Pervasive structural racism in environmental epidemiology
Abstract Background Epistemological biases in environmental epidemiology prevent the full understanding of how racism’s societal impacts directly influence health outcomes. With the ability to focus on “place” and the totality of environmental exposures, environmental epidemiologists have an importa...
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oai:doaj.org-article:98e3a4e1bf7c44ada9a4921ec41c1a0f2021-11-21T12:33:07ZPervasive structural racism in environmental epidemiology10.1186/s12940-021-00801-31476-069Xhttps://doaj.org/article/98e3a4e1bf7c44ada9a4921ec41c1a0f2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12940-021-00801-3https://doaj.org/toc/1476-069XAbstract Background Epistemological biases in environmental epidemiology prevent the full understanding of how racism’s societal impacts directly influence health outcomes. With the ability to focus on “place” and the totality of environmental exposures, environmental epidemiologists have an important opportunity to advance the field by proactively investigating the structural racist forces that drive disparities in health. Objective This commentary illustrates how environmental epidemiology has ignored racism for too long. Some examples from environmental health and male infertility are used to illustrate how failing to address racism neglects the health of entire populations. Discussion While research on environmental justice has attended to the structural sources of environmental racism, this work has not been fully integrated into the mainstream of environmental epidemiology. Epidemiology’s dominant paradigm that reduces race to a mere data point avoids the social dimensions of health and thus fails to improve population health for all. Failing to include populations who are Black, Indigenous, and people of color (BIPOC) in health research means researchers actually know very little about the effect of environmental contaminants on a range of population health outcomes. This commentary offers different practical solutions, such as naming racism in research, including BIPOC in leadership positions, mandating requirements for discussing “race”, conducting far more holistic analyses, increasing community participation in research, and improving racism training, to address the myriad of ways in which structural racism permeates environmental epidemiology questions, methods, results and impacts.Melissa J. PerrySuzanne ArringtonMarlaina S. FreisthlerIfeoma N. IbeNathan L. McCrayLaura M. NeumannPatrick TajanlangitBrenda M. Trejo RosasBMCarticleEnvironmental healthEnvironmental epidemiologyEnvironmental justiceRacismStructural racismMale reproductive healthIndustrial medicine. Industrial hygieneRC963-969Public aspects of medicineRA1-1270ENEnvironmental Health, Vol 20, Iss 1, Pp 1-13 (2021) |
institution |
DOAJ |
collection |
DOAJ |
language |
EN |
topic |
Environmental health Environmental epidemiology Environmental justice Racism Structural racism Male reproductive health Industrial medicine. Industrial hygiene RC963-969 Public aspects of medicine RA1-1270 |
spellingShingle |
Environmental health Environmental epidemiology Environmental justice Racism Structural racism Male reproductive health Industrial medicine. Industrial hygiene RC963-969 Public aspects of medicine RA1-1270 Melissa J. Perry Suzanne Arrington Marlaina S. Freisthler Ifeoma N. Ibe Nathan L. McCray Laura M. Neumann Patrick Tajanlangit Brenda M. Trejo Rosas Pervasive structural racism in environmental epidemiology |
description |
Abstract Background Epistemological biases in environmental epidemiology prevent the full understanding of how racism’s societal impacts directly influence health outcomes. With the ability to focus on “place” and the totality of environmental exposures, environmental epidemiologists have an important opportunity to advance the field by proactively investigating the structural racist forces that drive disparities in health. Objective This commentary illustrates how environmental epidemiology has ignored racism for too long. Some examples from environmental health and male infertility are used to illustrate how failing to address racism neglects the health of entire populations. Discussion While research on environmental justice has attended to the structural sources of environmental racism, this work has not been fully integrated into the mainstream of environmental epidemiology. Epidemiology’s dominant paradigm that reduces race to a mere data point avoids the social dimensions of health and thus fails to improve population health for all. Failing to include populations who are Black, Indigenous, and people of color (BIPOC) in health research means researchers actually know very little about the effect of environmental contaminants on a range of population health outcomes. This commentary offers different practical solutions, such as naming racism in research, including BIPOC in leadership positions, mandating requirements for discussing “race”, conducting far more holistic analyses, increasing community participation in research, and improving racism training, to address the myriad of ways in which structural racism permeates environmental epidemiology questions, methods, results and impacts. |
format |
article |
author |
Melissa J. Perry Suzanne Arrington Marlaina S. Freisthler Ifeoma N. Ibe Nathan L. McCray Laura M. Neumann Patrick Tajanlangit Brenda M. Trejo Rosas |
author_facet |
Melissa J. Perry Suzanne Arrington Marlaina S. Freisthler Ifeoma N. Ibe Nathan L. McCray Laura M. Neumann Patrick Tajanlangit Brenda M. Trejo Rosas |
author_sort |
Melissa J. Perry |
title |
Pervasive structural racism in environmental epidemiology |
title_short |
Pervasive structural racism in environmental epidemiology |
title_full |
Pervasive structural racism in environmental epidemiology |
title_fullStr |
Pervasive structural racism in environmental epidemiology |
title_full_unstemmed |
Pervasive structural racism in environmental epidemiology |
title_sort |
pervasive structural racism in environmental epidemiology |
publisher |
BMC |
publishDate |
2021 |
url |
https://doaj.org/article/98e3a4e1bf7c44ada9a4921ec41c1a0f |
work_keys_str_mv |
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