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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Main Author: Nancy Krieger
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-04-01
Series:Frontiers in Public Health
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fpubh.2021.655447/full
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spelling doaj-a6cf0dc90b564bff8ef880bf026e351b2021-04-15T06:16:19ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-04-01910.3389/fpubh.2021.655447655447Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural SolutionsNancy KriegerAnalyzing 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.https://www.frontiersin.org/articles/10.3389/fpubh.2021.655447/fullanti-racismdata governanceecosocial theory of disease distributionhealth equitypopulation healthpolitics of public health data
collection DOAJ
language English
format Article
sources DOAJ
author Nancy Krieger
spellingShingle Nancy Krieger
Structural Racism, Health Inequities, and the Two-Edged Sword of Data: Structural Problems Require Structural Solutions
Frontiers in Public Health
anti-racism
data governance
ecosocial theory of disease distribution
health equity
population health
politics of public health data
author_facet Nancy Krieger
author_sort Nancy Krieger
title 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_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_sort structural racism, health inequities, and the two-edged sword of data: structural problems require structural solutions
publisher Frontiers Media S.A.
series Frontiers in Public Health
issn 2296-2565
publishDate 2021-04-01
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.
topic anti-racism
data governance
ecosocial theory of disease distribution
health equity
population health
politics of public health data
url https://www.frontiersin.org/articles/10.3389/fpubh.2021.655447/full
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