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Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions

Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed—i. e., racialized categories—to document racialized health inequities. This essay, w...

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Detalles Bibliográficos
Autor principal: Krieger, Nancy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082016/
https://www.ncbi.nlm.nih.gov/pubmed/33937178
http://dx.doi.org/10.3389/fpubh.2021.655447
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author Krieger, Nancy
author_facet Krieger, Nancy
author_sort Krieger, Nancy
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description Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed—i. e., racialized categories—to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use (Edge #1) and problematic use (Edge #2) of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. Proposal/part 1 is to implement enforceable requirements that all US health data sets and research projects supported by government funds must explicitly explain and justify their conceptualization of racialized groups and the metrics used to categorize them. Proposal/part 2 is that any individual-level health data by membership in racialized groups must also be analyzed in relation to relevant data about racialized societal inequities. A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity.
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spelling pubmed-80820162021-04-30 Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions Krieger, Nancy Front Public Health Public Health Analyzing the myriad ways in which structural racism systemically generates health inequities requires engaging with the profound challenges of conceptualizing, operationalizing, and analyzing the very data deployed—i. e., racialized categories—to document racialized health inequities. This essay, written in the aftermath of the January 6, 2021 vigilante anti-democratic white supremacist assault on the US Capitol, calls attention to the two-edged sword of data at play, reflecting long histories of support for and opposition to white supremacy and scientific racism. As illustrated by both past and present examples, including COVID-19, at issue are both the non-use (Edge #1) and problematic use (Edge #2) of data on racialized groups. Recognizing that structural problems require structural solutions, in this essay I propose a new two-part institutional mandate regarding the reporting and analysis of publicly-funded work involving racialized groups and health data and documentation as to why the proposed mandates are feasible. Proposal/part 1 is to implement enforceable requirements that all US health data sets and research projects supported by government funds must explicitly explain and justify their conceptualization of racialized groups and the metrics used to categorize them. Proposal/part 2 is that any individual-level health data by membership in racialized groups must also be analyzed in relation to relevant data about racialized societal inequities. A new opportunity arises as US government agencies re-engage with their work, out of the shadow of white grievance politics cast by the Trump Administration, to move forward with this structural proposal to aid the work for health equity. Frontiers Media S.A. 2021-04-15 /pmc/articles/PMC8082016/ /pubmed/33937178 http://dx.doi.org/10.3389/fpubh.2021.655447 Text en Copyright © 2021 Krieger. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Krieger, Nancy
Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
title Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
title_full Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
title_fullStr Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
title_full_unstemmed Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
title_short Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
title_sort structural racism, health inequities, and the two-edged sword of data: structural problems require structural solutions
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082016/
https://www.ncbi.nlm.nih.gov/pubmed/33937178
http://dx.doi.org/10.3389/fpubh.2021.655447
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