133(C), pages 242-252.

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